足壓.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DcerO0kSFOsY"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow_hub as hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mKH4b8TjE-T",
        "outputId": "83e8c26d-7984-49b7-879f-96a227e32a38"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "下載資料集"
      ],
      "metadata": {
        "id": "FrYtp5yDZ5k2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ./train\n",
        "!cp \"/content/drive/MyDrive/Colab Notebooks/train.zip\" /content/train.zip\n",
        "!cd train&&unzip /content/train.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPSh6DplMMyX",
        "outputId": "9c3353e9-a0b6-415a-af40-86afd5631543"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/train.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os, cv2, random\n",
        "from sklearn.utils import shuffle\n",
        "import numpy as np\n",
        "import gc\n",
        "from tensorflow.keras.preprocessing.image import img_to_array, array_to_img\n",
        "import tensorflow as tf\n",
        "import torch"
      ],
      "metadata": {
        "id": "VevIdVZHSBhR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "讀取資料集"
      ],
      "metadata": {
        "id": "UIB9sMmOZ_vg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.preprocessing import image\n",
        "\n",
        "dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "                '/content/train/images',\n",
        "                batch_size = None,\n",
        "                label_mode = None,\n",
        "                image_size = (299, 299),\n",
        "                shuffle = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wpOI_S5TgDX",
        "outputId": "13964c74-90f6-46bc-9dde-631a3987b951"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1000 files belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = pd.read_csv('/content/train/annotation.csv')\n",
        "labels = labels.iloc[:,1:].to_numpy()"
      ],
      "metadata": {
        "id": "csfrO__qXrOh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "資料標準化"
      ],
      "metadata": {
        "id": "E-tpnVJ5aD1d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "images = []\n",
        "for a in dataset:\n",
        "     images.append(np.array(a))\n",
        "images = np.array(images)/255.\n",
        "\n",
        "print('image shape',images.shape)\n",
        "print('label shape',labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G20VnRZbXOph",
        "outputId": "692018ea-a10b-4198-d2f8-6a13bc443178"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "image shape (1000, 299, 299, 3)\n",
            "label shape (1000, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "分割訓練與測試集"
      ],
      "metadata": {
        "id": "rRM_M465aG-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test=train_test_split(images,labels, test_size=0.3, shuffle=True)"
      ],
      "metadata": {
        "id": "ppc2z7M-e6FM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pytorch"
      ],
      "metadata": {
        "id": "ihRM7lVhcEGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "from torchvision import models\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "\n",
        "batch_size = 16\n",
        "learning_rate = 0.0002\n",
        "epoch = 100"
      ],
      "metadata": {
        "id": "hVMLs8dIccmb"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "轉換為Pytorch Tensor格式"
      ],
      "metadata": {
        "id": "lEstrcPIbmsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tensor_x_train = torch.Tensor(x_train.transpose(0, 3, 1, 2)) # H*W*C to C*H*W\n",
        "tensor_y_train = torch.Tensor(y_train)\n",
        "train_datasets  = TensorDataset(tensor_x_train,tensor_y_train) \n",
        "train_dataloader = DataLoader(train_datasets, batch_size=batch_size, shuffle=True) \n",
        "\n",
        "tensor_x_test = torch.Tensor(x_test.transpose(0, 3, 1, 2)) \n",
        "tensor_y_test = torch.Tensor(y_test)\n",
        "test_datasets  = TensorDataset(tensor_x_test,tensor_y_test)\n",
        "test_dataloader = DataLoader(test_datasets, batch_size=batch_size, shuffle=True) "
      ],
      "metadata": {
        "id": "5EXuvoj73s90"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "建構網路 ResNet"
      ],
      "metadata": {
        "id": "D1fIUzJRbrik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ResNet(nn.Module):\n",
        "    def __init__(self, num_classes=4):\t   \n",
        "        super(ResNet, self).__init__()\n",
        "        self.model_resnet = models.resnet18(pretrained=True)\n",
        "        num_ftrs = self.model_resnet.fc.in_features\n",
        "        self.model_resnet.fc = nn.Identity()\n",
        "        self.fc = nn.Linear(num_ftrs, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.model_resnet(x)\n",
        "        out = self.fc(x)   \n",
        "        return out"
      ],
      "metadata": {
        "id": "Q2LnPVIFahvv"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = ResNet()\n",
        "if torch.cuda.is_available():\n",
        "    model.cuda()\n",
        "\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "loss_func = nn.MSELoss()"
      ],
      "metadata": {
        "id": "Y8IZo2uogtn3"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "模型訓練"
      ],
      "metadata": {
        "id": "pxjl66yVdpVR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history ={'train_loss':[],'test_loss':[]}\n",
        "for i in range(epoch):\n",
        "    print('epoch {}'.format(i + 1))\n",
        "    train_loss = 0.\n",
        "    test_loss = 0.\n",
        "    for img, label in train_dataloader:\n",
        "        img = Variable(img).cuda()\n",
        "        label = Variable(label).cuda()\n",
        "        optimizer.zero_grad()\n",
        "        output = model(img)\n",
        "        loss = loss_func(output, label)\n",
        "        train_loss += loss\n",
        "   \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "    with torch.no_grad():\n",
        "        for img, label in test_dataloader:\n",
        "              img = Variable(img).cuda()\n",
        "              label = Variable(label).cuda()\n",
        "              output = model(img)\n",
        "              loss = loss_func(output, label)\n",
        "              test_loss += loss\n",
        "    Train_loss = float(train_loss / (len(train_datasets)))\n",
        "    Test_loss = float(test_loss / (len(test_datasets)))\n",
        "    history['train_loss'].append(Train_loss)\n",
        "    history['test_loss'].append(Test_loss)\n",
        "\n",
        "    print(\"Train Loss:%.9f\" % Train_loss)\n",
        "    print(\"Test Loss:%.9f\" % Test_loss)"
      ],
      "metadata": {
        "id": "bGrxuu9r3KRm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cdb32da-c7bf-42e7-cc3a-c1128fb24c79"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1\n",
            "Train Loss:2379.884765625\n",
            "Test Loss:2112.548828125\n",
            "epoch 2\n",
            "Train Loss:1842.725219727\n",
            "Test Loss:1577.826171875\n",
            "epoch 3\n",
            "Train Loss:1347.054321289\n",
            "Test Loss:1119.480590820\n",
            "epoch 4\n",
            "Train Loss:928.330627441\n",
            "Test Loss:741.593322754\n",
            "epoch 5\n",
            "Train Loss:593.734375000\n",
            "Test Loss:451.518341064\n",
            "epoch 6\n",
            "Train Loss:345.814514160\n",
            "Test Loss:246.218673706\n",
            "epoch 7\n",
            "Train Loss:179.663467407\n",
            "Test Loss:118.111412048\n",
            "epoch 8\n",
            "Train Loss:77.425659180\n",
            "Test Loss:38.607501984\n",
            "epoch 9\n",
            "Train Loss:24.286712646\n",
            "Test Loss:13.310039520\n",
            "epoch 10\n",
            "Train Loss:10.127871513\n",
            "Test Loss:6.761248112\n",
            "epoch 11\n",
            "Train Loss:6.603415012\n",
            "Test Loss:5.425261497\n",
            "epoch 12\n",
            "Train Loss:4.720776081\n",
            "Test Loss:4.082118511\n",
            "epoch 13\n",
            "Train Loss:3.779805422\n",
            "Test Loss:3.547059774\n",
            "epoch 14\n",
            "Train Loss:3.081915379\n",
            "Test Loss:3.043467283\n",
            "epoch 15\n",
            "Train Loss:2.696862936\n",
            "Test Loss:3.153062344\n",
            "epoch 16\n",
            "Train Loss:2.635744333\n",
            "Test Loss:3.148567677\n",
            "epoch 17\n",
            "Train Loss:2.500668287\n",
            "Test Loss:3.006050348\n",
            "epoch 18\n",
            "Train Loss:2.515122652\n",
            "Test Loss:2.968553305\n",
            "epoch 19\n",
            "Train Loss:2.096292734\n",
            "Test Loss:2.608399391\n",
            "epoch 20\n",
            "Train Loss:2.274617434\n",
            "Test Loss:3.135797501\n",
            "epoch 21\n",
            "Train Loss:1.960991859\n",
            "Test Loss:2.794544220\n",
            "epoch 22\n",
            "Train Loss:1.909223318\n",
            "Test Loss:3.048360348\n",
            "epoch 23\n",
            "Train Loss:1.752646804\n",
            "Test Loss:3.167908907\n",
            "epoch 24\n",
            "Train Loss:1.726134658\n",
            "Test Loss:2.647781134\n",
            "epoch 25\n",
            "Train Loss:1.662393808\n",
            "Test Loss:2.658283234\n",
            "epoch 26\n",
            "Train Loss:1.764558792\n",
            "Test Loss:2.680907488\n",
            "epoch 27\n",
            "Train Loss:1.466108918\n",
            "Test Loss:3.116375208\n",
            "epoch 28\n",
            "Train Loss:1.297682285\n",
            "Test Loss:2.663793087\n",
            "epoch 29\n",
            "Train Loss:1.060050964\n",
            "Test Loss:2.594835758\n",
            "epoch 30\n",
            "Train Loss:0.942001224\n",
            "Test Loss:2.711360216\n",
            "epoch 31\n",
            "Train Loss:1.076502204\n",
            "Test Loss:2.451456308\n",
            "epoch 32\n",
            "Train Loss:1.129735589\n",
            "Test Loss:2.757297516\n",
            "epoch 33\n",
            "Train Loss:1.175982237\n",
            "Test Loss:2.582111597\n",
            "epoch 34\n",
            "Train Loss:0.934318423\n",
            "Test Loss:2.566848040\n",
            "epoch 35\n",
            "Train Loss:0.894310117\n",
            "Test Loss:2.553141356\n",
            "epoch 36\n",
            "Train Loss:0.972305179\n",
            "Test Loss:2.507139921\n",
            "epoch 37\n",
            "Train Loss:0.830539465\n",
            "Test Loss:2.460272074\n",
            "epoch 38\n",
            "Train Loss:0.857404590\n",
            "Test Loss:2.403872728\n",
            "epoch 39\n",
            "Train Loss:0.960884035\n",
            "Test Loss:2.591764927\n",
            "epoch 40\n",
            "Train Loss:0.815218031\n",
            "Test Loss:2.422940493\n",
            "epoch 41\n",
            "Train Loss:0.933922112\n",
            "Test Loss:2.337631226\n",
            "epoch 42\n",
            "Train Loss:0.706808567\n",
            "Test Loss:2.580875874\n",
            "epoch 43\n",
            "Train Loss:0.737122953\n",
            "Test Loss:2.521566629\n",
            "epoch 44\n",
            "Train Loss:0.717733204\n",
            "Test Loss:2.275302887\n",
            "epoch 45\n",
            "Train Loss:0.696905613\n",
            "Test Loss:2.404320002\n",
            "epoch 46\n",
            "Train Loss:0.659233749\n",
            "Test Loss:2.285666466\n",
            "epoch 47\n",
            "Train Loss:0.761753798\n",
            "Test Loss:2.216528893\n",
            "epoch 48\n",
            "Train Loss:0.611957371\n",
            "Test Loss:2.212345839\n",
            "epoch 49\n",
            "Train Loss:0.656643271\n",
            "Test Loss:2.199422121\n",
            "epoch 50\n",
            "Train Loss:0.621257246\n",
            "Test Loss:2.400219202\n",
            "epoch 51\n",
            "Train Loss:0.581593156\n",
            "Test Loss:2.152407169\n",
            "epoch 52\n",
            "Train Loss:0.575590372\n",
            "Test Loss:2.234047413\n",
            "epoch 53\n",
            "Train Loss:0.563485801\n",
            "Test Loss:2.193343639\n",
            "epoch 54\n",
            "Train Loss:0.609577060\n",
            "Test Loss:2.215907335\n",
            "epoch 55\n",
            "Train Loss:0.589091837\n",
            "Test Loss:2.164510012\n",
            "epoch 56\n",
            "Train Loss:0.590520561\n",
            "Test Loss:2.165340662\n",
            "epoch 57\n",
            "Train Loss:0.638656318\n",
            "Test Loss:2.257490873\n",
            "epoch 58\n",
            "Train Loss:0.622444034\n",
            "Test Loss:2.106500149\n",
            "epoch 59\n",
            "Train Loss:0.559903502\n",
            "Test Loss:2.049220085\n",
            "epoch 60\n",
            "Train Loss:0.586648703\n",
            "Test Loss:2.263511658\n",
            "epoch 61\n",
            "Train Loss:0.537704945\n",
            "Test Loss:2.205428362\n",
            "epoch 62\n",
            "Train Loss:0.530449688\n",
            "Test Loss:2.345425367\n",
            "epoch 63\n",
            "Train Loss:0.496765733\n",
            "Test Loss:2.241393089\n",
            "epoch 64\n",
            "Train Loss:0.514571726\n",
            "Test Loss:2.381491661\n",
            "epoch 65\n",
            "Train Loss:0.551000834\n",
            "Test Loss:2.227373362\n",
            "epoch 66\n",
            "Train Loss:0.536198616\n",
            "Test Loss:2.131982327\n",
            "epoch 67\n",
            "Train Loss:0.541213751\n",
            "Test Loss:2.273346901\n",
            "epoch 68\n",
            "Train Loss:0.500184834\n",
            "Test Loss:2.293817282\n",
            "epoch 69\n",
            "Train Loss:0.437351286\n",
            "Test Loss:2.055814266\n",
            "epoch 70\n",
            "Train Loss:0.573777676\n",
            "Test Loss:2.212482452\n",
            "epoch 71\n",
            "Train Loss:0.586203933\n",
            "Test Loss:2.170179129\n",
            "epoch 72\n",
            "Train Loss:0.548200309\n",
            "Test Loss:1.980640531\n",
            "epoch 73\n",
            "Train Loss:0.515426874\n",
            "Test Loss:2.085130453\n",
            "epoch 74\n",
            "Train Loss:0.458047092\n",
            "Test Loss:2.032627106\n",
            "epoch 75\n",
            "Train Loss:0.486221313\n",
            "Test Loss:2.077166796\n",
            "epoch 76\n",
            "Train Loss:0.436305314\n",
            "Test Loss:2.158528566\n",
            "epoch 77\n",
            "Train Loss:0.443826497\n",
            "Test Loss:2.138092041\n",
            "epoch 78\n",
            "Train Loss:0.512716472\n",
            "Test Loss:2.239400625\n",
            "epoch 79\n",
            "Train Loss:0.459383100\n",
            "Test Loss:2.214780092\n",
            "epoch 80\n",
            "Train Loss:0.441132843\n",
            "Test Loss:2.245763302\n",
            "epoch 81\n",
            "Train Loss:0.438608706\n",
            "Test Loss:2.027302504\n",
            "epoch 82\n",
            "Train Loss:0.517928302\n",
            "Test Loss:2.087671041\n",
            "epoch 83\n",
            "Train Loss:0.442221552\n",
            "Test Loss:2.149374008\n",
            "epoch 84\n",
            "Train Loss:0.478689611\n",
            "Test Loss:2.207424402\n",
            "epoch 85\n",
            "Train Loss:0.444301546\n",
            "Test Loss:2.028460264\n",
            "epoch 86\n",
            "Train Loss:0.478974313\n",
            "Test Loss:2.039020061\n",
            "epoch 87\n",
            "Train Loss:0.416667461\n",
            "Test Loss:2.158351898\n",
            "epoch 88\n",
            "Train Loss:0.491947323\n",
            "Test Loss:2.111344099\n",
            "epoch 89\n",
            "Train Loss:0.435865134\n",
            "Test Loss:2.060457706\n",
            "epoch 90\n",
            "Train Loss:0.434035063\n",
            "Test Loss:2.047871113\n",
            "epoch 91\n",
            "Train Loss:0.492208630\n",
            "Test Loss:2.335745335\n",
            "epoch 92\n",
            "Train Loss:0.469601393\n",
            "Test Loss:2.012410164\n",
            "epoch 93\n",
            "Train Loss:0.461851090\n",
            "Test Loss:2.370525360\n",
            "epoch 94\n",
            "Train Loss:0.513526797\n",
            "Test Loss:2.260265827\n",
            "epoch 95\n",
            "Train Loss:0.418942958\n",
            "Test Loss:2.072486162\n",
            "epoch 96\n",
            "Train Loss:0.391534388\n",
            "Test Loss:1.927185774\n",
            "epoch 97\n",
            "Train Loss:0.341347247\n",
            "Test Loss:2.108177900\n",
            "epoch 98\n",
            "Train Loss:0.382156104\n",
            "Test Loss:2.045412540\n",
            "epoch 99\n",
            "Train Loss:0.359861255\n",
            "Test Loss:1.987283826\n",
            "epoch 100\n",
            "Train Loss:0.339844704\n",
            "Test Loss:2.014022589\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LOSS紀錄"
      ],
      "metadata": {
        "id": "kI1_x8eQfnh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history['train_loss'],label='train-loss')\n",
        "plt.plot(history['test_loss'],label='test-loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.xticks([epoch for epoch in range(15)])\n",
        "plt.legend(loc='lower right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "c6on-5Yrflx4",
        "outputId": "98d900b0-161a-4d5a-8bb4-9404fd828b40"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9cf6592390>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "預測"
      ],
      "metadata": {
        "id": "XPSqpZt4lYbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imgs, labels = next(iter(test_dataloader))\n",
        "imgs = Variable(imgs).cuda()\n",
        "labels = Variable(labels).cuda()\n",
        "with torch.no_grad():\n",
        "  outputs = model(imgs)\n",
        "imgs = imgs.cpu().numpy()\n",
        "imgs = imgs.transpose(0, 2, 3, 1)\n",
        "labels = Variable(labels).cpu().numpy()\n",
        "outputs = Variable(outputs).cpu().numpy()\n",
        "imgs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WSA2I07o5hg",
        "outputId": "3085a712-388d-4b85-ac96-af7dffe9462e"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 299, 299, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FNYWQ7arx2L",
        "outputId": "8282fcf4-8fcb-40b7-f131-d7f304122e77"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 53.124954, 123.108406,  81.11635 , 384.15112 ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esQUsXGzsTwe",
        "outputId": "a7888e54-c37c-430d-aadf-f30e9ccabe3c"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 52., 131.,  77., 385.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import matplotlib \n",
        "\n",
        "dpi = matplotlib.rcParams['figure.dpi']\n",
        "show_img = cv2.resize(imgs[0],(120,400))\n",
        "\n",
        "height, width, depth = show_img.shape\n",
        "figsize = width / float(dpi), height / float(dpi)\n",
        "\n",
        "plt.figure(figsize=figsize)\n",
        "plt.axis('off')\n",
        "\n",
        "plt.scatter(labels[0][0], labels[0][1], c='r', s=120)\n",
        "plt.scatter(labels[0][2], labels[0][3], c='r', s=120)\n",
        "\n",
        "plt.scatter(outputs[0][0], outputs[0][1], c='b', s=120)\n",
        "plt.scatter(outputs[0][2], outputs[0][3], c='b', s=120)\n",
        "plt.imshow(show_img)\n",
        "plt.savefig('predict.png', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "2izrZdqcpm1M",
        "outputId": "45b4149d-e98e-4db2-f160-9c18dcb70900"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 120x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model, 'model.pt')"
      ],
      "metadata": {
        "id": "a9QpI3FDz2HZ"
      },
      "execution_count": 155,
      "outputs": []
    }
  ]
}