model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWaKpahj4a79",
        "outputId": "00e42173-9413-4f70-9d18-b9473ba6f055"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrKnp0kWmqsZ"
      },
      "source": [
        "# %cd /content/drive/MyDrive/Colab Notebooks/zip_files/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AR3G4mQmaj1O",
        "outputId": "6b141b9c-5774-4882-964e-41b18a065c8c"
      },
      "source": [
        "%cd /content/drive/MyDrive/Colab Notebooks/ML_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/ML_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tj9tV6tvl3fe"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ce5RIK4qd4ek"
      },
      "source": [
        "!unzip '*.zip' -d /content/drive/MyDrive/\"Colab Notebooks\"/ML_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKBFSQiRfaBu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "786bf72b-f031-45e4-8776-4d812411e799"
      },
      "source": [
        "while True:\n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-b7133701d76c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zjg3r8sctn6f"
      },
      "source": [
        "!sudo unzip /content/drive/MyDrive/\"Colab Notebooks\"/img.zip -d /content/drive/MyDrive/\"Colab Notebooks\"/img_final"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqXJEP9oJuCJ"
      },
      "source": [
        "while True:\n",
        "    pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eey0HJl86YC_"
      },
      "source": [
        "# /content/drive/MyDrive/Colab Notebooks/img_final/img\n",
        "import glob\n",
        "file = glob.glob('/content/drive/MyDrive/Colab Notebooks/img_final/img/*')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hKrdsYAd7LY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f088c3b-bed7-4e87-9330-73968a140c79"
      },
      "source": [
        "len(file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1580"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGHbDi-72VkT"
      },
      "source": [
        "import torch\n",
        "from torchvision import transforms\n",
        "from PIL import Image\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crhbybmR2-6f"
      },
      "source": [
        "def fileList(path_before : str)->list : \n",
        "    file_list = os.listdir(path_before) \n",
        "    category=[]\n",
        "    for file in file_list: \n",
        "        temp_list = file.split(\"_\")  \n",
        "        category.append(temp_list[0]) \n",
        "    print(len(category))\n",
        "    temp_set = set(category) \n",
        "    result = list(temp_set)\n",
        "    return result \n",
        "\n",
        "# C:\\Users\\LG그램\\Desktop\\img1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKCjxuld3KBr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b72ea53f-5715-4fea-aab7-aa0e246068c0"
      },
      "source": [
        "filelist = fileList('/content/drive/MyDrive/Colab Notebooks/ML_data/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DNGnnv-u_fX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e37db0d-c71b-467c-b5bc-5c5215b24595"
      },
      "source": [
        "len(filelist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uca62tqIjcG5",
        "outputId": "f496851e-7393-4fbf-a09c-a56b72d1bfbb"
      },
      "source": [
        "import glob\n",
        "len(glob.glob('/content/drive/MyDrive/Colab Notebooks/ML_data/*'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67597"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Cc-XqUPoIIV"
      },
      "source": [
        "filelist.remove('.7z')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inC3lEjUkmg2"
      },
      "source": [
        "filelist = ['Jersey',\n",
        " 'Trunks',\n",
        " 'Button-Down',\n",
        " 'Coat',\n",
        " 'Cutoffs',\n",
        " 'Sweatshorts',\n",
        " 'Jeggings',\n",
        " 'Tee',\n",
        " 'Cardigan',\n",
        " 'Parka',\n",
        " 'Sweatpants',\n",
        " 'Kimono',\n",
        " 'Top',\n",
        " 'Sweater',\n",
        " 'Turtleneck',\n",
        " 'Joggers',\n",
        " 'Jeans',\n",
        " 'Chinos',\n",
        " 'Flannel',\n",
        " 'Poncho',\n",
        " 'Blazer',\n",
        " 'Tank',\n",
        " 'Romper',\n",
        " 'Jacket',\n",
        " 'Blouse',\n",
        " 'Shorts',\n",
        " 'Leggings',\n",
        " 'Culottes',\n",
        " 'Hoodie',\n",
        " 'Jumpsuit',\n",
        " 'Skirt',\n",
        " 'Henley']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_FvukYLmcJP",
        "outputId": "400a1c32-ff20-4831-c956-0fa767dc90fc"
      },
      "source": [
        "filelist"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Cardigan',\n",
              " 'Jacket',\n",
              " 'Anorak',\n",
              " 'Culottes',\n",
              " 'Cutoffs',\n",
              " 'Halter',\n",
              " 'Trunks',\n",
              " 'Blouse',\n",
              " 'Button-Down',\n",
              " 'Bomber',\n",
              " 'Coverup',\n",
              " 'Capris',\n",
              " 'Caftan',\n",
              " 'Top',\n",
              " 'Flannel',\n",
              " 'Hoodie',\n",
              " 'Chinos',\n",
              " 'Gauchos',\n",
              " 'Turtleneck',\n",
              " 'Coat',\n",
              " 'Blazer',\n",
              " 'Henley']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyYloNJurgGY"
      },
      "source": [
        "def makeFolder(path_after : str, file_list : list):  \n",
        "    for file in file_list: \n",
        "        try: \n",
        "            os.makedirs(path_after+\"/\"+file) \n",
        "        except: \n",
        "            pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQS5qjADria4"
      },
      "source": [
        "makeFolder('/content/drive/MyDrive/Colab Notebooks/ML_data', filelist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVNXoQqiqUJz",
        "outputId": "999966d1-8ae5-48b6-e5e3-6e241e12ff3e"
      },
      "source": [
        "count=0\n",
        "for file in filelist:\n",
        "    ar = glob.glob('/content/drive/MyDrive/Colab Notebooks/img_final/img/*_' + file + '/*')\n",
        "    for num, a in enumerate(ar):s\n",
        "        file_modified = os.path.join('/content/drive/MyDrive/Colab Notebooks/ML_data/'+file, file + '_' + str(num)+'.jpg')\n",
        "        shutil.copy(a, file_modified)\n",
        "    count+=1\n",
        "    print(count)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_oVlXsisST2",
        "outputId": "3064bc8c-e56f-4144-bbaa-e900e37f561b"
      },
      "source": [
        "import glob\n",
        "# /content/drive/MyDrive/Colab Notebooks/ML_data\n",
        "len(glob.glob('/content/drive/MyDrive/Colab Notebooks/ML_data/*'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "217071"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGrSri7KsMYI",
        "outputId": "f4faab17-09c1-4b3e-83a0-9685750594e1"
      },
      "source": [
        "len(filelist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dP_mgVTu3LLj"
      },
      "source": [
        "file_dict = {string : i for i, string in enumerate(filelist)}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlNM51j_Ga7_"
      },
      "source": [
        "file_dict = {'Kaftan': 0,\n",
        " 'Capris': 1,\n",
        " 'Leggings': 2,\n",
        " 'Tee': 3,\n",
        " 'Flannel': 4,\n",
        " 'Sweatpants': 5,\n",
        " 'Sweatshorts': 6,\n",
        " 'Jumpsuit': 7,\n",
        " 'Kimono': 8,\n",
        " 'Peacoat': 9,\n",
        " 'Blazer': 10,\n",
        " 'Jersey': 11,\n",
        " 'Button-Down': 12,\n",
        " 'Jodhpurs': 13,\n",
        " 'Robe': 14,\n",
        " 'Chinos': 15,\n",
        " 'Onesie': 16,\n",
        " 'Gauchos': 17,\n",
        " 'Shorts': 18,\n",
        " 'Parka': 19,\n",
        " 'Hoodie': 20,\n",
        " 'Joggers': 21,\n",
        " 'Caftan': 22,\n",
        " 'Top': 23,\n",
        " 'Jeans': 24,\n",
        " 'Halter': 25,\n",
        "\n",
        " 'Sweater': 26,\n",
        " 'Romper': 27,\n",
        " 'Henley': 28,\n",
        " 'Jeggings': 29,\n",
        " 'Culottes': 30,\n",
        " 'Tank': 31,\n",
        " 'Blouse': 32,\n",
        " 'Coat': 33,\n",
        " 'Coverup': 34,\n",
        " 'Trunks': 35,\n",
        " 'Cardigan': 36,\n",
        " 'Jacket': 37,\n",
        " 'Bomber': 38,\n",
        " 'Poncho': 39,\n",
        " 'Anorak': 40,\n",
        " 'Turtleneck': 41,\n",
        " 'Cutoffs': 42,\n",
        " 'Sarong': 43,\n",
        " 'Skirt': 44}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6OAhnnemdCF"
      },
      "source": [
        "p = transforms.Compose([\n",
        "    transforms.Resize([227,227]),\n",
        "#     transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ2ClwOaRw24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3fe2c7a-bbdd-4f77-a4d3-6d83f8482d45"
      },
      "source": [
        "len(glob.glob('/content/drive/MyDrive/Colab Notebooks/ML_data/*'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67596"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02CUEk27nfAM"
      },
      "source": [
        "class ClothDataset(Dataset):\n",
        "    def __init__(self,file_dict, transform=None):\n",
        "#         self.all_data = sorted(glob.glob(os.path.join(data_dir, mode, '*', '*')))\n",
        "        self.file_dict = file_dict\n",
        "        self.all_data = glob.glob('/content/drive/MyDrive/Colab Notebooks/ML_data/*')\n",
        "        self.transform = transform\n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "\n",
        "        data_path = self.all_data[index]    \n",
        "        img = pil_loader(data_path)         \n",
        "        if self.transform is not None:\n",
        "            img = self.transform(img)\n",
        "        \n",
        "        label_name = os.path.basename(data_path).split('_')[0]\n",
        "        label = self.file_dict[label_name]\n",
        "        \n",
        "        return img, label\n",
        "    \n",
        "    def __len__(self):\n",
        "        # length = 289219\n",
        "        length = len(self.all_data)\n",
        "        return length"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zoK4nvXoLOU"
      },
      "source": [
        "def pil_loader(path):\n",
        "    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n",
        "    with open(path, 'rb') as f:\n",
        "        img = Image.open(f)\n",
        "        return img.convert('RGB')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxZzoAqDnjST"
      },
      "source": [
        "train_data = ClothDataset(file_dict, transform=p)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRlfwiXznqsT"
      },
      "source": [
        "train_loader = DataLoader(train_data, batch_size=128, shuffle=True, drop_last=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ur10Ukk_tFjX"
      },
      "source": [
        "import torchvision.models as models\n",
        "\n",
        "model = models.alexnet(pretrained=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qdsvjx7JtJCL",
        "outputId": "431fb31d-5e3b-46e5-b99b-8bbe284a37c0"
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Dropout(p=0.5, inplace=False)\n",
              "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXrcXK03tJ6M"
      },
      "source": [
        "model.classifier[6] = nn.Linear(in_features=4096, out_features=45, bias=True)\n",
        "# model.classifier = nn.Sequential(*list(model.classifier) + [nn.Linear(in_features=1000, out_features=46, bias=True)])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkdPzRfCtQLD"
      },
      "source": [
        "for param in model.parameters():\n",
        "#     print(param)\n",
        "    param.requires_grad = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBPUJF5ftR5Z"
      },
      "source": [
        "for name, param in model.named_parameters():\n",
        "#     print(name)\n",
        "    if name in ['classifier.6.weight', 'classifier.6.bias']:\n",
        "        param.requires_grad = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Biv-x_i8tUqB",
        "outputId": "d05a40a5-88e5-4f60-9bdd-2736ee2fa0d4"
      },
      "source": [
        "for name, param in model.named_parameters():\n",
        "    if param.requires_grad == True:\n",
        "        print(name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier.6.weight\n",
            "classifier.6.bias\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w21aXo0mtVfm"
      },
      "source": [
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "\n",
        "#Loss\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "#Optimizer(SGD)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbtYExWUtaF5",
        "outputId": "c250723b-75a0-4313-cab9-653cca780569"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0plTRrgQtxFf",
        "outputId": "3e2d9a43-bb22-4365-c271-71cef9ba7523"
      },
      "source": [
        "model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AlexNet(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU(inplace=True)\n",
              "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (9): ReLU(inplace=True)\n",
              "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
              "  (classifier): Sequential(\n",
              "    (0): Dropout(p=0.5, inplace=False)\n",
              "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
              "    (2): ReLU(inplace=True)\n",
              "    (3): Dropout(p=0.5, inplace=False)\n",
              "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (5): ReLU(inplace=True)\n",
              "    (6): Linear(in_features=4096, out_features=45, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Rjis0KCTtWlp",
        "outputId": "e831a160-628f-4124-fe6a-257d1a65f42d"
      },
      "source": [
        "loss_list = []\n",
        "for epoch in range(10):  # loop over the dataset multiple times\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(train_loader, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        output = model(inputs)\n",
        "        loss = criterion(output, labels)\n",
        "#         loss = F.cross_entropy(output, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        print(loss.item())\n",
        "#         break\n",
        "        # print statistics\n",
        "#         running_loss += loss.item()\n",
        "#         loss_list.append(running_loss)\n",
        "#         print(loss.item())\n",
        "#         if i % 2 == 0:\n",
        "#         # print every 2000 mini-batches\n",
        "#             print(loss.item())\n",
        "#             print('[%d, %5d] loss: %.3f' %\n",
        "#                   (epoch + 1, i + 1, running_loss / 2))\n",
        "#             running_loss = 0.0\n",
        "    print(f'------------ {epoch} finished!!! ------------')\n",
        "print('Finished Training of AlexNet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.2359580993652344\n",
            "3.3376243114471436\n",
            "3.267920970916748\n",
            "3.278489589691162\n",
            "3.2528231143951416\n",
            "3.263564109802246\n",
            "3.2709145545959473\n",
            "3.2629504203796387\n",
            "3.2843122482299805\n",
            "3.2236995697021484\n",
            "3.278369665145874\n",
            "3.2517802715301514\n",
            "3.282128095626831\n",
            "3.24078631401062\n",
            "3.244344711303711\n",
            "3.2323431968688965\n",
            "3.280003070831299\n",
            "3.300143241882324\n",
            "3.254660129547119\n",
            "3.272507667541504\n",
            "3.2583322525024414\n",
            "3.1898674964904785\n",
            "3.260831594467163\n",
            "3.2384965419769287\n",
            "3.2636923789978027\n",
            "3.2841036319732666\n",
            "3.268265724182129\n",
            "3.2411975860595703\n",
            "3.2969861030578613\n",
            "3.2951276302337646\n",
            "3.2827296257019043\n",
            "3.2254345417022705\n",
            "3.221998929977417\n",
            "3.2760426998138428\n",
            "3.2875969409942627\n",
            "3.282121181488037\n",
            "3.24212384223938\n",
            "3.2991654872894287\n",
            "3.3015544414520264\n",
            "3.299743413925171\n",
            "3.278094530105591\n",
            "3.2403531074523926\n",
            "3.251309394836426\n",
            "3.29449200630188\n",
            "3.284925937652588\n",
            "3.284383535385132\n",
            "3.2260475158691406\n",
            "3.274590492248535\n",
            "3.2910358905792236\n",
            "3.3148856163024902\n",
            "3.2470996379852295\n",
            "3.2421014308929443\n",
            "3.2431418895721436\n",
            "3.3099234104156494\n",
            "3.2277698516845703\n",
            "3.2687129974365234\n",
            "3.2900593280792236\n",
            "3.2688026428222656\n",
            "3.2941250801086426\n",
            "3.2439346313476562\n",
            "3.244994878768921\n",
            "3.294705390930176\n",
            "3.2820851802825928\n",
            "3.309250593185425\n",
            "3.2266945838928223\n",
            "3.2234280109405518\n",
            "3.2049381732940674\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-bf871a52c4b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# loop over the dataset multiple times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mrunning_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0;31m# get the inputs; data is a list of [inputs, labels]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    562\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    563\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-37-d1430c7ba1ec>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mdata_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-38-7a83f2fd0b7f>\u001b[0m in \u001b[0;36mpil_loader\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2850\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2852\u001b[0;31m     \u001b[0mprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2853\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2854\u001b[0m     \u001b[0mpreinit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOE4SCqCt6dR"
      },
      "source": [
        "a = '''\n",
        "3.2359580993652344\n",
        "3.3376243114471436\n",
        "3.267920970916748\n",
        "3.278489589691162\n",
        "3.2528231143951416\n",
        "3.263564109802246\n",
        "3.2709145545959473\n",
        "3.2629504203796387\n",
        "3.2843122482299805\n",
        "3.2236995697021484\n",
        "3.278369665145874\n",
        "3.2517802715301514\n",
        "3.282128095626831\n",
        "3.24078631401062\n",
        "3.244344711303711\n",
        "3.2323431968688965\n",
        "3.280003070831299\n",
        "3.300143241882324\n",
        "3.254660129547119\n",
        "3.272507667541504\n",
        "3.2583322525024414\n",
        "3.1898674964904785\n",
        "3.260831594467163\n",
        "3.2384965419769287\n",
        "3.2636923789978027\n",
        "3.2841036319732666\n",
        "3.268265724182129\n",
        "3.2411975860595703\n",
        "3.2969861030578613\n",
        "3.2951276302337646\n",
        "3.2827296257019043\n",
        "3.2254345417022705\n",
        "3.221998929977417\n",
        "3.2760426998138428\n",
        "3.2875969409942627\n",
        "3.282121181488037\n",
        "3.24212384223938\n",
        "3.2991654872894287\n",
        "3.3015544414520264\n",
        "3.299743413925171\n",
        "3.278094530105591\n",
        "3.2403531074523926\n",
        "3.251309394836426\n",
        "3.29449200630188\n",
        "3.284925937652588\n",
        "3.284383535385132\n",
        "3.2260475158691406\n",
        "3.274590492248535\n",
        "3.2910358905792236\n",
        "3.3148856163024902\n",
        "3.2470996379852295\n",
        "3.2421014308929443\n",
        "3.2431418895721436\n",
        "3.3099234104156494\n",
        "3.2277698516845703\n",
        "3.2687129974365234\n",
        "3.2900593280792236\n",
        "3.2688026428222656\n",
        "3.2941250801086426\n",
        "3.2439346313476562\n",
        "3.244994878768921\n",
        "3.294705390930176\n",
        "3.2820851802825928\n",
        "3.309250593185425\n",
        "3.2266945838928223\n",
        "3.2234280109405518\n",
        "3.2049381732940674\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quWqoBM1swn8"
      },
      "source": [
        "a = a.split('\\n')[1:-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paW055yVsw9s"
      },
      "source": [
        "a = [float(i) for i in a]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghiY80avs991",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "234b99a9-47ea-4515-acca-b28369cfb8ae"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.2359580993652344,\n",
              " 3.3376243114471436,\n",
              " 3.267920970916748,\n",
              " 3.278489589691162,\n",
              " 3.2528231143951416,\n",
              " 3.263564109802246,\n",
              " 3.2709145545959473,\n",
              " 3.2629504203796387,\n",
              " 3.2843122482299805,\n",
              " 3.2236995697021484,\n",
              " 3.278369665145874,\n",
              " 3.2517802715301514,\n",
              " 3.282128095626831,\n",
              " 3.24078631401062,\n",
              " 3.244344711303711,\n",
              " 3.2323431968688965,\n",
              " 3.280003070831299,\n",
              " 3.300143241882324,\n",
              " 3.254660129547119,\n",
              " 3.272507667541504,\n",
              " 3.2583322525024414,\n",
              " 3.1898674964904785,\n",
              " 3.260831594467163,\n",
              " 3.2384965419769287,\n",
              " 3.2636923789978027,\n",
              " 3.2841036319732666,\n",
              " 3.268265724182129,\n",
              " 3.2411975860595703,\n",
              " 3.2969861030578613,\n",
              " 3.2951276302337646,\n",
              " 3.2827296257019043,\n",
              " 3.2254345417022705,\n",
              " 3.221998929977417,\n",
              " 3.2760426998138428,\n",
              " 3.2875969409942627,\n",
              " 3.282121181488037,\n",
              " 3.24212384223938,\n",
              " 3.2991654872894287,\n",
              " 3.3015544414520264,\n",
              " 3.299743413925171,\n",
              " 3.278094530105591,\n",
              " 3.2403531074523926,\n",
              " 3.251309394836426,\n",
              " 3.29449200630188,\n",
              " 3.284925937652588,\n",
              " 3.284383535385132,\n",
              " 3.2260475158691406,\n",
              " 3.274590492248535,\n",
              " 3.2910358905792236,\n",
              " 3.3148856163024902,\n",
              " 3.2470996379852295,\n",
              " 3.2421014308929443,\n",
              " 3.2431418895721436,\n",
              " 3.3099234104156494,\n",
              " 3.2277698516845703,\n",
              " 3.2687129974365234,\n",
              " 3.2900593280792236,\n",
              " 3.2688026428222656,\n",
              " 3.2941250801086426,\n",
              " 3.2439346313476562,\n",
              " 3.244994878768921,\n",
              " 3.294705390930176,\n",
              " 3.2820851802825928,\n",
              " 3.309250593185425,\n",
              " 3.2266945838928223,\n",
              " 3.2234280109405518,\n",
              " 3.2049381732940674]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "gjWqGX4TtCA8",
        "outputId": "a8be07d0-c685-41c9-d893-4f19ffc67774"
      },
      "source": [
        "plt.plot(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9d2c070e50>]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDOjwnHXtKND"
      },
      "source": [
        "model = torch.hub.load('googlenet', pretrained=True).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpkoTgjHyGYd"
      },
      "source": [
        "import torchvision.models as models\n",
        "\n",
        "# model = models.alexnet(pretrained=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1aRP3hgyLd8"
      },
      "source": [
        "model = models.googlenet(pretrained=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vL6VcefoyWBk"
      },
      "source": [
        "model.fc = nn.Linear(in_features=1024, out_features=22, bias=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLALyzNm1WbV"
      },
      "source": [
        "for param in model.parameters():\n",
        "#     print(param)\n",
        "    param.requires_grad = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hIUAGHu1ZAc"
      },
      "source": [
        "for name, param in model.named_parameters():\n",
        "#     print(name)\n",
        "    if name in ['fc.weight','fc.bias']:\n",
        "        param.requires_grad = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2hCuZrl1i-0",
        "outputId": "c8012206-268f-40e2-89f3-23139684b9ad"
      },
      "source": [
        "for name, param in model.named_parameters():\n",
        "    if param.requires_grad == True:\n",
        "        print(name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fc.weight\n",
            "fc.bias\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyQrwxr0zpvw",
        "outputId": "f378e3dd-bc1c-47ba-fe34-89cfe6abd782"
      },
      "source": [
        "model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GoogLeNet(\n",
              "  (conv1): BasicConv2d(\n",
              "    (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (conv2): BasicConv2d(\n",
              "    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (conv3): BasicConv2d(\n",
              "    (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception3a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception3b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception4a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4c): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4d): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception4e): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
              "  (inception5a): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (inception5b): Inception(\n",
              "    (branch1): BasicConv2d(\n",
              "      (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (branch2): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch3): Sequential(\n",
              "      (0): BasicConv2d(\n",
              "        (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (branch4): Sequential(\n",
              "      (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
              "      (1): BasicConv2d(\n",
              "        (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (aux1): None\n",
              "  (aux2): None\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              "  (fc): Linear(in_features=1024, out_features=22, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnCU-Z4-zqN7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}