NetVLAD_manual.ipynb

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  "metadata": {
    "colab": {
      "name": "NetVLAD_manual.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/socome/NetVLAD-Example-on-Colab/blob/master/NetVLAD_manual.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Re-V0EBYOt7s",
        "colab_type": "text"
      },
      "source": [
        "[***출처: 심현주님의 블로그를 참고했습니다.***](https://medium.com/hyunjulie/%EC%BA%90%EA%B8%80%EA%B3%BC-%EA%B5%AC%EA%B8%80-colab-%EC%97%B0%EA%B2%B0%ED%95%B4%EC%A3%BC%EA%B8%B0-6a274f6de81d)\n",
        "\n",
        "\n",
        "**1. 먼저 본인의 캐글 계정에서 API Token 을 다운받습니다.**\n",
        "\n",
        "![업로드방법](https://cdn-images-1.medium.com/max/800/1*yvocb2Sbyf_ZbBxUcJM7rA.png)\n",
        "\n",
        "**오른쪽 상단에서 My Account 에 들어갑니다**\n",
        "\n",
        "![API받기](https://cdn-images-1.medium.com/max/800/1*JgVnYqQh6mEEoKCLayFz9g.png)\n",
        "\n",
        "**Create New API Token 을 누르면 kaggle.json 파일이 다운로드 됩니다. 이 파일은 찾기 쉬운곳에 일단 옮겨두고,**\n",
        "\n",
        "**아래의 셀을 실행합니다. 여기서 '파일선택'을 누르고 kaggle.json을 업로드합니다.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vh8xECtdNpm2",
        "colab_type": "code",
        "outputId": "3e8a4f25-8be0-4d3b-fd2d-addd8cfebb29",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 96
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6e761f3c-b73e-402b-8e34-737acafa5b8e\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-6e761f3c-b73e-402b-8e34-737acafa5b8e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"socome\",\"key\":\"e2625f9da36b80e36314f9a35b062e3b\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9S3u36HNrXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# json 파일 옮겨주기\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "# Permission Warning 이 일어나지 않도록 \n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6MLF36rNcqG",
        "colab_type": "code",
        "outputId": "4ef09b9b-5ae3-4a4b-b926-0b9489eb619c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16397
        }
      },
      "source": [
        "!kaggle datasets download -d socome/netvlad-landmark-dataset-sejong\n",
        "!unzip netvlad-landmark-dataset-sejong.zip\n",
        "!tar -xvzf Sejong.tgz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading netvlad-landmark-dataset-sejong.zip to /content\n",
            "100% 1.53G/1.53G [00:21<00:00, 98.7MB/s]\n",
            "100% 1.53G/1.53G [00:21<00:00, 77.9MB/s]\n",
            "Archive:  netvlad-landmark-dataset-sejong.zip\n",
            "  inflating: Sejong.tgz              \n",
            "Sejong/\n",
            "Sejong/test/\n",
            "Sejong/train/\n",
            "Sejong/test/AI/\n",
            "Sejong/test/Clock tower/\n",
            "Sejong/test/desktop.ini\n",
            "Sejong/test/Front door Child/\n",
            "Sejong/test/Front door Sejong/\n",
            "Sejong/test/Museum/\n",
            "Sejong/test/Stone statue/\n",
            "Sejong/train/AI/\n",
            "Sejong/train/Clock tower/\n",
            "Sejong/train/desktop.ini\n",
            "Sejong/train/Front door Child/\n",
            "Sejong/train/Front door Sejong/\n",
            "Sejong/train/Museum/\n",
            "Sejong/train/Stone statue/\n",
            "Sejong/test/AI/1245_1167_139.jpg\n",
            "Sejong/test/AI/42523.png\n",
            "Sejong/test/AI/clip20190606_1620_45_379.png\n",
            "Sejong/test/AI/desktop.ini\n",
            "Sejong/test/AI/대양 4.png\n",
            "Sejong/test/AI/대양.jpg\n",
            "Sejong/test/AI/대양.PNG\n",
            "Sejong/test/AI/대양2.png\n",
            "Sejong/test/AI/대양22.PNG\n",
            "Sejong/test/AI/대양3.png\n",
            "Sejong/test/AI/대양33.PNG\n",
            "Sejong/test/AI/대양4.PNG\n",
            "Sejong/test/AI/대양5.png\n",
            "Sejong/test/AI/대양6.png\n",
            "Sejong/test/AI/대양67.png\n",
            "Sejong/test/Clock tower/182388_63837_2045.jpg\n",
            "Sejong/test/Clock tower/2.jpg\n",
            "Sejong/test/Clock tower/20151016_103810_edit.jpg\n",
            "Sejong/test/Clock tower/da17bb39ff8ae1a87e2182d53ec2656d.gif\n",
            "Sejong/test/Clock tower/desktop.ini\n",
            "Sejong/test/Clock tower/DSC08927.jpeg\n",
            "Sejong/test/Clock tower/htm_2016122293339878068.jpg\n",
            "Sejong/test/Clock tower/IMAGE0107.jpg\n",
            "Sejong/test/Clock tower/images (2).jpeg\n",
            "Sejong/test/Clock tower/image_readtop_2017_466166_14997999662951835.jpg\n",
            "Sejong/test/Clock tower/IMGP0092.jpeg\n",
            "Sejong/test/Clock tower/IMG_9553.jpeg\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbw-FVyCRHpJ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# **다음과 같이 정상적으로 설치 됐는지 확인합니다.**\n",
        "\n",
        "![확인](https://user-images.githubusercontent.com/44772344/58857792-015ac400-86e1-11e9-9cd3-a4f772cfccfe.PNG)\n",
        "\n",
        "\n",
        "**다음으로는 NetVLAD와 TripletNEt을 섞은 EmbedNet을 통해서 학습을 진행할 예정입니다.**\n",
        "\n",
        "![모델](https://user-images.githubusercontent.com/44772344/58701146-24cdf800-83dd-11e9-924d-4e5e247bfec3.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxSWZJWjP-jB",
        "colab_type": "code",
        "outputId": "5ae2e82b-8d59-450a-b56c-c9767ebc4d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "from torch.autograd import Variable\n",
        "from torchvision.models import resnet18\n",
        "\n",
        "from tensorflow import keras\n",
        "from keras.preprocessing import image\n",
        "\n",
        "import PIL\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets, metrics\n",
        "\n",
        "torch.manual_seed(777)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fa1b9ea66b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcrISVFrRn3u",
        "colab_type": "code",
        "outputId": "00f3fc10-43d4-4cb6-967f-315146a3f86f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "!pip install tensorboardcolab\n",
        "from tensorboardcolab import TensorBoardColab\n",
        "tb = TensorBoardColab()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorboardcolab in /usr/local/lib/python3.6/dist-packages (0.0.22)\n",
            "Wait for 8 seconds...\n",
            "TensorBoard link:\n",
            "https://aa6b77f6.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrDzaVOFSZpU",
        "colab_type": "text"
      },
      "source": [
        "## **NetVLAD와 TripletLoss를 결합하는 코드는 다음 깃허브에서 참고했습니다.**\n",
        "\n",
        "# [바로가기](https://github.com/lyakaap/NetVLAD-pytorch)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wP1v725Ro5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class NetVLAD(nn.Module):\n",
        "    \"\"\"NetVLAD layer implementation\"\"\"\n",
        "\n",
        "    def __init__(self, num_clusters=6, dim=128, alpha=100.0,\n",
        "                 normalize_input=True):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            num_clusters : int\n",
        "                The number of clusters\n",
        "            dim : int\n",
        "                Dimension of descriptors\n",
        "            alpha : float\n",
        "                Parameter of initialization. Larger value is harder assignment.\n",
        "            normalize_input : bool\n",
        "                If true, descriptor-wise L2 normalization is applied to input.\n",
        "        \"\"\"\n",
        "        super(NetVLAD, self).__init__()\n",
        "        self.num_clusters = num_clusters\n",
        "        self.dim = dim\n",
        "        self.alpha = alpha\n",
        "        self.normalize_input = normalize_input\n",
        "        self.conv = nn.Conv2d(dim, num_clusters, kernel_size=(1, 1), bias=True)\n",
        "        self.centroids = nn.Parameter(torch.rand(num_clusters, dim))\n",
        "        self._init_params()\n",
        "\n",
        "    def _init_params(self):\n",
        "        self.conv.weight = nn.Parameter(\n",
        "            (2.0 * self.alpha * self.centroids).unsqueeze(-1).unsqueeze(-1)\n",
        "        )\n",
        "        self.conv.bias = nn.Parameter(\n",
        "            - self.alpha * self.centroids.norm(dim=1)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        N, C = x.shape[:2]\n",
        "\n",
        "        if self.normalize_input:\n",
        "            x = F.normalize(x, p=2, dim=1)  # across descriptor dim\n",
        "\n",
        "        # soft-assignment\n",
        "        soft_assign = self.conv(x).view(N, self.num_clusters, -1)\n",
        "        soft_assign = F.softmax(soft_assign, dim=1)\n",
        "\n",
        "        x_flatten = x.view(N, C, -1)\n",
        "        \n",
        "        # calculate residuals to each clusters\n",
        "        residual = x_flatten.expand(self.num_clusters, -1, -1, -1).permute(1, 0, 2, 3) - \\\n",
        "            self.centroids.expand(x_flatten.size(-1), -1, -1).permute(1, 2, 0).unsqueeze(0)\n",
        "        residual *= soft_assign.unsqueeze(2)\n",
        "        vlad = residual.sum(dim=-1)\n",
        "\n",
        "        vlad = F.normalize(vlad, p=2, dim=2)  # intra-normalization\n",
        "        vlad = vlad.view(x.size(0), -1)  # flatten\n",
        "        vlad = F.normalize(vlad, p=2, dim=1)  # L2 normalize\n",
        "\n",
        "        return vlad"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0-nmvniRqmh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class EmbedNet(nn.Module):\n",
        "    def __init__(self, base_model, net_vlad):\n",
        "        super(EmbedNet, self).__init__()\n",
        "        self.base_model = base_model\n",
        "        self.net_vlad = net_vlad\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.base_model(x)\n",
        "        embedded_x = self.net_vlad(x)\n",
        "        return embedded_x\n",
        "      \n",
        "class TripletNet(nn.Module):\n",
        "    def __init__(self, embed_net):\n",
        "        super(TripletNet, self).__init__()\n",
        "        self.embed_net = embed_net\n",
        "\n",
        "    def forward(self, a, p, n):\n",
        "        embedded_a = self.embed_net(a)\n",
        "        embedded_p = self.embed_net(p)\n",
        "        embedded_n = self.embed_net(n)\n",
        "        return embedded_a, embedded_p, embedded_n\n",
        "\n",
        "    def feature_extract(self, x):\n",
        "        return self.embed_net(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXdrzb1YRr_o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class HardTripletLoss(nn.Module):\n",
        "    \"\"\"Hard/Hardest Triplet Loss\n",
        "    (pytorch implementation of https://omoindrot.github.io/triplet-loss)\n",
        "    For each anchor, we get the hardest positive and hardest negative to form a triplet.\n",
        "    \"\"\"\n",
        "    def __init__(self, margin=0.1, hardest=False, squared=False):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            margin: margin for triplet loss\n",
        "            hardest: If true, loss is considered only hardest triplets.\n",
        "            squared: If true, output is the pairwise squared euclidean distance matrix.\n",
        "                If false, output is the pairwise euclidean distance matrix.\n",
        "        \"\"\"\n",
        "        super(HardTripletLoss, self).__init__()\n",
        "        self.margin = margin\n",
        "        self.hardest = hardest\n",
        "        self.squared = squared\n",
        "\n",
        "    def forward(self, embeddings, labels):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            labels: labels of the batch, of size (batch_size,)\n",
        "            embeddings: tensor of shape (batch_size, embed_dim)\n",
        "        Returns:\n",
        "            triplet_loss: scalar tensor containing the triplet loss\n",
        "        \"\"\"\n",
        "        pairwise_dist = _pairwise_distance(embeddings, squared=self.squared)\n",
        "\n",
        "        if self.hardest:\n",
        "            # Get the hardest positive pairs\n",
        "            mask_anchor_positive = _get_anchor_positive_triplet_mask(labels).float()\n",
        "            valid_positive_dist = pairwise_dist * mask_anchor_positive\n",
        "            hardest_positive_dist, _ = torch.max(valid_positive_dist, dim=1, keepdim=True)\n",
        "\n",
        "            # Get the hardest negative pairs\n",
        "            mask_anchor_negative = _get_anchor_negative_triplet_mask(labels).float()\n",
        "            max_anchor_negative_dist, _ = torch.max(pairwise_dist, dim=1, keepdim=True)\n",
        "            anchor_negative_dist = pairwise_dist + max_anchor_negative_dist * (\n",
        "                    1.0 - mask_anchor_negative)\n",
        "            hardest_negative_dist, _ = torch.min(anchor_negative_dist, dim=1, keepdim=True)\n",
        "\n",
        "            # Combine biggest d(a, p) and smallest d(a, n) into final triplet loss\n",
        "            triplet_loss = F.relu(hardest_positive_dist - hardest_negative_dist + 0.1)\n",
        "            triplet_loss = torch.mean(triplet_loss)\n",
        "        else:\n",
        "            anc_pos_dist = pairwise_dist.unsqueeze(dim=2)\n",
        "            anc_neg_dist = pairwise_dist.unsqueeze(dim=1)\n",
        "\n",
        "            # Compute a 3D tensor of size (batch_size, batch_size, batch_size)\n",
        "            # triplet_loss[i, j, k] will contain the triplet loss of anc=i, pos=j, neg=k\n",
        "            # Uses broadcasting where the 1st argument has shape (batch_size, batch_size, 1)\n",
        "            # and the 2nd (batch_size, 1, batch_size)\n",
        "            loss = anc_pos_dist - anc_neg_dist + self.margin\n",
        "\n",
        "            mask = _get_triplet_mask(labels).float()\n",
        "            triplet_loss = loss * mask\n",
        "\n",
        "            # Remove negative losses (i.e. the easy triplets)\n",
        "            triplet_loss = F.relu(triplet_loss)\n",
        "\n",
        "            # Count number of hard triplets (where triplet_loss > 0)\n",
        "            hard_triplets = torch.gt(triplet_loss, 1e-16).float()\n",
        "            num_hard_triplets = torch.sum(hard_triplets)\n",
        "\n",
        "            triplet_loss = torch.sum(triplet_loss) / (num_hard_triplets + 1e-16)\n",
        "\n",
        "        return triplet_loss\n",
        "\n",
        "\n",
        "def _pairwise_distance(x, squared=False, eps=1e-16):\n",
        "    # Compute the 2D matrix of distances between all the embeddings.\n",
        "\n",
        "    cor_mat = torch.matmul(x, x.t())\n",
        "    norm_mat = cor_mat.diag()\n",
        "    distances = norm_mat.unsqueeze(1) - 2 * cor_mat + norm_mat.unsqueeze(0)\n",
        "    distances = F.relu(distances)\n",
        "\n",
        "    if not squared:\n",
        "        mask = torch.eq(distances, 0.0).float()\n",
        "        distances = distances + mask * eps\n",
        "        distances = torch.sqrt(distances)\n",
        "        distances = distances * (1.0 - mask)\n",
        "\n",
        "    return distances\n",
        "\n",
        "\n",
        "def _get_anchor_positive_triplet_mask(labels):\n",
        "    # Return a 2D mask where mask[a, p] is True iff a and p are distinct and have same label.\n",
        "\n",
        "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    indices_not_equal = torch.eye(labels.shape[0]).to(device).byte() ^ 1\n",
        "\n",
        "    # Check if labels[i] == labels[j]\n",
        "    labels_equal = torch.unsqueeze(labels, 0) == torch.unsqueeze(labels, 1)\n",
        "\n",
        "    mask = indices_not_equal * labels_equal\n",
        "\n",
        "    return mask\n",
        "\n",
        "\n",
        "def _get_anchor_negative_triplet_mask(labels):\n",
        "    # Return a 2D mask where mask[a, n] is True iff a and n have distinct labels.\n",
        "\n",
        "    # Check if labels[i] != labels[k]\n",
        "    labels_equal = torch.unsqueeze(labels, 0) == torch.unsqueeze(labels, 1)\n",
        "    mask = labels_equal ^ 1\n",
        "\n",
        "    return mask\n",
        "\n",
        "\n",
        "def _get_triplet_mask(labels):\n",
        "    \"\"\"Return a 3D mask where mask[a, p, n] is True iff the triplet (a, p, n) is valid.\n",
        "    A triplet (i, j, k) is valid if:\n",
        "        - i, j, k are distinct\n",
        "        - labels[i] == labels[j] and labels[i] != labels[k]\n",
        "    \"\"\"\n",
        "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    # Check that i, j and k are distinct\n",
        "    indices_not_same = torch.eye(labels.shape[0]).to(device).byte() ^ 1\n",
        "    i_not_equal_j = torch.unsqueeze(indices_not_same, 2)\n",
        "    i_not_equal_k = torch.unsqueeze(indices_not_same, 1)\n",
        "    j_not_equal_k = torch.unsqueeze(indices_not_same, 0)\n",
        "    distinct_indices = i_not_equal_j * i_not_equal_k * j_not_equal_k\n",
        "\n",
        "    # Check if labels[i] == labels[j] and labels[i] != labels[k]\n",
        "    label_equal = torch.eq(torch.unsqueeze(labels, 0), torch.unsqueeze(labels, 1))\n",
        "    i_equal_j = torch.unsqueeze(label_equal, 2)\n",
        "    i_equal_k = torch.unsqueeze(label_equal, 1)\n",
        "    valid_labels = i_equal_j * (~i_equal_k)\n",
        "\n",
        "    mask = distinct_indices * valid_labels   # Combine the two masks\n",
        "\n",
        "    return mask"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YP-XU6tsRtub",
        "colab_type": "code",
        "outputId": "c3256231-27a0-4814-c6d2-d6e19b19a601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# Discard layers at the end of base network\n",
        "encoder = resnet18(pretrained=True)\n",
        "base_model = nn.Sequential(\n",
        "    encoder.conv1,\n",
        "    encoder.bn1,\n",
        "    encoder.relu,\n",
        "    encoder.maxpool,\n",
        "    encoder.layer1,\n",
        "    encoder.layer2,\n",
        "    encoder.layer3,\n",
        "    encoder.layer4,\n",
        ")\n",
        "dim = list(base_model.parameters())[-1].shape[0]  # last channels (512)\n",
        "\n",
        "# Define model for embedding\n",
        "net_vlad = NetVLAD(num_clusters=6, dim=dim, alpha=1.0)\n",
        "model = EmbedNet(base_model, net_vlad).cuda()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
            "100%|██████████| 46827520/46827520 [00:01<00:00, 26883362.87it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxG3KaF5TA57",
        "colab_type": "text"
      },
      "source": [
        "## 실제 학습을 진행하는 부분입니다. \n",
        "#### Epoch = ??/ batch = 8을 설정하겠습니다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agkolTeaRyKd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define loss\n",
        "criterion = HardTripletLoss(margin=0.1).cuda()\n",
        "epochs = 50\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "556hHcgDTaFf",
        "colab_type": "text"
      },
      "source": [
        " #### 'colab oserror errno 5 input/output error' 를 해결하기 위해 \n",
        " #### 모든 데이터를 로드해 캐시에 저장하고 이후에 학습을 진행하겠습니다. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5n_-UPNULpV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transforms = torchvision.transforms.Compose([\n",
        "    torchvision.transforms.Resize((128,128)),               \n",
        "    torchvision.transforms.ToTensor()\n",
        "    \n",
        "])\n",
        "\n",
        "bef_train_imagenet_data = torchvision.datasets.ImageFolder('./Sejong/train', transform=transforms)\n",
        "bef_train_data_loader = torch.utils.data.DataLoader(bef_train_imagenet_data,\n",
        "                                          batch_size=776,\n",
        "                                          shuffle=False,\n",
        "                                          num_workers=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdpSb7fmUWwd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for bef_train_image,bef_train_label in bef_train_data_loader :\n",
        "  bef_train_image = bef_train_image\n",
        "  bef_train_label = bef_train_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bB7ahj0SLaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_imagenet_data = torchvision.datasets.ImageFolder('./Sejong/train', transform=transforms)\n",
        "train_data_loader = torch.utils.data.DataLoader(train_imagenet_data,\n",
        "                                          batch_size=8,\n",
        "                                          shuffle=True,\n",
        "                                          num_workers=0)\n",
        "test_imagenet_data = torchvision.datasets.ImageFolder('./Sejong/test', transform=transforms)\n",
        "test_data_loader = torch.utils.data.DataLoader(test_imagenet_data,\n",
        "                                          shuffle=False,\n",
        "                                          batch_size=100,\n",
        "                                          num_workers=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcMSP_G6TW6I",
        "colab_type": "code",
        "outputId": "6afc58ec-e5e1-4a90-b21b-fe56c106a900",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87317
        }
      },
      "source": [
        "globaliter = 0\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  for batch_idx, (train_image,train_label) in enumerate(train_data_loader) :\n",
        "    output_train = model(train_image.cuda())\n",
        "    triplet_loss = criterion(output_train, train_label.cuda())\n",
        "    optimizer.zero_grad()\n",
        "    triplet_loss.backward(retain_graph=True)\n",
        "    optimizer.step()\n",
        "    # This is where I'm recording to Tensorboard\n",
        "    tb.save_value('Train Loss', 'train_loss', globaliter, triplet_loss.item())\n",
        "    print('epoch : {}, globaliter : {}, batch_idx  : {}, triplet_loss : {}'.format(epoch,globaliter,batch_idx,triplet_loss.item()))\n",
        "    globaliter += 1\n",
        "  model_save_name = 'model_{:02d}.pt'.format(epoch)\n",
        "  path = F\"./{model_save_name}\" \n",
        "  torch.save(model.state_dict(), path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch : 0, globaliter : 0, batch_idx  : 0, triplet_loss : 0.10053567588329315\n",
            "epoch : 0, globaliter : 1, batch_idx  : 1, triplet_loss : 0.09991779178380966\n",
            "epoch : 0, globaliter : 2, batch_idx  : 2, triplet_loss : 0.09675561636686325\n",
            "epoch : 0, globaliter : 3, batch_idx  : 3, triplet_loss : 0.09777558594942093\n",
            "epoch : 0, globaliter : 4, batch_idx  : 4, triplet_loss : 0.09523854404687881\n",
            "epoch : 0, globaliter : 5, batch_idx  : 5, triplet_loss : 0.09562209248542786\n",
            "epoch : 0, globaliter : 6, batch_idx  : 6, triplet_loss : 0.09767952561378479\n",
            "epoch : 0, globaliter : 7, batch_idx  : 7, triplet_loss : 0.09922640770673752\n",
            "epoch : 0, globaliter : 8, batch_idx  : 8, triplet_loss : 0.097873255610466\n",
            "epoch : 0, globaliter : 9, batch_idx  : 9, triplet_loss : 0.09688548743724823\n",
            "epoch : 0, globaliter : 10, batch_idx  : 10, triplet_loss : 0.09446236491203308\n",
            "epoch : 0, globaliter : 11, batch_idx  : 11, triplet_loss : 0.09784585982561111\n",
            "epoch : 0, globaliter : 12, batch_idx  : 12, triplet_loss : 0.09520281851291656\n",
            "epoch : 0, globaliter : 13, batch_idx  : 13, triplet_loss : 0.09695020318031311\n",
            "epoch : 0, globaliter : 14, batch_idx  : 14, triplet_loss : 0.09599002450704575\n",
            "epoch : 0, globaliter : 15, batch_idx  : 15, triplet_loss : 0.09899340569972992\n",
            "epoch : 0, globaliter : 16, batch_idx  : 16, triplet_loss : 0.09734060615301132\n",
            "epoch : 0, globaliter : 17, batch_idx  : 17, triplet_loss : 0.09632319211959839\n",
            "epoch : 0, globaliter : 18, batch_idx  : 18, triplet_loss : 0.09319467097520828\n",
            "epoch : 0, globaliter : 19, batch_idx  : 19, triplet_loss : 0.09093181788921356\n",
            "epoch : 0, globaliter : 20, batch_idx  : 20, triplet_loss : 0.09367603063583374\n",
            "epoch : 0, globaliter : 21, batch_idx  : 21, triplet_loss : 0.09605221450328827\n",
            "epoch : 0, globaliter : 22, batch_idx  : 22, triplet_loss : 0.09814866632223129\n",
            "epoch : 0, globaliter : 23, batch_idx  : 23, triplet_loss : 0.09498986601829529\n",
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            "epoch : 0, globaliter : 25, batch_idx  : 25, triplet_loss : 0.09816445410251617\n",
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            "epoch : 48, globaliter : 4672, batch_idx  : 16, triplet_loss : 0.002697531133890152\n",
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            "epoch : 48, globaliter : 4678, batch_idx  : 22, triplet_loss : 0.0010650679469108582\n",
            "epoch : 48, globaliter : 4679, batch_idx  : 23, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4680, batch_idx  : 24, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4681, batch_idx  : 25, triplet_loss : 0.0008298605680465698\n",
            "epoch : 48, globaliter : 4682, batch_idx  : 26, triplet_loss : 0.011689537204802036\n",
            "epoch : 48, globaliter : 4683, batch_idx  : 27, triplet_loss : 0.0076215192675590515\n",
            "epoch : 48, globaliter : 4684, batch_idx  : 28, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4685, batch_idx  : 29, triplet_loss : 0.0012022653827443719\n",
            "epoch : 48, globaliter : 4686, batch_idx  : 30, triplet_loss : 0.0018159355968236923\n",
            "epoch : 48, globaliter : 4687, batch_idx  : 31, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4688, batch_idx  : 32, triplet_loss : 0.005202757194638252\n",
            "epoch : 48, globaliter : 4689, batch_idx  : 33, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4690, batch_idx  : 34, triplet_loss : 0.003445874899625778\n",
            "epoch : 48, globaliter : 4691, batch_idx  : 35, triplet_loss : 0.00936832744628191\n",
            "epoch : 48, globaliter : 4692, batch_idx  : 36, triplet_loss : 0.0037129074335098267\n",
            "epoch : 48, globaliter : 4693, batch_idx  : 37, triplet_loss : 0.0008207659120671451\n",
            "epoch : 48, globaliter : 4694, batch_idx  : 38, triplet_loss : 0.0\n",
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            "epoch : 48, globaliter : 4698, batch_idx  : 42, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4699, batch_idx  : 43, triplet_loss : 0.017216874286532402\n",
            "epoch : 48, globaliter : 4700, batch_idx  : 44, triplet_loss : 0.002487925114110112\n",
            "epoch : 48, globaliter : 4701, batch_idx  : 45, triplet_loss : 0.008445181883871555\n",
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            "epoch : 48, globaliter : 4704, batch_idx  : 48, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4705, batch_idx  : 49, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4706, batch_idx  : 50, triplet_loss : 0.0\n",
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            "epoch : 48, globaliter : 4711, batch_idx  : 55, triplet_loss : 0.004925819113850594\n",
            "epoch : 48, globaliter : 4712, batch_idx  : 56, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4713, batch_idx  : 57, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4714, batch_idx  : 58, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4715, batch_idx  : 59, triplet_loss : 0.007144654169678688\n",
            "epoch : 48, globaliter : 4716, batch_idx  : 60, triplet_loss : 0.0016828328371047974\n",
            "epoch : 48, globaliter : 4717, batch_idx  : 61, triplet_loss : 0.002169005572795868\n",
            "epoch : 48, globaliter : 4718, batch_idx  : 62, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4719, batch_idx  : 63, triplet_loss : 0.0010407989611849189\n",
            "epoch : 48, globaliter : 4720, batch_idx  : 64, triplet_loss : 0.0003854036331176758\n",
            "epoch : 48, globaliter : 4721, batch_idx  : 65, triplet_loss : 0.019821539521217346\n",
            "epoch : 48, globaliter : 4722, batch_idx  : 66, triplet_loss : 0.0006844967720098794\n",
            "epoch : 48, globaliter : 4723, batch_idx  : 67, triplet_loss : 0.0074181705713272095\n",
            "epoch : 48, globaliter : 4724, batch_idx  : 68, triplet_loss : 0.0\n",
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            "epoch : 48, globaliter : 4726, batch_idx  : 70, triplet_loss : 0.0038629435002803802\n",
            "epoch : 48, globaliter : 4727, batch_idx  : 71, triplet_loss : 0.0028506508097052574\n",
            "epoch : 48, globaliter : 4728, batch_idx  : 72, triplet_loss : 0.005361150950193405\n",
            "epoch : 48, globaliter : 4729, batch_idx  : 73, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4730, batch_idx  : 74, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4731, batch_idx  : 75, triplet_loss : 0.001712935627438128\n",
            "epoch : 48, globaliter : 4732, batch_idx  : 76, triplet_loss : 0.0034016978461295366\n",
            "epoch : 48, globaliter : 4733, batch_idx  : 77, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4734, batch_idx  : 78, triplet_loss : 0.001818593591451645\n",
            "epoch : 48, globaliter : 4735, batch_idx  : 79, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4736, batch_idx  : 80, triplet_loss : 0.0014284271746873856\n",
            "epoch : 48, globaliter : 4737, batch_idx  : 81, triplet_loss : 0.004562743008136749\n",
            "epoch : 48, globaliter : 4738, batch_idx  : 82, triplet_loss : 0.0027029290795326233\n",
            "epoch : 48, globaliter : 4739, batch_idx  : 83, triplet_loss : 0.0021700896322727203\n",
            "epoch : 48, globaliter : 4740, batch_idx  : 84, triplet_loss : 0.004592370241880417\n",
            "epoch : 48, globaliter : 4741, batch_idx  : 85, triplet_loss : 0.014635693281888962\n",
            "epoch : 48, globaliter : 4742, batch_idx  : 86, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4743, batch_idx  : 87, triplet_loss : 0.008970345370471478\n",
            "epoch : 48, globaliter : 4744, batch_idx  : 88, triplet_loss : 0.0032242326997220516\n",
            "epoch : 48, globaliter : 4745, batch_idx  : 89, triplet_loss : 0.028304222971200943\n",
            "epoch : 48, globaliter : 4746, batch_idx  : 90, triplet_loss : 0.00011772662401199341\n",
            "epoch : 48, globaliter : 4747, batch_idx  : 91, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4748, batch_idx  : 92, triplet_loss : 0.005566145293414593\n",
            "epoch : 48, globaliter : 4749, batch_idx  : 93, triplet_loss : 0.00356938480399549\n",
            "epoch : 48, globaliter : 4750, batch_idx  : 94, triplet_loss : 0.0\n",
            "epoch : 48, globaliter : 4751, batch_idx  : 95, triplet_loss : 0.006836841348558664\n",
            "epoch : 48, globaliter : 4752, batch_idx  : 96, triplet_loss : 0.010928978212177753\n",
            "epoch : 49, globaliter : 4753, batch_idx  : 0, triplet_loss : 0.0005428045988082886\n",
            "epoch : 49, globaliter : 4754, batch_idx  : 1, triplet_loss : 6.114691495895386e-05\n",
            "epoch : 49, globaliter : 4755, batch_idx  : 2, triplet_loss : 0.004549477715045214\n",
            "epoch : 49, globaliter : 4756, batch_idx  : 3, triplet_loss : 0.0007573887705802917\n",
            "epoch : 49, globaliter : 4757, batch_idx  : 4, triplet_loss : 0.03227876126766205\n",
            "epoch : 49, globaliter : 4758, batch_idx  : 5, triplet_loss : 0.001940503716468811\n",
            "epoch : 49, globaliter : 4759, batch_idx  : 6, triplet_loss : 0.0006597265601158142\n",
            "epoch : 49, globaliter : 4760, batch_idx  : 7, triplet_loss : 0.012607038952410221\n",
            "epoch : 49, globaliter : 4761, batch_idx  : 8, triplet_loss : 0.013499769382178783\n",
            "epoch : 49, globaliter : 4762, batch_idx  : 9, triplet_loss : 0.0018164545763283968\n",
            "epoch : 49, globaliter : 4763, batch_idx  : 10, triplet_loss : 0.002335973083972931\n",
            "epoch : 49, globaliter : 4764, batch_idx  : 11, triplet_loss : 0.006088383495807648\n",
            "epoch : 49, globaliter : 4765, batch_idx  : 12, triplet_loss : 0.0013321265578269958\n",
            "epoch : 49, globaliter : 4766, batch_idx  : 13, triplet_loss : 0.0069230287335813046\n",
            "epoch : 49, globaliter : 4767, batch_idx  : 14, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4768, batch_idx  : 15, triplet_loss : 0.004439679905772209\n",
            "epoch : 49, globaliter : 4769, batch_idx  : 16, triplet_loss : 0.0006961673498153687\n",
            "epoch : 49, globaliter : 4770, batch_idx  : 17, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4771, batch_idx  : 18, triplet_loss : 0.050632767379283905\n",
            "epoch : 49, globaliter : 4772, batch_idx  : 19, triplet_loss : 0.0027795396745204926\n",
            "epoch : 49, globaliter : 4773, batch_idx  : 20, triplet_loss : 0.003606724087148905\n",
            "epoch : 49, globaliter : 4774, batch_idx  : 21, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4775, batch_idx  : 22, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4776, batch_idx  : 23, triplet_loss : 0.00011038780212402344\n",
            "epoch : 49, globaliter : 4777, batch_idx  : 24, triplet_loss : 0.003329038619995117\n",
            "epoch : 49, globaliter : 4778, batch_idx  : 25, triplet_loss : 0.006873242557048798\n",
            "epoch : 49, globaliter : 4779, batch_idx  : 26, triplet_loss : 0.004572045058012009\n",
            "epoch : 49, globaliter : 4780, batch_idx  : 27, triplet_loss : 0.0011428386205807328\n",
            "epoch : 49, globaliter : 4781, batch_idx  : 28, triplet_loss : 0.00048182904720306396\n",
            "epoch : 49, globaliter : 4782, batch_idx  : 29, triplet_loss : 0.002040887949988246\n",
            "epoch : 49, globaliter : 4783, batch_idx  : 30, triplet_loss : 0.02227373793721199\n",
            "epoch : 49, globaliter : 4784, batch_idx  : 31, triplet_loss : 0.002241038717329502\n",
            "epoch : 49, globaliter : 4785, batch_idx  : 32, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4786, batch_idx  : 33, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4787, batch_idx  : 34, triplet_loss : 8.72388482093811e-05\n",
            "epoch : 49, globaliter : 4788, batch_idx  : 35, triplet_loss : 0.00872509740293026\n",
            "epoch : 49, globaliter : 4789, batch_idx  : 36, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4790, batch_idx  : 37, triplet_loss : 0.0016171932220458984\n",
            "epoch : 49, globaliter : 4791, batch_idx  : 38, triplet_loss : 0.004474002402275801\n",
            "epoch : 49, globaliter : 4792, batch_idx  : 39, triplet_loss : 0.001119069755077362\n",
            "epoch : 49, globaliter : 4793, batch_idx  : 40, triplet_loss : 0.0024956620763987303\n",
            "epoch : 49, globaliter : 4794, batch_idx  : 41, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4795, batch_idx  : 42, triplet_loss : 0.007098156027495861\n",
            "epoch : 49, globaliter : 4796, batch_idx  : 43, triplet_loss : 7.956475019454956e-05\n",
            "epoch : 49, globaliter : 4797, batch_idx  : 44, triplet_loss : 0.0016524922102689743\n",
            "epoch : 49, globaliter : 4798, batch_idx  : 45, triplet_loss : 0.00029427558183670044\n",
            "epoch : 49, globaliter : 4799, batch_idx  : 46, triplet_loss : 0.0010815523564815521\n",
            "epoch : 49, globaliter : 4800, batch_idx  : 47, triplet_loss : 0.0008002817630767822\n",
            "epoch : 49, globaliter : 4801, batch_idx  : 48, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4802, batch_idx  : 49, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4803, batch_idx  : 50, triplet_loss : 0.005047677084803581\n",
            "epoch : 49, globaliter : 4804, batch_idx  : 51, triplet_loss : 0.0021277682390064\n",
            "epoch : 49, globaliter : 4805, batch_idx  : 52, triplet_loss : 0.004427736159414053\n",
            "epoch : 49, globaliter : 4806, batch_idx  : 53, triplet_loss : 0.0007052049040794373\n",
            "epoch : 49, globaliter : 4807, batch_idx  : 54, triplet_loss : 0.00010669231414794922\n",
            "epoch : 49, globaliter : 4808, batch_idx  : 55, triplet_loss : 0.002260050969198346\n",
            "epoch : 49, globaliter : 4809, batch_idx  : 56, triplet_loss : 0.008594784885644913\n",
            "epoch : 49, globaliter : 4810, batch_idx  : 57, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4811, batch_idx  : 58, triplet_loss : 0.0015649236738681793\n",
            "epoch : 49, globaliter : 4812, batch_idx  : 59, triplet_loss : 0.0010428652167320251\n",
            "epoch : 49, globaliter : 4813, batch_idx  : 60, triplet_loss : 0.013955804519355297\n",
            "epoch : 49, globaliter : 4814, batch_idx  : 61, triplet_loss : 6.899610161781311e-05\n",
            "epoch : 49, globaliter : 4815, batch_idx  : 62, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4816, batch_idx  : 63, triplet_loss : 0.0010517202317714691\n",
            "epoch : 49, globaliter : 4817, batch_idx  : 64, triplet_loss : 0.002061774954199791\n",
            "epoch : 49, globaliter : 4818, batch_idx  : 65, triplet_loss : 0.001065034419298172\n",
            "epoch : 49, globaliter : 4819, batch_idx  : 66, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4820, batch_idx  : 67, triplet_loss : 0.00025960803031921387\n",
            "epoch : 49, globaliter : 4821, batch_idx  : 68, triplet_loss : 0.0005180974258109927\n",
            "epoch : 49, globaliter : 4822, batch_idx  : 69, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4823, batch_idx  : 70, triplet_loss : 0.0014180903090164065\n",
            "epoch : 49, globaliter : 4824, batch_idx  : 71, triplet_loss : 0.0016722625587135553\n",
            "epoch : 49, globaliter : 4825, batch_idx  : 72, triplet_loss : 0.016585061326622963\n",
            "epoch : 49, globaliter : 4826, batch_idx  : 73, triplet_loss : 0.0027685377281159163\n",
            "epoch : 49, globaliter : 4827, batch_idx  : 74, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4828, batch_idx  : 75, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4829, batch_idx  : 76, triplet_loss : 0.0035009298007935286\n",
            "epoch : 49, globaliter : 4830, batch_idx  : 77, triplet_loss : 0.0054955752566456795\n",
            "epoch : 49, globaliter : 4831, batch_idx  : 78, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4832, batch_idx  : 79, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4833, batch_idx  : 80, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4834, batch_idx  : 81, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4835, batch_idx  : 82, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4836, batch_idx  : 83, triplet_loss : 0.0036352500319480896\n",
            "epoch : 49, globaliter : 4837, batch_idx  : 84, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4838, batch_idx  : 85, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4839, batch_idx  : 86, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4840, batch_idx  : 87, triplet_loss : 0.010192441754043102\n",
            "epoch : 49, globaliter : 4841, batch_idx  : 88, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4842, batch_idx  : 89, triplet_loss : 0.01274787075817585\n",
            "epoch : 49, globaliter : 4843, batch_idx  : 90, triplet_loss : 0.006533609703183174\n",
            "epoch : 49, globaliter : 4844, batch_idx  : 91, triplet_loss : 0.0015874765813350677\n",
            "epoch : 49, globaliter : 4845, batch_idx  : 92, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4846, batch_idx  : 93, triplet_loss : 0.0016172254690900445\n",
            "epoch : 49, globaliter : 4847, batch_idx  : 94, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4848, batch_idx  : 95, triplet_loss : 0.0\n",
            "epoch : 49, globaliter : 4849, batch_idx  : 96, triplet_loss : 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzoBYf5YaDre",
        "colab_type": "text"
      },
      "source": [
        "![111](https://user-images.githubusercontent.com/44772344/59032140-850edf00-88a0-11e9-90f6-925901406168.PNG)\n",
        "\n",
        "## 이를 텐서보드로 확인하면 다음과 같습니다.\n",
        "\n",
        "\n",
        "## 이미 학습한 모델을 받는다면 싶다면 다음 명령어를 실행해 주세요\n",
        "\n",
        "!wget -O /content/model_47.pt \"https://www.dropbox.com/s/g5nwt0do351huc2/model_47.pt?dl=1\"</br>\n",
        "!wget -O /content/model_48.pt \"https://www.dropbox.com/s/86qev1vgg545ee4/model_48.pt?dl=1\"</br>\n",
        "!wget -O /content/model_49.pt \"https://www.dropbox.com/s/obceyvw3d40rsxo/model_49.pt?dl=1\"</br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTPm3xhyW-tz",
        "colab_type": "code",
        "outputId": "52ac5f28-6484-45ed-c960-0784104a831d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "state_dict = torch.load('./model_49.pt')\n",
        "model.load_state_dict(state_dict)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbI7RChAWg9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out_train_image = model(bef_train_image.cuda())\n",
        "X_train = out_train_image\n",
        "Y_train = bef_train_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uvt_yzLqVECP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for test_image,test_label in test_data_loader :  \n",
        "  output_test = model(test_image.cuda())\n",
        "  X_test = output_test\n",
        "  Y_test = test_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZ0acjmoXJIU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "from sklearn import (manifold, datasets, decomposition, ensemble,\n",
        "                     discriminant_analysis, random_projection)\n",
        "import torchvision.transforms.functional as Function\n",
        "from IPython.display import display\n",
        "from time import time\n",
        "from matplotlib import offsetbox\n",
        "from sklearn.neighbors import DistanceMetric\n",
        "%matplotlib inline\n",
        "\n",
        "def plot_embedding(X, y_t, title=None):\n",
        "\n",
        "    y = y_t.numpy()\n",
        "    \n",
        "    x_min, x_max = np.min(X, 0), np.max(X, 0)\n",
        "    X = (X - x_min) / (x_max - x_min)\n",
        "\n",
        "    plt.figure()\n",
        "    for i in range(X.shape[0]):\n",
        "      if i == 776 :\n",
        "        plt.text(X[i, 0], X[i, 1], str(y[i]),\n",
        "               color='black',\n",
        "               fontdict={'weight': 'bold', 'size': 30})\n",
        "      else :\n",
        "        plt.text(X[i, 0], X[i, 1], str(y[i]),\n",
        "                 color=plt.cm.Set1(y[i] / 10.),\n",
        "                 fontdict={'weight': 'bold', 'size': 9})\n",
        "\n",
        "    plt.xticks([]), plt.yticks([])\n",
        "    if title is not None:\n",
        "        plt.title(title)   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiHZBzGoY0pW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 0~98\n",
        "check_index = 80"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TYEuv2hHBRR",
        "colab_type": "code",
        "outputId": "771979fd-2657-4889-a416-e47b8bc69813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_image.shape\n",
        "test_label.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([98])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqBMAsiWXSF2",
        "colab_type": "code",
        "outputId": "3ee3bf34-5ee1-4fcb-ccdd-aed692e204ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "image_query = X_test[check_index].view(1,-1)\n",
        "label_query = Y_test[check_index].view(1)\n",
        "X_total= torch.cat([X_train, image_query], dim=0)\n",
        "Y_total= torch.cat([Y_train, label_query], dim=0)\n",
        "\n",
        "test_img = Function.to_pil_image(test_image[check_index])\n",
        "print('테스트할 이미지 : {}'.format(test_label[check_index].item()))\n",
        "display(test_img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "테스트할 이미지 : 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FRDgAB9WGl2FVVBVFccymYc2MaXMpDYvZoCiVAICorelvwkQmtNYwGSVSRVhT5ADYREhN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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F650052E8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qssqf7H1XUvI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)\n",
        "X_tsne = tsne.fit_transform(X_total.cpu().detach().numpy())\n",
        "\n",
        "pairwise_dist_t = _pairwise_distance(X_total)\n",
        "pairwise_dist_n = pairwise_dist_t.cpu().detach().numpy()\n",
        "\n",
        "pairwise_dist_sort = np.sort(pairwise_dist_n[-1][:-1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efxWLOCxXg3S",
        "colab_type": "code",
        "outputId": "b9f6b6b4-c25e-4ac4-a8fd-6988e838c466",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1127
        }
      },
      "source": [
        "test_img = Function.to_pil_image(test_image[check_index])\n",
        "print('들어온 이미지 : {}\\n'.format(test_label[check_index].item()))\n",
        "display(test_img)\n",
        "print(\"\\n\")\n",
        "\n",
        "for ii in range(5):\n",
        "  idx = np.where( pairwise_dist_n[-1] == pairwise_dist_sort[ii])\n",
        "  print('{} 번째로 비슷한 {} 번째 이미지 : '.format(ii+1,idx[0][0]))\n",
        "  img = Function.to_pil_image(bef_train_image[idx[0][0]])\n",
        "  #     img.save('test{}_{}.png'.format(Y_total[ii],ii))\n",
        "  display(img)\n",
        "  print(\"\\n\")\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "들어온 이미지 : 4\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FRDgAB9WGl2FVVBVFccymYc2MaXMpDYvZoCiVAICorelvwkQmtNYwGSVSRVhT5ADYREhN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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7FA106058240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1 번째로 비슷한 578 번째 이미지 : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rnkWrZb0kVHUTgDtTFohERCml2Pu8L1KRenaM3DmX5zlLbHpPOjNo9vZ2xFof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\nMSfJWYSYEGk2uqeuhwCAV0IiRHA3n1/M3dBcHAID3ANUiRmJiVAN3Ia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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F65005978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "2 번째로 비슷한 579 번째 이미지 : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eXPVUMAvmLFUxrNrVqzfPfvn1T7UGIm679bpddt2GxCzXV1oplsl4/N6o2b24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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F650059B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "3 번째로 비슷한 581 번째 이미지 : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a589/YzTl+cj5AcQAxG+ff/7s2W9++MkfbJo1s/gam81m6HoXhrzMZlOtlBwf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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F65005978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "4 번째로 비슷한 587 번째 이미지 : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l9clxKBweHDpjcbXMOdvJpOJJuoDl+bPVy885dlEIU0w5gCsVKOKUBJBjUk4/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\njcnMmclGxUqT8hgniEhoY5vhx+wqWosiSU0JuN92Ij7n6AtF62vhvuC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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F650059B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "5 번째로 비슷한 577 번째 이미지 : \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KJRvv9HWjjaE8Q2EBJso++eHvkdJ1vY7BXSxP3px+fXFxNhnvnpy+8Gm5u3z5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\nj05Ij/h/ZVfhPlgSuSQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=128x128 at 0x7F9F65005978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vbmieku0C0n7",
        "colab_type": "text"
      },
      "source": [
        "## 해당 그래프 그리는 방법은 TFKR 김대하님의 Github를 참고했습니다. \n",
        "\n",
        "# [바로가기](https://github.com/kdhht2334/Triplet_loss_for_image_retrieval?fbclid=IwAR2sBMRu6ae7XCMBfotevsttCDQGV5DcUvBLkUOS93izbpXlR7JrRzkJYvc)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7iH93-QXooK",
        "colab_type": "code",
        "outputId": "eeab7661-d84b-4096-a2ca-0a5b8351a271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "plot_embedding(X_tsne, Y_total,\"Sejong Landmark data set\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RhkwKv848DyZ8J49du+u+R1ofuGBG7fN/fgZvDoXPrtrXWSsUh5S/tDCX5pTj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\nQpgRgVkQBCHM/H+6B4xAIcs6WwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFOzaABybQvY",
        "colab_type": "code",
        "outputId": "9dabaf6d-f596-4adc-f799-b674f09315ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "X_tsne.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(777, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQcGyhMavtlx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}