aidea_aso_foot_image_analysis.ipynb

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  "cells": [
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        },
        "outputId": "154f9edb-76e7-4fb7-976e-fd0db04ce8bd"
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "!ls /content/gdrive/MyDrive/aidea_aso\n",
        "!cp /content/gdrive/MyDrive/aidea_aso/submission.csv ./\n",
        "!unzip -q /content/gdrive/MyDrive/aidea_aso/train.zip\n",
        "!mv images train_images\n",
        "!mkdir train_20210106\n",
        "!unzip -q /content/gdrive/MyDrive/aidea_aso/train_20210106.zip -d train_20210106\n",
        "!unzip -q /content/gdrive/MyDrive/aidea_aso/test.zip\n",
        "!mv images test_images\n",
        "!mkdir model"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            "submission.csv\ttrain_20210106\t    train_old  動態足壓影像辨識-報告格式.docx\n",
            "test.zip\ttrain_20210106.zip  train.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHqzqWKs063F"
      },
      "source": [
        ""
      ]
    },
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      "metadata": {
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        },
        "id": "OjzQdXhHLsjC",
        "outputId": "fd74f153-10a0-4cbc-8db9-8c51f36a0428"
      },
      "source": [
        "!pip install pytorch-lightning --quiet\n",
        "!pip install -U albumentations --quiet\n",
        "!echo ---\n",
        "!ls\n",
        "!echo ---\n",
        "!ls train_20210106"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 839kB 19.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 184kB 55.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 276kB 50.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 60.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 829kB 55.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3MB 51.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 296kB 55.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 143kB 59.3MB/s \n",
            "\u001b[?25h  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 81kB 8.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 952kB 19.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 37.6MB 72kB/s \n",
            "\u001b[?25h---\n",
            "annotation.csv\tmodel\t     test_images     train_images\n",
            "gdrive\t\tsample_data  train_20210106\n",
            "---\n",
            "annotation.csv\timages\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Efuxv4HJHgXa",
        "outputId": "590c3207-a7f7-483a-f959-014ec1028947"
      },
      "source": [
        "!ls train_20210106"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "annotation.csv\timages\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7RAb4HXz7Mb"
      },
      "source": [
        "# Set the path to save PyTorch model\n",
        "MODEL_SAVE_PATH = '/content/gdrive/MyDrive/aidea_aso_model/'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSKuEODGKZlB"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import random\n",
        "from skimage import io, transform\n",
        "from pathlib import Path\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler, Subset\n",
        "from torchvision import transforms, utils, models\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.loggers import TensorBoardLogger\n",
        "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "import albumentations as A\n",
        "MODEL_SAVE_PATH = Path(MODEL_SAVE_PATH)\n",
        "%matplotlib inline"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVV_N_8veHce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "624d9620-1a3e-4da8-9be7-eb6d72f6a9b0"
      },
      "source": [
        "# combine new and old train data\n",
        "def get_new_file_name(file_name):\n",
        "    file_name = file_name.split('.')\n",
        "    assert len(file_name) == 2\n",
        "    return file_name[0] + \"_1\" + \".\" + file_name[1]\n",
        "\n",
        "new_data_path = Path('train_20210106')\n",
        "dest_data_path = Path('train_images')\n",
        "for p in (new_data_path / 'images').glob('image_*'):\n",
        "    p.rename(Path(dest_data_path, get_new_file_name(p.name)))\n",
        "print(len(list(dest_data_path.glob('image_*'))))\n",
        "\n",
        "new_annotation_df = pd.read_csv(new_data_path / \"annotation.csv\")\n",
        "dest_annotation_df = pd.read_csv(\"annotation.csv\")\n",
        "new_annotation_df['images'] = new_annotation_df['images'].apply(get_new_file_name)\n",
        "annotation_df = pd.concat([dest_annotation_df, new_annotation_df], ignore_index = True)\n",
        "\n",
        "test_data_path = Path('test_images')\n",
        "test_image_names = sorted([p.name for p in test_data_path.glob('image_*')])\n",
        "test_annotation_df = pd.DataFrame({'images': test_image_names})\n",
        "\n",
        "# find black dots for train set\n",
        "def find_black_dot(img):\n",
        "    ## 注意回傳要是 j, i、不是 i, j\n",
        "    ## 黑點大小 10 * 10\n",
        "    ## 要找 8*8 都是黑色的點\n",
        "    kernel = np.ones((8, 8), dtype=\"float\")\n",
        "    BLACK_THR = 20\n",
        "    KERNAL_THR = 2.2*5*5 + 1.5*(8*8-5*5)\n",
        "    img_black = np.sum(img <= BLACK_THR, axis = 2)\n",
        "    img_filtered = cv2.filter2D(img_black, 1, kernel)\n",
        "    img_dot_mask = img_filtered >= KERNAL_THR\n",
        "    dot_i, dot_j = np.where(img_dot_mask)\n",
        "    dot_i, dot_j = int(np.median(dot_i)), int(np.median(dot_j))\n",
        "    return (dot_j, dot_i)\n",
        "\n",
        "dot_list = [find_black_dot(io.imread(dest_data_path / f)) for f in annotation_df.images]\n",
        "dot_list_trans = list(zip(*dot_list))\n",
        "annotation_df['dot_x'] = dot_list_trans[0]\n",
        "annotation_df['dot_y'] = dot_list_trans[1]\n",
        "print(annotation_df.head())\n",
        "print()\n",
        "# find black dots for test set\n",
        "print(len(test_image_names))\n",
        "dot_list = [find_black_dot(io.imread(test_data_path / f)) for f in test_image_names]\n",
        "dot_list_trans = list(zip(*dot_list))\n",
        "test_annotation_df['dot_x'] = dot_list_trans[0]\n",
        "test_annotation_df['dot_y'] = dot_list_trans[1]\n",
        "print(test_annotation_df.head())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1424\n",
            "           images  x1   y1  x2   y2  dot_x  dot_y\n",
            "0  image_0001.png  55  136  75  383     52    275\n",
            "1  image_0002.png  62  139  42  385     67    286\n",
            "2  image_0003.png  47  117  76  368     52    254\n",
            "3  image_0004.png  66  119  37  376     65    246\n",
            "4  image_0005.png  58  147  85  381     54    276\n",
            "\n",
            "1000\n",
            "           images  dot_x  dot_y\n",
            "0  image_0001.png     58    282\n",
            "1  image_0002.png     59    279\n",
            "2  image_0003.png     52    292\n",
            "3  image_0004.png     65    275\n",
            "4  image_0005.png     55    267\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixd36UvbGhsK"
      },
      "source": [
        "# 120 * 400 * 3\n",
        "X_CROP_LOWER, X_CROP_UPPER = (3, 8) # 120\n",
        "Y_CROP_LOWER, Y_CROP_UPPER = (79, 5) # 400\n",
        "# note: width of upper and lower should logically be the same\n",
        "split_crop = {\n",
        "    'upper': {'x': X_CROP_LOWER, 'y': Y_CROP_LOWER, 'width': 120-X_CROP_LOWER-X_CROP_UPPER, 'height': 120},\n",
        "    'lower': {'x': X_CROP_LOWER, 'y': 400-Y_CROP_UPPER-75, 'width': 120-X_CROP_LOWER-X_CROP_UPPER, 'height': 95}\n",
        "}\n",
        "assert split_crop['upper']['width'] == split_crop['lower']['width']\n",
        "\n",
        "def check_annotation_range(x1, y1, x2, y2):\n",
        "    # upper box check\n",
        "    b = x1 < split_crop['upper']['x'] + split_crop['upper']['width']\n",
        "    b = b and x1 > split_crop['upper']['x']\n",
        "    b = b and y1 < split_crop['upper']['y'] + split_crop['upper']['height']\n",
        "    b = b and y1 > split_crop['upper']['y']\n",
        "    # lower box check\n",
        "    b = b and x2 < split_crop['lower']['x'] + split_crop['lower']['width']\n",
        "    b = b and x2 > split_crop['lower']['x']\n",
        "    b = b and y2 < split_crop['lower']['y'] + split_crop['lower']['height']\n",
        "    b = b and y2 > split_crop['lower']['y']\n",
        "    return b\n",
        "\n",
        "assert annotation_df['x1'].max() < split_crop['upper']['x'] + split_crop['upper']['width']\n",
        "assert annotation_df['x1'].min() > split_crop['upper']['x']\n",
        "assert annotation_df['y1'].max() < split_crop['upper']['y'] + split_crop['upper']['height']\n",
        "assert annotation_df['y1'].min() > split_crop['upper']['y']\n",
        "assert annotation_df['x2'].max() < split_crop['lower']['x'] + split_crop['lower']['width']\n",
        "assert annotation_df['x2'].min() > split_crop['lower']['x']\n",
        "assert annotation_df['y2'].max() < split_crop['lower']['y'] + split_crop['lower']['height']\n",
        "assert annotation_df['y2'].min() > split_crop['lower']['y']"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgaDbq5lfPBv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "4dc40da3-404e-4ac1-f114-30b77622b355"
      },
      "source": [
        "def show_foot(image: np.ndarray, annotations: dict=None, predictions: dict=None, black_dot: dict=None, split_crop: dict=None):\n",
        "    \"\"\"Show image with landmarks\"\"\"\n",
        "    \"\"\"split_crop: {'upper': {'x': int, 'y': int, 'width': int, 'height': int}, 'lower': {'x': int, 'y': int, 'width': int, 'height': int}}\"\"\"\n",
        "    plt.figure(figsize = (6.4*1.5,4.8*1.5))\n",
        "    plt.imshow(image)\n",
        "    if annotations:\n",
        "        plt.scatter(annotations['x1'], annotations['y1'], s=30, marker='s', c='white', edgecolors='black')\n",
        "        plt.scatter(annotations['x2'], annotations['y2'], s=30, marker='s', c='lightgreen', edgecolors='black')\n",
        "    if predictions:\n",
        "        if predictions.get('upper') is not None:\n",
        "            plt.scatter(predictions['upper'][0], predictions['upper'][1], s=15, marker='s', c='gold', edgecolors='black')\n",
        "        if predictions.get('lower') is not None:\n",
        "            plt.scatter(predictions['lower'][0], predictions['lower'][1], s=15, marker='s', c='gold', edgecolors='black')\n",
        "    if black_dot:\n",
        "        plt.scatter(black_dot['dot_x'], black_dot['dot_y'], s=20, marker='o', c='lightgrey', edgecolors='white')\n",
        "    if split_crop:\n",
        "        plt.hlines(y = split_crop['upper']['y'],\n",
        "              xmin = split_crop['upper']['x'],\n",
        "              xmax = split_crop['upper']['x'] + split_crop['upper']['width'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.hlines(y = split_crop['upper']['y'] + split_crop['upper']['height'],\n",
        "              xmin = split_crop['upper']['x'],\n",
        "              xmax = split_crop['upper']['x'] + split_crop['upper']['width'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.vlines(x = split_crop['upper']['x'],\n",
        "              ymin = split_crop['upper']['y'],\n",
        "              ymax = split_crop['upper']['y'] + split_crop['upper']['height'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.vlines(x = split_crop['upper']['x'] + split_crop['upper']['width'],\n",
        "              ymin = split_crop['upper']['y'],\n",
        "              ymax = split_crop['upper']['y'] + split_crop['upper']['height'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.text(x = split_crop['upper']['x'], y = split_crop['upper']['y'], s = 'UPPER', color = 'red')\n",
        "        #\n",
        "        plt.hlines(y = split_crop['lower']['y'],\n",
        "              xmin = split_crop['lower']['x'],\n",
        "              xmax = split_crop['lower']['x'] + split_crop['lower']['width'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.hlines(y = split_crop['lower']['y'] + split_crop['lower']['height'],\n",
        "              xmin = split_crop['lower']['x'],\n",
        "              xmax = split_crop['lower']['x'] + split_crop['lower']['width'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.vlines(x = split_crop['lower']['x'],\n",
        "              ymin = split_crop['lower']['y'],\n",
        "              ymax = split_crop['lower']['y'] + split_crop['lower']['height'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.vlines(x = split_crop['lower']['x'] + split_crop['lower']['width'],\n",
        "              ymin = split_crop['lower']['y'],\n",
        "              ymax = split_crop['lower']['y'] + split_crop['lower']['height'],\n",
        "              colors = 'grey', linestyles = 'solid')\n",
        "        plt.text(x = split_crop['lower']['x'], y = split_crop['lower']['y'], s = 'LOWER', color = 'red')\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
        "    plt.show()\n",
        "\n",
        "{'image_0980.png',}\n",
        "img_name = 'image_0083.png'\n",
        "img_annotation = annotation_df[annotation_df.images == img_name].to_dict('records')[0]\n",
        "show_foot(\n",
        "    io.imread(Path('train_images') / img_name), \n",
        "    annotations = img_annotation,\n",
        "    black_dot = img_annotation,\n",
        "    split_crop = split_crop\n",
        "    )"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 691.2x518.4 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWrDP_PaelBB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "a73899be-ae0e-45b8-8bb6-bb8f562b58f8"
      },
      "source": [
        "def _translate_mask(mask):\n",
        "    mask_y = np.where(mask)[0]\n",
        "    mask_x = np.where(mask)[1]\n",
        "    if len(mask_x) == 0 or len(mask_y) == 0:\n",
        "        return None\n",
        "    else:\n",
        "        return (mask_x.mean(), mask_y.mean())\n",
        "def elastic(img, annot, dot, pred_lower=None):\n",
        "    has_pred_lower = pred_lower is not None\n",
        "    height, width = img.shape[:2]\n",
        "    mask1 = np.zeros((*img.shape[:2], 1))\n",
        "    mask1[int(round(annot[0][1])), int(round(annot[0][0]))] = 1\n",
        "    mask2 = np.zeros((*img.shape[:2], 1))\n",
        "    mask2[int(round(annot[1][1])), int(round(annot[1][0]))] = 1\n",
        "    mask_dot = np.zeros((*img.shape[:2], 1))\n",
        "    mask_dot[int(round(dot[1])), int(round(dot[0]))] = 1\n",
        "    img_ori = np.array(img, copy=True)\n",
        "    mask1_ori = np.array(mask1, copy=True)\n",
        "    mask2_ori = np.array(mask2, copy=True)\n",
        "    mask_dot_ori = np.array(mask_dot, copy=True)\n",
        "    if has_pred_lower:\n",
        "        mask3 = np.zeros((*img.shape[:2], 1))\n",
        "        mask3[int(round(pred_lower[1])), int(round(pred_lower[0]))] = 1\n",
        "        mask3_ori = np.array(mask3, copy=True)\n",
        "    # Random affine\n",
        "    center_square = np.float32((height, width)) // 2\n",
        "    square_size = min((height, width)) // 3\n",
        "    pts1 = np.float32(\n",
        "        [\n",
        "            center_square + square_size,\n",
        "            [center_square[0] + square_size, center_square[1] - square_size],\n",
        "            center_square - square_size,\n",
        "        ]\n",
        "    )\n",
        "    if_retry = True\n",
        "    while if_retry:\n",
        "        img = np.array(img_ori, copy=True)\n",
        "        mask1 = np.array(mask1_ori, copy=True)\n",
        "        mask2 = np.array(mask2_ori, copy=True)\n",
        "        mask_dot = np.array(mask_dot_ori, copy=True)\n",
        "        if has_pred_lower:\n",
        "            mask3 = np.array(mask3_ori, copy=True)\n",
        "        alpha_affine = float(random.random()*15)\n",
        "        pts2 = pts1 + np.random.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)\n",
        "        matrix = cv2.getAffineTransform(pts1, pts2)\n",
        "        img = cv2.warpAffine(img,\n",
        "            M=matrix, dsize=(width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(255,255,255)\n",
        "        )\n",
        "        mask1 = cv2.warpAffine(mask1,\n",
        "            M=matrix, dsize=(width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0)\n",
        "        )\n",
        "        mask2 = cv2.warpAffine(mask2,\n",
        "            M=matrix, dsize=(width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0)\n",
        "        )\n",
        "        if has_pred_lower:\n",
        "            mask3 = cv2.warpAffine(mask3,\n",
        "                M=matrix, dsize=(width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0)\n",
        "            )\n",
        "            mask3 = _translate_mask(mask3)\n",
        "            if mask3 is None:\n",
        "                continue\n",
        "        mask_dot = cv2.warpAffine(mask_dot,\n",
        "            M=matrix, dsize=(width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0)\n",
        "        )\n",
        "        mask1 = _translate_mask(mask1)\n",
        "        mask2 = _translate_mask(mask2)\n",
        "        mask_dot = _translate_mask(mask_dot)\n",
        "        if mask1 is None or mask2 is None or mask_dot is None:\n",
        "            continue\n",
        "        else:\n",
        "            if_retry = False\n",
        "        new_x1, new_y1 = mask1\n",
        "        new_x2, new_y2 = mask2\n",
        "        new_dot_x, new_dot_y = mask_dot\n",
        "        if has_pred_lower:\n",
        "            new_pred_lower_x, new_pred_lower_y = mask3\n",
        "            new_pred_lower = np.array([new_pred_lower_x, new_pred_lower_y]).astype('float')\n",
        "        else:\n",
        "            new_pred_lower = None\n",
        "    return img, np.array([[new_x1, new_y1], [new_x2, new_y2]]).astype('float'), np.array([new_dot_x, new_dot_y]).astype('float'), new_pred_lower\n",
        "\n",
        "img_name = 'image_0201_1.jpg'\n",
        "img_annotation = annotation_df[annotation_df.images == img_name].to_dict('records')[0]\n",
        "print(img_annotation)\n",
        "img = io.imread(Path('train_images') / img_name)\n",
        "img, annot, dot, pred_lower = elastic(img, np.array([[img_annotation['x1'], img_annotation['y1']], [img_annotation['x2'], img_annotation['y2']]]), np.array([img_annotation['dot_x'], img_annotation['dot_y']]))\n",
        "\n",
        "print(img.shape)\n",
        "plt.imshow(img)\n",
        "plt.scatter(annot[:,0], annot[:,1], s=30, marker='s', c='white', edgecolors='black')\n",
        "plt.scatter(dot[0], dot[1], s=15, marker='o', c='red', edgecolors='white')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'images': 'image_0201_1.jpg', 'x1': 62, 'y1': 154, 'x2': 86, 'y2': 382, 'dot_x': 54, 'dot_y': 287}\n",
            "(400, 120, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwpypBnKK7Fg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "80198b51-d1cb-4687-fe1e-066d8b3e6366"
      },
      "source": [
        "trafo = A.CoarseDropout(always_apply=True, max_height=15, max_width=15, min_height=6, min_width=6, min_holes=3, max_holes=10, fill_value=(255,255,255))\n",
        "img_name = 'image_0203_1.jpg'\n",
        "img_annotation = annotation_df[annotation_df.images == img_name].to_dict('records')[0]\n",
        "print(img_annotation)\n",
        "img = io.imread(Path('train_images') / img_name)\n",
        "img_trafo = trafo(image=img)['image']\n",
        "plt.imshow(img_trafo)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'images': 'image_0203_1.jpg', 'x1': 83, 'y1': 122, 'x2': 75, 'y2': 368, 'dot_x': 62, 'dot_y': 268}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbb65101250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TVTc3Ez2Bgq"
      },
      "source": [
        "# Move, Rotate, Scale up / down\n",
        "# 平移\n",
        "\n",
        "def move_points(points, param):\n",
        "    M = np.float32([[1,0,param['X_MOVE']], [0,1,param['Y_MOVE']]])\n",
        "    if points.shape == (2,):\n",
        "        points = np.transpose(np.concatenate((points, np.array([1])), axis=0))\n",
        "    elif points.shape == (2,2):\n",
        "        points = np.transpose(np.concatenate((points, np.array([[1], [1]])), axis=1))\n",
        "    else:\n",
        "        print('unknown shape in `move_points`')\n",
        "        raise RuntimeError \n",
        "    points = np.transpose(np.matmul(M, points)[:2])\n",
        "    return points\n",
        "\n",
        "def move_image(img, param):\n",
        "    M = np.float32([[1,0,param['X_MOVE']], [0,1,param['Y_MOVE']]])\n",
        "    if random.random() < 0.5:\n",
        "        img = cv2.warpAffine(img, M, dsize=(120, 400), borderValue=(255,255,255))\n",
        "    else:\n",
        "        img = cv2.warpAffine(img, M, dsize=(120, 400), borderMode=cv2.BORDER_REPLICATE)\n",
        "    return img\n",
        "\n",
        "# 旋轉 (以 dot 為中心)\n",
        "\n",
        "def rotate_points(points, dot, param):\n",
        "    M = cv2.getRotationMatrix2D((dot[0], dot[1]), param['ANGLE'], 1)\n",
        "    if points.shape == (2,):\n",
        "        points = np.transpose(np.concatenate((points, np.array([1])), axis=0))\n",
        "    elif points.shape == (2,2):\n",
        "        points = np.transpose(np.concatenate((points, np.array([[1], [1]])), axis=1))\n",
        "    else:\n",
        "        print('unknown shape in `rotate_points`')\n",
        "        raise RuntimeError \n",
        "    points = np.transpose(np.matmul(M, points)[:2])\n",
        "    return points\n",
        "\n",
        "def rotate_image(img, dot, param):\n",
        "    M = cv2.getRotationMatrix2D((dot[0], dot[1]), param['ANGLE'], 1)\n",
        "    if random.random() < 0.5:\n",
        "        img = cv2.warpAffine(img, M, dsize=(120, 400), borderValue=(255,255,255))\n",
        "    else:\n",
        "        img = cv2.warpAffine(img, M, dsize=(120, 400), borderMode=cv2.BORDER_REPLICATE)\n",
        "    return img\n",
        "\n",
        "# 縮放\n",
        "## 放大 = 先 crop 再放大\n",
        "\n",
        "def scale_up_points(points, param):\n",
        "    points = points - np.array([param['NEW_ORI_X'], param['NEW_ORI_Y']])\n",
        "    points = points * np.array([param['PROP_X'], param['PROP_Y']])\n",
        "    return points\n",
        "\n",
        "def scale_up_image(img, param):\n",
        "    img = img[param['NEW_ORI_Y']:, param['NEW_ORI_X']:, :]\n",
        "    img = cv2.resize(img, (120, 400))\n",
        "    return img\n",
        "\n",
        "## 縮小 = 先 crop 再縮小再填充\n",
        "\n",
        "def scale_down_points(points, param):\n",
        "    points = points - np.array([param['NEW_ORI_X'], param['NEW_ORI_Y']])\n",
        "    points = points * np.array([param['PROP_X'], param['PROP_Y']])\n",
        "    points = points + np.array([param['PAD_LEFT'], param['PAD_TOP']])\n",
        "    return points\n",
        "\n",
        "def scale_down_image(img, param):\n",
        "    img = img[param['NEW_ORI_Y']:, param['NEW_ORI_X']:, :]\n",
        "    img = cv2.resize(img, (param['NEW_WIDTH'], param['NEW_HEIGHT']))\n",
        "    if random.random() < 0.5:\n",
        "        img = cv2.copyMakeBorder(img, top=param['PAD_TOP'], bottom=400-param['NEW_HEIGHT']-param['PAD_TOP'], \n",
        "                                left=param['PAD_LEFT'], right=120-param['NEW_WIDTH']-param['PAD_LEFT'],\n",
        "                                borderType=cv2.BORDER_CONSTANT,\n",
        "                                value=(255,255,255))\n",
        "    else:\n",
        "        img = cv2.copyMakeBorder(img, top=param['PAD_TOP'], bottom=400-param['NEW_HEIGHT']-param['PAD_TOP'], \n",
        "                                left=param['PAD_LEFT'], right=120-param['NEW_WIDTH']-param['PAD_LEFT'],\n",
        "                                borderType=cv2.BORDER_REPLICATE)\n",
        "    return img"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjqNG8avkgMh"
      },
      "source": [
        "# Transform: Move, rotate, scale (up or down)\n",
        "def _get_param(t):\n",
        "    param = {}\n",
        "    if 'move' in t:\n",
        "        # 平移\n",
        "        X_MOVE, Y_MOVE = random.randint(-X_CROP_LOWER-10, X_CROP_UPPER+25), random.randint(-45, Y_CROP_UPPER+5)\n",
        "        param.update({'X_MOVE': X_MOVE, 'Y_MOVE': Y_MOVE})\n",
        "    if 'rotate' in t:\n",
        "        # 旋轉\n",
        "        ANGLE = random.randint(-5, 5)\n",
        "        param.update({'ANGLE': ANGLE})\n",
        "    if 'scale_up' in t:\n",
        "        # 放大\n",
        "        NEW_ORI_X, NEW_ORI_Y = random.randint(1, X_CROP_LOWER+50), random.randint(1, 70)\n",
        "        PROP_X, PROP_Y = 120/(120-NEW_ORI_X), 400/(400-NEW_ORI_Y)\n",
        "        param.update({'NEW_ORI_X': NEW_ORI_X, 'NEW_ORI_Y': NEW_ORI_Y,\n",
        "                    'PROP_X': PROP_X, 'PROP_Y': PROP_Y})\n",
        "    elif 'scale_down' in t:\n",
        "        # 縮小\n",
        "        NEW_ORI_X, NEW_ORI_Y = random.randint(1, X_CROP_LOWER+35), random.randint(1, 55)\n",
        "        NEW_WIDTH, NEW_HEIGHT = 120-random.randint(1, X_CROP_UPPER+30)-NEW_ORI_X, 400-random.randint(1, 50)-NEW_ORI_Y\n",
        "        PROP_X, PROP_Y = NEW_WIDTH/(120-NEW_ORI_X), NEW_HEIGHT/(400-NEW_ORI_Y)\n",
        "        PAD_LEFT, PAD_TOP = random.randint(0, NEW_ORI_X), random.randint(0, NEW_ORI_Y)\n",
        "        param.update({'NEW_ORI_X': NEW_ORI_X, 'NEW_ORI_Y': NEW_ORI_Y,\n",
        "                    'NEW_WIDTH': NEW_WIDTH, 'NEW_HEIGHT': NEW_HEIGHT,\n",
        "                    'PROP_X': PROP_X, 'PROP_Y': PROP_Y,\n",
        "                    'PAD_LEFT': PAD_LEFT, 'PAD_TOP': PAD_TOP})\n",
        "    return param\n",
        "\n",
        "def _get_trans_list(prob=0.5):\n",
        "    if random.random() < 0.4:\n",
        "        return ['elastic']\n",
        "    TRANS_LIST = ['scale', 'rotate', 'move']\n",
        "    t = list()\n",
        "    while len(t) == 0:\n",
        "        for i in random.sample(range(len(TRANS_LIST)), len(TRANS_LIST)):\n",
        "            if random.random() >= prob: continue\n",
        "            if TRANS_LIST[i] == 'scale':\n",
        "                if random.random() < 0.5:\n",
        "                    t.append('scale_up')\n",
        "                else:\n",
        "                    t.append('scale_down')\n",
        "            else:\n",
        "                t.append(TRANS_LIST[i])\n",
        "    return t\n",
        "\n",
        "def move_rotate_scale(img, annot, dot, pred_lower=None, prob=0.5):\n",
        "    t = _get_trans_list(prob)\n",
        "    has_pred_lower = pred_lower is not None\n",
        "    if len(t) == 0: \n",
        "        if has_pred_lower is False:\n",
        "            pred_lower = None\n",
        "        return (img, annot, dot, pred_lower)\n",
        "    img_ori, annot_ori, dot_ori = np.array(img, copy=True), np.array(annot, copy=True), np.array(dot, copy=True)\n",
        "    if has_pred_lower:\n",
        "        pred_lower_ori = np.array(pred_lower, copy=True)\n",
        "    # get new param and transform annot and dot first\n",
        "    # if transform not legal, reset param\n",
        "    while True:\n",
        "        if_retry = False\n",
        "        param = _get_param(t)\n",
        "        \n",
        "        annot = np.array(annot_ori, copy=True)\n",
        "        dot = np.array(dot_ori, copy=True)\n",
        "        if has_pred_lower:\n",
        "            pred_lower = np.array(pred_lower_ori, copy=True)\n",
        "        \n",
        "        for trafo in t:\n",
        "            if trafo == 'move':\n",
        "                annot = move_points(annot, param)\n",
        "                dot = move_points(dot, param)\n",
        "                if has_pred_lower:\n",
        "                    pred_lower = move_points(pred_lower, param)\n",
        "            elif trafo == 'rotate':\n",
        "                annot = rotate_points(annot, dot, param)\n",
        "                if has_pred_lower:\n",
        "                    pred_lower = rotate_points(pred_lower, dot, param)\n",
        "            elif trafo == 'scale_up':\n",
        "                annot = scale_up_points(annot, param)\n",
        "                dot = scale_up_points(dot, param)\n",
        "                if has_pred_lower:\n",
        "                    pred_lower = scale_up_points(pred_lower, param)\n",
        "            elif trafo == 'scale_down':\n",
        "                annot = scale_down_points(annot, param)\n",
        "                dot = scale_down_points(dot, param)\n",
        "                if has_pred_lower:\n",
        "                    pred_lower = scale_down_points(pred_lower, param)\n",
        "            elif trafo == 'elastic':\n",
        "                if has_pred_lower:\n",
        "                    _pred_lower = pred_lower\n",
        "                else:\n",
        "                    _pred_lower = None\n",
        "                img_elastic, annot, dot, pred_lower = elastic(img, annot, dot, _pred_lower)\n",
        "            else:\n",
        "                print('warning: unknown transformation')\n",
        "    \n",
        "            if not check_annotation_range(annot[0][0], annot[0][1], annot[1][0], annot[1][1]):\n",
        "                if_retry = True\n",
        "                break\n",
        "            if has_pred_lower and not check_annotation_range(annot[0][0], annot[0][1], pred_lower[0], pred_lower[1]):\n",
        "                if_retry = True\n",
        "                break\n",
        "        if not if_retry: break\n",
        "    \n",
        "    # transform image (only if trafo legal)\n",
        "    img = np.array(img_ori, copy=True)\n",
        "    for trafo in t:\n",
        "        if trafo == 'move':\n",
        "            img = move_image(img, param)\n",
        "        elif trafo == 'rotate':\n",
        "            img = rotate_image(img, dot, param)\n",
        "        elif trafo == 'scale_up':\n",
        "            img = scale_up_image(img, param)\n",
        "        elif trafo == 'scale_down':\n",
        "            img = scale_down_image(img, param)\n",
        "        elif trafo == 'elastic':\n",
        "            img = img_elastic\n",
        "        else:\n",
        "            print('warning: unknown transformation')\n",
        "    if has_pred_lower is False:\n",
        "        pred_lower = None\n",
        "    return (img, annot, dot, pred_lower)\n",
        "\n",
        "class MoveRotateScale():\n",
        "    def __init__(self, prob=0.5):\n",
        "        self.prob = prob\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        if random.random() >= self.prob: return sample\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample['annotations']\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        image, annotations, dot, pred_lower = move_rotate_scale(image, annotations, dot, pred_lower, prob=0.6)\n",
        "        sample = {'file_name': file_name, 'image': image, 'annotations': annotations, 'dot': dot}\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = pred_lower\n",
        "        return sample"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v7KvXM5giGJ3"
      },
      "source": [
        "# Transformations\n",
        "# split_crop: {'upper': {'x': int, 'y': int, 'width': int, 'height': int}, 'lower': {'x': int, 'y': int, 'width': int, 'height': int}}\n",
        "\n",
        "def split_crop_image(img):\n",
        "    return {\n",
        "        'upper': img[split_crop['upper']['y']:(split_crop['upper']['y']+split_crop['upper']['height']),\n",
        "                     split_crop['upper']['x']:(split_crop['upper']['x']+split_crop['upper']['width']), :],\n",
        "        'lower': img[split_crop['lower']['y']:(split_crop['lower']['y']+split_crop['lower']['height']),\n",
        "                     split_crop['lower']['x']:(split_crop['lower']['x']+split_crop['lower']['width']), :]\n",
        "    }\n",
        "\n",
        "def split_crop_annotation(x1, y1, x2, y2):\n",
        "    upper = np.array([x1-split_crop['upper']['x'], y1-split_crop['upper']['y']]).astype('float')\n",
        "    lower = np.array([x2-split_crop['lower']['x'], y2-split_crop['lower']['y']]).astype('float')\n",
        "    return {'upper': upper, 'lower': lower}\n",
        "\n",
        "def split_crop_dot(x, y):\n",
        "    return np.array([x-split_crop['upper']['x'], y-split_crop['upper']['y']]).astype('float')\n",
        "\n",
        "def normalize_image(img):\n",
        "    return {'upper': img['upper'] / 255, 'lower': img['lower'] / 255}\n",
        "\n",
        "def normalize_dot(x, y):\n",
        "    H = split_crop['lower']['y'] - split_crop['upper']['y'] + split_crop['lower']['height']\n",
        "    W = split_crop['upper']['width']\n",
        "    return np.array([x / W, y / H])\n",
        "\n",
        "def normalize_annotation(annotations):\n",
        "    return {\n",
        "        'upper': annotations['upper'] / np.array([split_crop['upper'][k] for k in ['width', 'height']]),\n",
        "        'lower': annotations['lower'] / np.array([split_crop['lower'][k] for k in ['width', 'height']]),\n",
        "    }\n",
        "\n",
        "def flip_dot_horizontal(x, y):\n",
        "    return np.array([abs(x - split_crop['upper']['width']), y])\n",
        "\n",
        "def flip_annotation_horizontal(annotations):\n",
        "    return {\n",
        "        'upper': np.abs(np.array([split_crop['upper']['width'], 0]) - annotations['upper']),\n",
        "        'lower': np.abs(np.array([split_crop['lower']['width'], 0]) - annotations['lower'])\n",
        "    }\n",
        "\n",
        "def interpret_output(out):\n",
        "    out_upper, out_lower = out.get('upper'), out.get('lower')\n",
        "    # reverse normalization\n",
        "    # reverse crop\n",
        "    if out_upper:\n",
        "        out_upper = out_upper * np.array([split_crop['upper'][k] for k in ['width', 'height']])\n",
        "        out_upper = out_upper + np.array([split_crop['upper']['x'], split_crop['upper']['y']])\n",
        "    if out_lower:\n",
        "        out_lower = out_lower * np.array([split_crop['lower'][k] for k in ['width', 'height']])\n",
        "        out_lower = out_lower + np.array([split_crop['lower']['x'], split_crop['lower']['y']])\n",
        "    result = {'upper': (round(out_upper[0], 4), round(out_upper[1], 4)) if out_upper is not None else None,\n",
        "              'lower': (round(out_lower[0], 4), round(out_lower[1], 4)) if out_lower is not None else None}\n",
        "    return result\n",
        "\n",
        "class SplitCrop():\n",
        "    def __init__(self):\n",
        "        pass\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample.get('annotations')\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        image = split_crop_image(image)\n",
        "\n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': split_crop_dot(dot[0], dot[1])}\n",
        "        if annotations is not None:\n",
        "            new_annotations = split_crop_annotation(\n",
        "                **{'x1': annotations[0][0], 'y1': annotations[0][1],\n",
        "                  'x2': annotations[1][0], 'y2': annotations[1][1]}\n",
        "                )\n",
        "            sample['annotations'] = new_annotations\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = split_crop_annotation(0, 0, pred_lower[0], pred_lower[1])['lower']\n",
        "        return sample\n",
        "\n",
        "class Normalize():\n",
        "    def __init__(self):\n",
        "        pass\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample.get('annotations')\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        image = normalize_image(image)\n",
        "\n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': normalize_dot(dot[0], dot[1])}\n",
        "        if annotations is not None:\n",
        "            sample['annotations'] = normalize_annotation(annotations)\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = normalize_annotation({'upper': np.array([0., 0.]), 'lower': pred_lower})['lower']\n",
        "        return sample\n",
        "\n",
        "def reduce_noise(image, if_test=False):\n",
        "    THR = 300\n",
        "    BLUR = 13\n",
        "    image_bool = np.logical_and((image.max(axis=2) - np.median(image, axis=2) < THR), (np.median(image, axis=2) - image.min(axis=2) < THR))\n",
        "    image_bool_mask = np.repeat(image_bool[:, :, np.newaxis], repeats=3, axis=2)\n",
        "    blurred_image = cv2.medianBlur(image, BLUR)\n",
        "    transformed_image = np.where(image_bool_mask, blurred_image, image)\n",
        "    return transformed_image\n",
        "\n",
        "class RandomReduceNoise():\n",
        "    def __init__(self, prob=1.0, if_test=False):\n",
        "        self.prob = prob\n",
        "        self.if_test = if_test\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample.get('annotations')\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        if self.if_test:\n",
        "            image = reduce_noise(image, if_test=True)\n",
        "        elif random.random() < self.prob:\n",
        "            image = reduce_noise(image)\n",
        "        \n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': dot}\n",
        "        if annotations is not None:\n",
        "            sample['annotations'] = annotations\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = pred_lower\n",
        "        return sample\n",
        "\n",
        "class RandomHorizontalFlip():\n",
        "    def __init__(self, prob=0.5):\n",
        "        self.prob = prob\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample['annotations']\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        if random.random() < self.prob:\n",
        "            image = {k: cv2.flip(v, 1) for k, v in image.items()}\n",
        "            dot = flip_dot_horizontal(dot[0], dot[1])\n",
        "            annotations = flip_annotation_horizontal(annotations)\n",
        "            if pred_lower is not None:\n",
        "                pred_lower = flip_annotation_horizontal({'upper': np.array([0., 0.]), 'lower': pred_lower})['lower']\n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': dot, 'annotations': annotations}\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = pred_lower\n",
        "        return sample\n",
        "\n",
        "class RandomCoarseDropout():\n",
        "    def __init__(self, prob=0.5):\n",
        "        self.prob = prob\n",
        "        self.trafo = A.CoarseDropout(always_apply=True, max_height=17, max_width=17, min_height=6, min_width=6, min_holes=5, max_holes=10, fill_value=(255,255,255))\n",
        "    \n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample['annotations']\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        if random.random() < self.prob:\n",
        "            image = {k: self.trafo(image=v)['image'] for k, v in image.items()}\n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': dot, 'annotations': annotations}\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = pred_lower\n",
        "        return sample\n",
        "\n",
        "class ToTensor(object):\n",
        "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
        "\n",
        "    def __call__(self, sample):\n",
        "        file_name, image, dot, annotations = sample['file_name'], sample['image'], sample['dot'], sample.get('annotations')\n",
        "        pred_lower = sample.get('pred_lower')\n",
        "        # swap color channel axis \n",
        "        # numpy image: H x W x C\n",
        "        # torch image: C X H X W\n",
        "        image = {k: torch.from_numpy(v.transpose((2, 0, 1))).float() for k, v in image.items()}\n",
        "        sample = {'file_name': file_name, 'image': image, 'dot': torch.from_numpy(dot).float()}\n",
        "        if annotations is not None:\n",
        "            sample['annotations'] = {k: torch.from_numpy(v).float() for k, v in annotations.items()}\n",
        "        if pred_lower is not None:\n",
        "            sample['pred_lower'] = torch.from_numpy(pred_lower).float()\n",
        "        return sample\n",
        "\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQ_TfjF-OUoT"
      },
      "source": [
        "# PyTorch Dataset\n",
        "train_transform = transforms.Compose([MoveRotateScale(0.85), RandomReduceNoise(prob=1), SplitCrop(), RandomHorizontalFlip(), RandomCoarseDropout(0.6), Normalize(), ToTensor()])\n",
        "test_transform = transforms.Compose([RandomReduceNoise(if_test=True), SplitCrop(), Normalize(), ToTensor()])\n",
        "\n",
        "class FeetDataset(Dataset):\n",
        "    \"\"\"Feet Annotation dataset.\"\"\"\n",
        "\n",
        "    def __init__(self, mode='train', indices=None, transform=None):\n",
        "\n",
        "        self.if_pred_lower = False\n",
        "        assert mode in {'train', 'val', 'test'}\n",
        "        self.mode = mode\n",
        "        if self.mode in {'train', 'val'}:\n",
        "            self.annotation_df = annotation_df.copy()\n",
        "            self.img_dir = Path('train_images')\n",
        "            REMOVE_FILES = {'image_0980.png', 'image_0749.png', 'image_0751.png', 'image_0147.png', 'image_0747.png', 'image_0649.jpg', 'image_0479.png'}\n",
        "            img_path_list = [f for f in sorted(list(self.img_dir.glob('image_*'))) if f.name not in REMOVE_FILES]\n",
        "            self.annotation_df = self.annotation_df[~self.annotation_df['images'].isin(REMOVE_FILES)]\n",
        "        else:\n",
        "            self.annotation_df = test_annotation_df.copy()\n",
        "            self.img_dir = Path('test_images')\n",
        "            img_path_list = sorted(list(self.img_dir.glob('image_*')))\n",
        "\n",
        "        if indices:\n",
        "            self.img_path_list = [img_path_list[i] for i in sorted(indices)]\n",
        "        else: \n",
        "            self.img_path_list = img_path_list\n",
        "        \n",
        "        if transform is False:\n",
        "            self.transform = None\n",
        "        elif transform is not None:\n",
        "            self.transform = transform\n",
        "        else:\n",
        "            if self.mode == 'train':\n",
        "                self.transform = train_transform\n",
        "            else:\n",
        "                self.transform = test_transform\n",
        "\n",
        "    def update_file_removal(self, file_list):\n",
        "        self.img_path_list = [f for f in self.img_path_list if f.name not in file_list]\n",
        "        self.annotation_df = self.annotation_df[~self.annotation_df['images'].isin(file_list)]\n",
        "\n",
        "    def add_pred_lower(self, new_annotation_df):\n",
        "        self.if_pred_lower = True\n",
        "        self.annotation_df = new_annotation_df.copy()\n",
        "        if self.mode == 'test':\n",
        "            assert 'x1' not in self.annotation_df.columns\n",
        "            assert 'y2' not in self.annotation_df.columns\n",
        "        assert 'pred_lower_x' in self.annotation_df.columns\n",
        "        assert 'pred_lower_y' in self.annotation_df.columns\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_path_list)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()[0]\n",
        "        img_name = self.img_path_list[idx].name\n",
        "        img_path = self.img_dir / img_name\n",
        "        img_record = self.annotation_df[self.annotation_df.images == img_name].to_dict('records')[0]\n",
        "        image = io.imread(img_path)\n",
        "        sample = {'file_name': img_name, 'image': image, \n",
        "                  'dot': np.array([img_record[k] for k in ['dot_x', 'dot_y']])}\n",
        "        if self.mode in ['train', 'val']:\n",
        "            annotations = np.array([img_record[k] for k in ['x1', 'y1', 'x2', 'y2']])\n",
        "            annotations = annotations.astype('float').reshape(-1, 2)\n",
        "            sample['annotations'] = annotations\n",
        "        if self.if_pred_lower:\n",
        "            pred_lower = np.array([img_record[k] for k in ['pred_lower_x', 'pred_lower_y']])\n",
        "            sample['pred_lower'] = pred_lower\n",
        "        if self.transform:\n",
        "            sample = self.transform(sample)\n",
        "\n",
        "        return sample"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxtsVCDE0Lql",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34fd2848-2c03-4794-8a80-4abd232aba85"
      },
      "source": [
        "BATCH_SIZE = 65\n",
        "LEARNING_RATE = 0.0005\n",
        "VAL_SPLIT = .2\n",
        "\n",
        "RANDOM_SEED= 42\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "np.random.seed(RANDOM_SEED)\n",
        "pl.trainer.seed_everything(seed=RANDOM_SEED)\n",
        "\n",
        "class FeetDataModule(pl.LightningDataModule):\n",
        "\n",
        "    def __init__(self, batch_size=BATCH_SIZE, val_split=VAL_SPLIT):\n",
        "        super().__init__()\n",
        "        self.batch_size = batch_size\n",
        "        self.val_split = val_split\n",
        "        self.train_transform = train_transform\n",
        "        self.test_transform = test_transform\n",
        "        self.if_train_all_data = False\n",
        "\n",
        "        dataset_size = len(FeetDataset(mode='train'))\n",
        "        indices = list(range(dataset_size))\n",
        "        split = int(np.floor(self.val_split * dataset_size))\n",
        "        np.random.shuffle(indices)\n",
        "        train_indices, val_indices = indices[split:], indices[:split]\n",
        "\n",
        "        self.all_train_dataset = FeetDataset(mode='train', transform=self.test_transform)\n",
        "        self.val_dataset = FeetDataset(mode='val', indices=val_indices)\n",
        "        self.train_dataset = FeetDataset(mode='train', indices=train_indices)\n",
        "        self.test_dataset = FeetDataset(mode='test')\n",
        "\n",
        "\n",
        "    def _modify_batch_size(self):\n",
        "        while (len(self.train_dataset)%self.batch_size<=3) or (len(self.val_dataset)%self.batch_size<=3) or (len(self.test_dataset)%self.batch_size<=3):\n",
        "            self.batch_size += 1\n",
        "\n",
        "    def set_if_train_all_data(self, if_train_all_data):\n",
        "        self.if_train_all_data = self.if_train_all_data\n",
        "\n",
        "    def all_train_dataloader(self, if_test=True):\n",
        "        if if_test:\n",
        "            return DataLoader(self.all_train_dataset, batch_size=self.batch_size, shuffle=False)\n",
        "        else:\n",
        "            ds = FeetDataset(mode='train', transform=self.train_transform)\n",
        "            if self.all_train_dataset.if_pred_lower:\n",
        "                ds.add_pred_lower(self.all_train_dataset.annotation_df)\n",
        "            else:\n",
        "                print(\"Noticing: `all_train_dataset` hasn't set `pred_lower` yet when asking for `all_train_loader()`\")\n",
        "            return DataLoader(ds, batch_size=self.batch_size, shuffle=True)\n",
        "\n",
        "    def train_dataloader(self, shuffle=True):\n",
        "        if self.if_train_all_data:\n",
        "            print(\"Noticing: you're training with all data (without val split)\")\n",
        "            return self.all_train_dataloader(if_test=False)\n",
        "        else:\n",
        "            return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=shuffle)\n",
        "\n",
        "    def val_dataloader(self, shuffle=False):\n",
        "        return DataLoader(self.val_dataset, batch_size=self.batch_size, shuffle=shuffle)\n",
        "\n",
        "    def test_dataloader(self, shuffle=False):\n",
        "        return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=shuffle)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Global seed set to 42\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dWbnP99ZdOQ"
      },
      "source": [
        "class LowerFeetModel(pl.LightningModule):\n",
        "    \n",
        "    def __init__(self, learning_rate=LEARNING_RATE):\n",
        "\n",
        "        super().__init__()\n",
        "        self.learning_rate = learning_rate\n",
        "\n",
        "        resnet18_model = models.resnet34(pretrained=False)\n",
        "        resnet18_conv = nn.Sequential(*list(resnet18_model.children())[:-2])\n",
        "        self.lower_conv = resnet18_conv \n",
        "        self.lower_fc = nn.Sequential(\n",
        "            nn.Linear((512*3*4+2), 512),\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(128, 32),\n",
        "            nn.BatchNorm1d(32),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(32, 2),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "        self.flatten = nn.Flatten()\n",
        "\n",
        "    def euclidean_distance_loss(self, prediction, ground_truth):\n",
        "        diff = prediction.view(-1, 2) - ground_truth.view(-1, 2)\n",
        "        eu_dist = torch.sqrt(torch.sum(torch.pow(diff, 2), dim=1))\n",
        "        return torch.mean(eu_dist)\n",
        "\n",
        "    def forward(self, image_lower, dot):\n",
        "        lower_conv = self.lower_conv(image_lower)\n",
        "        lower_out = self.lower_fc(torch.cat((self.flatten(lower_conv), dot), 1))\n",
        "        return lower_out\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        image_lower = batch['image']['lower']\n",
        "        dot = batch['dot']\n",
        "        annotations_lower = batch['annotations']['lower']\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self.forward(image_lower, dot)\n",
        "        loss = self.euclidean_distance_loss(outputs, annotations_lower)\n",
        "\n",
        "        self.log('train_loss', loss, prog_bar=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        image_lower = batch['image']['lower']\n",
        "        dot = batch['dot']\n",
        "        annotations_lower = batch['annotations']['lower']\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self.forward(image_lower, dot)\n",
        "        loss = self.euclidean_distance_loss(outputs, annotations_lower)\n",
        "        self.log('val_loss', loss, prog_bar=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = torch.optim.AdamW(self.parameters(), lr=self.learning_rate)\n",
        "        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=3, verbose=True)\n",
        "        # return [optimizer], [scheduler]\n",
        "        return {\n",
        "            'optimizer': optimizer,\n",
        "            'lr_scheduler': scheduler,\n",
        "            'monitor': 'val_loss'\n",
        "        }"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krDM2o2_v3RZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863,
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          ]
        },
        "outputId": "f1ac8d38-10a2-4d9e-b2d5-38253d40ed67"
      },
      "source": [
        "TRAIN_LOWER = True\n",
        "TRAIN_ALL_DATA = False\n",
        "MODEL_SAVE_NAME = 'my_lower_feet_model_weights'\n",
        "\n",
        "dm = FeetDataModule()\n",
        "dm.set_if_train_all_data(TRAIN_ALL_DATA)\n",
        "model_lower = LowerFeetModel()\n",
        "\n",
        "if TRAIN_LOWER:\n",
        "    logger = TensorBoardLogger('tb_logs', name='my_lower_feet_model')\n",
        "    early_stop_callback = EarlyStopping(\n",
        "        min_delta=0.00,\n",
        "        patience=6,\n",
        "        verbose=False,\n",
        "        monitor='val_loss',\n",
        "        mode='min'\n",
        "    )\n",
        "    checkpoint_callback = ModelCheckpoint(\n",
        "        dirpath=MODEL_SAVE_PATH,\n",
        "        filename=MODEL_SAVE_NAME,\n",
        "        save_top_k=1,\n",
        "        verbose=True,\n",
        "        monitor='val_loss',\n",
        "        mode='min'\n",
        "    )\n",
        "    trainer = pl.Trainer(max_epochs=50, \n",
        "                        logger=logger, \n",
        "                        progress_bar_refresh_rate=20, \n",
        "                        gpus=-1 if torch.cuda.is_available() else 0, \n",
        "                        auto_lr_find=True,\n",
        "                        callbacks=[early_stop_callback, checkpoint_callback])\n",
        "    # trainer.tune(model_lower, dm)\n",
        "    trainer.fit(model_lower, dm)\n",
        "    assert checkpoint_callback.best_model_score.item() < 0.034\n",
        "    model_lower = LowerFeetModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n",
        "else:\n",
        "    print(f\"Reuse model: {str(MODEL_SAVE_PATH / MODEL_SAVE_NAME)+'.ckpt'}\")\n",
        "    model_lower = LowerFeetModel.load_from_checkpoint(str(MODEL_SAVE_PATH / MODEL_SAVE_NAME)+'.ckpt')\n",
        "\n",
        "model_lower.eval()\n",
        "assert model_lower.training is False"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:52: UserWarning: Checkpoint directory /content/gdrive/MyDrive/aidea_aso_model exists and is not empty.\n",
            "  warnings.warn(*args, **kwargs)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type       | Params\n",
            "------------------------------------------\n",
            "0 | lower_conv | Sequential | 21.3 M\n",
            "1 | lower_fc   | Sequential | 3.2 M \n",
            "2 | flatten    | Flatten    | 0     \n",
            "------------------------------------------\n",
            "24.5 M    Trainable params\n",
            "0         Non-trainable params\n",
            "24.5 M    Total params\n",
            "98.013    Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec2e7161da5f40508999d066391ab1ea",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f07a1b7df90e46cca7f260c08a3259e4",
              "version_minor": 0,
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0, global step 17: val_loss reached 0.32524 (best 0.32524), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Epoch 1, global step 35: val_loss reached 0.07610 (best 0.07610), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 2, global step 53: val_loss reached 0.05760 (best 0.05760), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 3, global step 71: val_loss reached 0.05143 (best 0.05143), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 4, global step 89: val_loss reached 0.04453 (best 0.04453), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 5, step 107: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 6, step 125: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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          "output_type": "stream",
          "text": [
            "Epoch 7, global step 143: val_loss reached 0.04114 (best 0.04114), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 8, step 161: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 9, step 179: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "Epoch 10, global step 197: val_loss reached 0.03759 (best 0.03759), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11, step 215: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 12, step 233: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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          "data": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 13, step 251: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 14, global step 269: val_loss reached 0.03433 (best 0.03433), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 15, step 287: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 16, step 305: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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          "data": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 17, step 323: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 18, step 341: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch    19: reducing learning rate of group 0 to 5.0000e-05.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 19, global step 359: val_loss reached 0.03332 (best 0.03332), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "Epoch 20, global step 377: val_loss reached 0.03091 (best 0.03091), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_lower_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 21, step 395: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 22, step 413: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 23, step 431: val_loss was not in top 1\n"
          ],
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        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 24, step 449: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch    25: reducing learning rate of group 0 to 5.0000e-06.\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 25, step 467: val_loss was not in top 1\n"
          ],
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        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 26, step 485: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzhdxO8IrIHd"
      },
      "source": [
        "# # Start tensorboard.\n",
        "# %reload_ext tensorboard\n",
        "# %tensorboard --logdir tb_logs"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjbKaP-7X0o-"
      },
      "source": [
        "# Check the prediction results\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "my_model_lower = model_lower.eval().to(device)\n",
        "\n",
        "# predict lower foot for all train dataset\n",
        "my_pred_lower_train = dict()\n",
        "for i, batch in enumerate(dm.all_train_dataloader()):\n",
        "    image_lower = batch['image']['lower'].to(device)\n",
        "    dot = batch['dot'].to(device)\n",
        "\n",
        "    outputs = my_model_lower(image_lower, dot)\n",
        "    pred = [interpret_output({'upper': None, 'lower': (x[0], x[1])}) for x in outputs.data.to('cpu').tolist()]\n",
        "    my_pred_lower_train.update({f: {'pred_lower_x': pred[idx]['lower'][0], 'pred_lower_y': pred[idx]['lower'][1]} for idx, f in enumerate(batch['file_name'])})"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DD32-b6kPZDJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d9ed881-5ed8-46a0-86fe-2156780bae66"
      },
      "source": [
        "# update train and val dataset\n",
        "df = dm.all_train_dataset.annotation_df.copy()\n",
        "df['pred_lower_x'] = [my_pred_lower_train[f]['pred_lower_x'] for f in df['images']]\n",
        "df['pred_lower_y'] = [my_pred_lower_train[f]['pred_lower_y'] for f in df['images']]\n",
        "print(df.tail())\n",
        "dm.all_train_dataset.add_pred_lower(df)\n",
        "dm.train_dataset.add_pred_lower(df)\n",
        "dm.val_dataset.add_pred_lower(df)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                images  x1   y1  x2  ...  dot_x  dot_y  pred_lower_x  pred_lower_y\n",
            "1419  image_0420_1.jpg  63  158  46  ...     62    311       37.6525      383.1680\n",
            "1420  image_0421_1.jpg  53  157  76  ...     64    300       80.8016      380.3346\n",
            "1421  image_0422_1.jpg  49  154  33  ...     56    293       28.4952      375.3470\n",
            "1422  image_0423_1.jpg  64  149  76  ...     55    297       82.7625      382.2083\n",
            "1423  image_0424_1.jpg  55  147  36  ...     64    296       29.2520      383.0644\n",
            "\n",
            "[5 rows x 9 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQ-ngBo2Y0Fu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "35f38835-ef48-4b0a-c35c-32740ae4103e"
      },
      "source": [
        "# View a random prediction in training data\n",
        "print(len(batch['file_name']))\n",
        "i = 0\n",
        "img_name = batch['file_name'][i]\n",
        "print(img_name)\n",
        "print(df[df.images == img_name])\n",
        "img_annotation = annotation_df[annotation_df.images == img_name].to_dict('records')[0]\n",
        "print(pred[i])\n",
        "show_foot(\n",
        "    io.imread(Path('train_images') / img_name), \n",
        "    annotations = img_annotation,\n",
        "    black_dot = img_annotation,\n",
        "    split_crop = split_crop,\n",
        "    predictions = pred[i]\n",
        "    )\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "52\n",
            "image_0948.png\n",
            "             images  x1   y1  x2   y2  dot_x  dot_y  pred_lower_x  pred_lower_y\n",
            "947  image_0948.png  59  123  76  384     54    273       76.5797      382.5799\n",
            "{'upper': None, 'lower': (76.5797, 382.5799)}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 691.2x518.4 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKGXscvZ7c_O"
      },
      "source": [
        "# predict lower foot for test dataset\n",
        "my_pred_lower_test = dict()\n",
        "for i, batch in enumerate(dm.test_dataloader()):\n",
        "    image_lower = batch['image']['lower'].to(device)\n",
        "    dot = batch['dot'].to(device)\n",
        "\n",
        "    outputs = my_model_lower(image_lower, dot)\n",
        "    pred = [interpret_output({'upper': None, 'lower': (x[0], x[1])}) for x in outputs.data.to('cpu').tolist()]\n",
        "    my_pred_lower_test.update({f: {'pred_lower_x': pred[idx]['lower'][0], 'pred_lower_y': pred[idx]['lower'][1]} for idx, f in enumerate(batch['file_name'])})"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxtapcKI7ufe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a46ec4e8-1dec-43c3-d3c1-e96d06386824"
      },
      "source": [
        "# update test dataset\n",
        "df = dm.test_dataset.annotation_df.copy()\n",
        "df['pred_lower_x'] = [my_pred_lower_test[f]['pred_lower_x'] for f in df['images']]\n",
        "df['pred_lower_y'] = [my_pred_lower_test[f]['pred_lower_y'] for f in df['images']]\n",
        "print(df.tail())\n",
        "dm.test_dataset.add_pred_lower(df)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             images  dot_x  dot_y  pred_lower_x  pred_lower_y\n",
            "995  image_0996.png     67    238       39.0998      362.0266\n",
            "996  image_0997.png     54    263       79.0246      382.1481\n",
            "997  image_0998.png     65    253       28.5796      381.0147\n",
            "998  image_0999.png     56    268       76.1049      380.9322\n",
            "999  image_1000.png     63    256       36.3968      381.2558\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzeS46pG4PaS"
      },
      "source": [
        "## Upper"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEI97ATa38o1"
      },
      "source": [
        "class UpperFeetModel(pl.LightningModule):\n",
        "    \n",
        "    def __init__(self, learning_rate=LEARNING_RATE):\n",
        "\n",
        "        super().__init__()\n",
        "        self.learning_rate = learning_rate\n",
        "\n",
        "        resnet18_model = models.resnet34(pretrained=False)\n",
        "        resnet18_conv = nn.Sequential(*list(resnet18_model.children())[:-2])\n",
        "        self.upper_conv = resnet18_conv \n",
        "        self.upper_fc = nn.Sequential(\n",
        "            nn.Linear((512*4*4+2+2), 512), # resnet18\n",
        "            nn.BatchNorm1d(512),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.BatchNorm1d(128),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(128, 32),\n",
        "            nn.BatchNorm1d(32),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(32, 2),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "        self.flatten = nn.Flatten()\n",
        "\n",
        "    def euclidean_distance_loss(self, prediction, ground_truth):\n",
        "        diff = prediction.view(-1, 2) - ground_truth.view(-1, 2)\n",
        "        eu_dist = torch.sqrt(torch.sum(torch.pow(diff, 2), dim=1))\n",
        "        return torch.mean(eu_dist)\n",
        "\n",
        "    def forward(self, image_upper, dot, pred_lower):\n",
        "        upper_conv = self.upper_conv(image_upper)\n",
        "        upper_out = self.upper_fc(torch.cat((self.flatten(upper_conv), dot, pred_lower), 1))\n",
        "        return upper_out\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        image_upper = batch['image']['upper']\n",
        "        dot = batch['dot']\n",
        "        annotations_upper = batch['annotations']['upper']\n",
        "        annotations_lower = batch['annotations']['lower']\n",
        "        pred_lower = batch['pred_lower']\n",
        "\n",
        "        RAND_NUM = random.randint(0, 2)\n",
        "        if RAND_NUM == 0:\n",
        "            pred_lower = pred_lower\n",
        "        elif RAND_NUM == 1:\n",
        "            pred_lower = annotations_lower\n",
        "        else:\n",
        "            pred_lower = (pred_lower+annotations_lower)/2\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self.forward(image_upper, dot, pred_lower)\n",
        "        loss = self.euclidean_distance_loss(outputs, annotations_upper)\n",
        "\n",
        "        self.log('train_loss', loss, prog_bar=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        image_upper = batch['image']['upper']\n",
        "        dot = batch['dot']\n",
        "        annotations_upper = batch['annotations']['upper']\n",
        "        pred_lower = batch['pred_lower']\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = self.forward(image_upper, dot, pred_lower)\n",
        "        loss = self.euclidean_distance_loss(outputs, annotations_upper)\n",
        "        self.log('val_loss', loss, prog_bar=True)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        optimizer = torch.optim.AdamW(self.parameters(), lr=self.learning_rate)\n",
        "        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=3, verbose=True)\n",
        "        # return [optimizer], [scheduler]\n",
        "        return {\n",
        "            'optimizer': optimizer,\n",
        "            'lr_scheduler': scheduler,\n",
        "            'monitor': 'val_loss'\n",
        "        }"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
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          ]
        },
        "outputId": "ad65ff85-7c2d-4027-dea6-a9de233a88f9"
      },
      "source": [
        "TRAIN_UPPER = True\n",
        "MODEL_SAVE_NAME = 'my_upper_feet_model_weights'\n",
        "\n",
        "# dm = FeetDataModule() \n",
        "# datamodule 就用之前的 dm\n",
        "UPPER_REMOVE_FILE_LIST = {'image_0478.png', 'image_0633.jpg', 'image_0663.jpg', 'image_0623.jpg', 'image_0652.jpg', \n",
        "                          'image_0888.png', 'image_0170_1.jpg', 'image_0609.jpg', 'image_0331_1.jpg', 'image_0359_1.jpg'}\n",
        "\n",
        "dm.all_train_dataset.update_file_removal(UPPER_REMOVE_FILE_LIST)\n",
        "dm.train_dataset.update_file_removal(UPPER_REMOVE_FILE_LIST)\n",
        "dm.val_dataset.update_file_removal(UPPER_REMOVE_FILE_LIST)\n",
        "dm._modify_batch_size()\n",
        "print(f\"batch_size: {dm.batch_size}\")\n",
        "\n",
        "model_upper = UpperFeetModel()\n",
        "\n",
        "if TRAIN_UPPER:\n",
        "    logger = TensorBoardLogger('tb_logs', name='my_upper_feet_model')\n",
        "    early_stop_callback = EarlyStopping(\n",
        "        min_delta=0.00,\n",
        "        patience=6,\n",
        "        verbose=False,\n",
        "        monitor='val_loss',\n",
        "        mode='min'\n",
        "    )\n",
        "    checkpoint_callback = ModelCheckpoint(\n",
        "        dirpath=MODEL_SAVE_PATH,\n",
        "        filename=MODEL_SAVE_NAME,\n",
        "        save_top_k=1,\n",
        "        verbose=True,\n",
        "        monitor='val_loss',\n",
        "        mode='min'\n",
        "    )\n",
        "    trainer = pl.Trainer(max_epochs=50, \n",
        "                        logger=logger, \n",
        "                        progress_bar_refresh_rate=20, \n",
        "                        gpus=-1 if torch.cuda.is_available() else 0, \n",
        "                        auto_lr_find=True,\n",
        "                        callbacks=[early_stop_callback, checkpoint_callback])\n",
        "    # trainer.tune(model_upper, dm)\n",
        "    trainer.fit(model_upper, dm)\n",
        "    assert checkpoint_callback.best_model_score.item() < 0.062\n",
        "    model_upper = UpperFeetModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n",
        "else:\n",
        "    print(f\"Reuse model: {str(MODEL_SAVE_PATH / MODEL_SAVE_NAME)+'.ckpt'}\")\n",
        "    model_upper = UpperFeetModel.load_from_checkpoint(str(MODEL_SAVE_PATH / MODEL_SAVE_NAME)+'.ckpt')\n",
        "model_upper.eval()\n",
        "assert model_upper.training is False"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batch_size: 65\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:52: UserWarning: Checkpoint directory /content/gdrive/MyDrive/aidea_aso_model exists and is not empty.\n",
            "  warnings.warn(*args, **kwargs)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "\n",
            "  | Name       | Type       | Params\n",
            "------------------------------------------\n",
            "0 | upper_conv | Sequential | 21.3 M\n",
            "1 | upper_fc   | Sequential | 4.3 M \n",
            "2 | flatten    | Flatten    | 0     \n",
            "------------------------------------------\n",
            "25.6 M    Trainable params\n",
            "0         Non-trainable params\n",
            "25.6 M    Total params\n",
            "102.211   Total estimated model params size (MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c357883e4644c22a2e628bf29c3693d",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0, global step 17: val_loss reached 0.16212 (best 0.16212), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1, global step 35: val_loss reached 0.08507 (best 0.08507), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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        {
          "output_type": "stream",
          "text": [
            "Epoch 2, global step 53: val_loss reached 0.07877 (best 0.07877), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 3, step 71: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 4, step 89: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 5, global step 107: val_loss reached 0.06905 (best 0.06905), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 6, step 125: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 7, step 143: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 8, global step 161: val_loss reached 0.06534 (best 0.06534), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "dea2c6a7e478440dafdc192cd517a841",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 9, step 179: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 10, global step 197: val_loss reached 0.06137 (best 0.06137), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11, step 215: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 12, step 233: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 13, step 251: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 14, step 269: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch    15: reducing learning rate of group 0 to 5.0000e-05.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "755f3b64e77b4b34bd9c1f6072654e60",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 15, step 287: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47139436c2c74128a08b146acec1000e",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 16, global step 305: val_loss reached 0.06076 (best 0.06076), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "08c88f5ca8514f2cada80ce5ec293f72",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 17, global step 323: val_loss reached 0.05738 (best 0.05738), saving model to \"/content/gdrive/MyDrive/aidea_aso_model/my_upper_feet_model_weights.ckpt\" as top 1\n"
          ],
          "name": "stderr"
        },
        {
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          "data": {
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              "model_id": "e7f91b51976c49f4bf1dc55ddbbb79ed",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 18, step 341: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 19, step 359: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 20, step 377: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 21, step 395: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch    22: reducing learning rate of group 0 to 5.0000e-06.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 22, step 413: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validating', layout=Layout(flex='2'), m…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 23, step 431: val_loss was not in top 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZV7r-aSj_js"
      },
      "source": [
        "# # Start tensorboard.\n",
        "# %reload_ext tensorboard\n",
        "# %tensorboard --logdir tb_logs/"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfUbmCiukJOs"
      },
      "source": [
        "# Check the prediction results\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "my_model_upper = model_upper.eval().to(device)\n",
        "\n",
        "def _eu_dist_loss(prediction, ground_truth):\n",
        "    diff = prediction.view(-1, 2) - ground_truth.view(-1, 2)\n",
        "    eu_dist = torch.sqrt(torch.sum(torch.pow(diff, 2), dim=1))\n",
        "    return eu_dist\n",
        "\n",
        "# predict upper foot for all train dataset\n",
        "my_pred_upper_train = dict()\n",
        "for i, batch in enumerate(dm.all_train_dataloader()):\n",
        "    image_upper = batch['image']['upper'].to(device)\n",
        "    dot = batch['dot'].to(device)\n",
        "    pred_lower = batch['pred_lower'].to(device)\n",
        "    annotations_upper = batch['annotations']['upper'].to(device)\n",
        "\n",
        "    outputs = my_model_upper(image_upper, dot, pred_lower)\n",
        "    loss = _eu_dist_loss(outputs, annotations_upper).data.to('cpu').tolist()\n",
        "    pred = [interpret_output({'upper': (x[0], x[1]), 'lower': None}) for x in outputs.data.to('cpu').tolist()]\n",
        "    my_pred_upper_train.update({f: {'pred_upper_x': pred[idx]['upper'][0], \n",
        "                                    'pred_upper_y': pred[idx]['upper'][1],\n",
        "                                    'loss': loss[idx]} for idx, f in enumerate(batch['file_name'])})"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nhOmbIqkVbK"
      },
      "source": [
        "# predict upper foot for test dataset\n",
        "my_pred_upper_test = dict()\n",
        "for i, batch in enumerate(dm.test_dataloader()):\n",
        "    image_upper = batch['image']['upper'].to(device)\n",
        "    dot = batch['dot'].to(device)\n",
        "    pred_lower = batch['pred_lower'].to(device)\n",
        "\n",
        "    outputs = my_model_upper(image_upper, dot, pred_lower)\n",
        "    pred = [interpret_output({'upper': (x[0], x[1]), 'lower': None}) for x in outputs.data.to('cpu').tolist()]\n",
        "    my_pred_upper_test.update({f: {'pred_upper_x': pred[idx]['upper'][0], 'pred_upper_y': pred[idx]['upper'][1]} for idx, f in enumerate(batch['file_name'])})"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMgzdNi-kdqc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0172fde-7b37-4197-d777-b06e7fc80eee"
      },
      "source": [
        "df = dm.test_dataset.annotation_df.copy()\n",
        "df['pred_upper_x'] = [my_pred_upper_test[f]['pred_upper_x'] for f in df['images']]\n",
        "df['pred_upper_y'] = [my_pred_upper_test[f]['pred_upper_y'] for f in df['images']]\n",
        "print(df.tail())"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             images  dot_x  dot_y  ...  pred_lower_y  pred_upper_x  pred_upper_y\n",
            "995  image_0996.png     67    238  ...      362.0266       60.1573      120.5693\n",
            "996  image_0997.png     54    263  ...      382.1481       56.6851      134.4048\n",
            "997  image_0998.png     65    253  ...      381.0147       59.1323      133.4609\n",
            "998  image_0999.png     56    268  ...      380.9322       58.7979      120.4937\n",
            "999  image_1000.png     63    256  ...      381.2558       60.0660      120.7972\n",
            "\n",
            "[5 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmLEg718uuJk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "509d1c02-ba70-4a97-deaa-c1bada70b5f1"
      },
      "source": [
        "# Final prediction and submission\n",
        "df = dm.test_dataset.annotation_df.copy()\n",
        "df['pred_upper_x'] = [my_pred_upper_test[f]['pred_upper_x'] for f in df['images']]\n",
        "df['pred_upper_y'] = [my_pred_upper_test[f]['pred_upper_y'] for f in df['images']]\n",
        "tmp = pd.read_csv('submission.csv')\n",
        "assert all([df['images'][i] == tmp['images'][i] for i in random.sample(range(len(df)), 10)])\n",
        "df = df[['images', 'pred_upper_x', 'pred_upper_y', 'pred_lower_x', 'pred_lower_y']]\n",
        "df.columns = ['images', 'x1', 'y1', 'x2', 'y2']\n",
        "print(df.tail())\n",
        "df.to_csv('my_submission.csv', index=False)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             images       x1        y1       x2        y2\n",
            "995  image_0996.png  60.1573  120.5693  39.0998  362.0266\n",
            "996  image_0997.png  56.6851  134.4048  79.0246  382.1481\n",
            "997  image_0998.png  59.1323  133.4609  28.5796  381.0147\n",
            "998  image_0999.png  58.7979  120.4937  76.1049  380.9322\n",
            "999  image_1000.png  60.0660  120.7972  36.3968  381.2558\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}