{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "yolov3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0y3wIkfSuIT",
        "colab_type": "code",
        "outputId": "0d3e6c55-9f21-4006-b2cd-e93a08e57dbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "## Check your google colab/drive settings!!! (libraries, argument paths, ...)\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "## variables for notebook\n",
        "trainingMode = 1 ## 1 : train, 2 : eval\n",
        "\n",
        "##### changes\n",
        "### changed some variable names because of argument conflicts\n",
        "### last two parts are train, test mode code. you can switch the mode with above variable, 'training'\n",
        "### there are some difficulties for separating train/eval code (making into functions), because of variable dependencies"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n",
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yh3RWBkgAjZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## TFRecord utils here\n",
        "import tensorflow as tf\n",
        "from itertools import tee\n",
        "\n",
        "class TFRecordIterator:\n",
        "    def __init__(self, path, compression=None):\n",
        "        self._core = tf.python_io.tf_record_iterator(path, tf.python_io.TFRecordOptions(compression))\n",
        "        self._iterator = iter(self._core)\n",
        "        self._iterator, self._iterator_temp = tee(self._iterator)\n",
        "        self._total_cnt = sum(1 for _ in self._iterator_temp)\n",
        "\n",
        "    def _read_value(self, feature):\n",
        "        if len(feature.int64_list.value) > 0:\n",
        "            return feature.int64_list.value\n",
        "\n",
        "        if len(feature.bytes_list.value) > 0:\n",
        "            return feature.bytes_list.value\n",
        "\n",
        "        if len(feature.float_list.value) > 0:\n",
        "            return feature.float_list.value\n",
        "\n",
        "        return None\n",
        "\n",
        "    def _read_features(self, features):\n",
        "        d = dict()\n",
        "        for data in features:\n",
        "            d[data] = self._read_value(features[data])\n",
        "        return d\n",
        "\n",
        "    def __enter__(self):\n",
        "        return self\n",
        "\n",
        "    def __exit__(self, exception_type, exception_value, traceback):\n",
        "        pass\n",
        "\n",
        "    def __iter__(self):\n",
        "        return self\n",
        "\n",
        "    def __next__(self):\n",
        "        record = next(self._iterator)\n",
        "        example = tf.train.Example()\n",
        "        example.ParseFromString(record)\n",
        "        return self._read_features(example.features.feature)\n",
        "\n",
        "    def count(self):\n",
        "        return self._total_cnt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCVOPE2XC3qE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## plot utils\n",
        "from __future__ import division, print_function\n",
        "\n",
        "import cv2\n",
        "import random\n",
        "\n",
        "def get_color_table(class_num, seed=2):\n",
        "    random.seed(seed)\n",
        "    color_table = {}\n",
        "    for i in range(class_num):\n",
        "        color_table[i] = [random.randint(0, 255) for _ in range(3)]\n",
        "    return color_table\n",
        "\n",
        "\n",
        "def plot_one_box(img, coord, label=None, color=None, line_thickness=None):\n",
        "    tl = line_thickness or int(round(0.002 * max(img.shape[0:2])))  # line thickness\n",
        "    color = color or [random.randint(0, 255) for _ in range(3)]\n",
        "    c1, c2 = (int(coord[0]), int(coord[1])), (int(coord[2]), int(coord[3]))\n",
        "    cv2.rectangle(img, c1, c2, color, thickness=tl)\n",
        "    if label:\n",
        "        tf = max(tl - 1, 1)  # font thickness\n",
        "        t_size = cv2.getTextSize(label, 0, fontScale=float(tl) / 3, thickness=tf)[0]\n",
        "        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3\n",
        "        cv2.rectangle(img, c1, c2, color, -1)  # filled\n",
        "        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, float(tl) / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SY10K9LoDJOZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## nms utils\n",
        "import numpy as np\n",
        "\n",
        "def gpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, nms_thresh=0.5):\n",
        "    boxes_list, label_list, score_list = [], [], []\n",
        "    max_boxes = tf.constant(max_boxes, dtype='int32')\n",
        "\n",
        "    boxes = tf.reshape(boxes, [-1, 4]) # '-1' means we don't konw the exact number of boxes\n",
        "    score = tf.reshape(scores, [-1, num_classes])\n",
        "\n",
        "    # Step 1: Create a filtering mask based on \"box_class_scores\" by using \"threshold\".\n",
        "    mask = tf.greater_equal(score, tf.constant(score_thresh))\n",
        "    # Step 2: Do non_max_suppression for each class\n",
        "    for i in range(num_classes):\n",
        "        # Step 3: Apply the mask to scores, boxes and pick them out\n",
        "        filter_boxes = tf.boolean_mask(boxes, mask[:,i])\n",
        "        filter_score = tf.boolean_mask(score[:,i], mask[:,i])\n",
        "        nms_indices = tf.image.non_max_suppression(boxes=filter_boxes,\n",
        "                                                   scores=filter_score,\n",
        "                                                   max_output_size=max_boxes,\n",
        "                                                   iou_threshold=nms_thresh, name='nms_indices')\n",
        "        label_list.append(tf.ones_like(tf.gather(filter_score, nms_indices), 'int32')*i)\n",
        "        boxes_list.append(tf.gather(filter_boxes, nms_indices))\n",
        "        score_list.append(tf.gather(filter_score, nms_indices))\n",
        "\n",
        "    boxes = tf.concat(boxes_list, axis=0)\n",
        "    score = tf.concat(score_list, axis=0)\n",
        "    label = tf.concat(label_list, axis=0)\n",
        "\n",
        "    return boxes, score, label\n",
        "\n",
        "\n",
        "def py_nms(boxes, scores, max_boxes=50, iou_thresh=0.5):\n",
        "    assert boxes.shape[1] == 4 and len(scores.shape) == 1\n",
        "\n",
        "    x1 = boxes[:, 0]\n",
        "    y1 = boxes[:, 1]\n",
        "    x2 = boxes[:, 2]\n",
        "    y2 = boxes[:, 3]\n",
        "\n",
        "    areas = (x2 - x1) * (y2 - y1)\n",
        "    order = scores.argsort()[::-1]\n",
        "\n",
        "    keep = []\n",
        "    while order.size > 0:\n",
        "        i = order[0]\n",
        "        keep.append(i)\n",
        "        xx1 = np.maximum(x1[i], x1[order[1:]])\n",
        "        yy1 = np.maximum(y1[i], y1[order[1:]])\n",
        "        xx2 = np.minimum(x2[i], x2[order[1:]])\n",
        "        yy2 = np.minimum(y2[i], y2[order[1:]])\n",
        "\n",
        "        w = np.maximum(0.0, xx2 - xx1 + 1)\n",
        "        h = np.maximum(0.0, yy2 - yy1 + 1)\n",
        "        inter = w * h\n",
        "        ovr = inter / (areas[i] + areas[order[1:]] - inter)\n",
        "\n",
        "        inds = np.where(ovr <= iou_thresh)[0]\n",
        "        order = order[inds + 1]\n",
        "\n",
        "    return keep[:max_boxes]\n",
        "\n",
        "\n",
        "def cpu_nms(boxes, scores, num_classes, max_boxes=50, score_thresh=0.5, iou_thresh=0.5):\n",
        "    boxes = boxes.reshape(-1, 4)\n",
        "    scores = scores.reshape(-1, num_classes)\n",
        "    picked_boxes, picked_score, picked_label = [], [], []\n",
        "\n",
        "    for i in range(num_classes):\n",
        "        indices = np.where(scores[:,i] >= score_thresh)\n",
        "        filter_boxes = boxes[indices]\n",
        "        filter_scores = scores[:,i][indices]\n",
        "        if len(filter_boxes) == 0: \n",
        "            continue\n",
        "\n",
        "        indices = py_nms(filter_boxes, filter_scores,\n",
        "                         max_boxes=max_boxes, iou_thresh=iou_thresh)\n",
        "        picked_boxes.append(filter_boxes[indices])\n",
        "        picked_score.append(filter_scores[indices])\n",
        "        picked_label.append(np.ones(len(indices), dtype='int32')*i)\n",
        "    if len(picked_boxes) == 0: \n",
        "        return None, None, None\n",
        "\n",
        "    boxes = np.concatenate(picked_boxes, axis=0)\n",
        "    score = np.concatenate(picked_score, axis=0)\n",
        "    label = np.concatenate(picked_label, axis=0)\n",
        "\n",
        "    return boxes, score, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dg-ZKHmRDlPp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## misc utils\n",
        "class AverageMeter(object):\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.average = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.average = self.sum / float(self.count)\n",
        "\n",
        "\n",
        "def parse_anchors(anchor_path):\n",
        "    anchors = np.reshape(np.asarray(open(anchor_path, 'r').read().split(','), np.float32), [-1, 2])\n",
        "    return anchors\n",
        "\n",
        "\n",
        "def read_class_names(class_name_path):\n",
        "    names = {}\n",
        "    with open(class_name_path, 'r') as data:\n",
        "        for ID, name in enumerate(data):\n",
        "            names[ID] = name.strip('\\n')\n",
        "    return names\n",
        "\n",
        "\n",
        "def shuffle_and_overwrite(file_name):\n",
        "    content = open(file_name, 'r').readlines()\n",
        "    random.shuffle(content)\n",
        "    with open(file_name, 'w') as f:\n",
        "        for line in content:\n",
        "            f.write(line)\n",
        "\n",
        "\n",
        "def update_dict(ori_dict, new_dict):\n",
        "    if not ori_dict:\n",
        "        return new_dict\n",
        "    for key in ori_dict:\n",
        "        ori_dict[key] += new_dict[key]\n",
        "    return ori_dict\n",
        "\n",
        "\n",
        "def list_add(ori_list, new_list):\n",
        "    for i in range(len(ori_list)):\n",
        "        ori_list[i] += new_list[i]\n",
        "    return ori_list\n",
        "\n",
        "\n",
        "def load_weights(var_list, weights_file):\n",
        "    with open(weights_file, \"rb\") as fp:\n",
        "        np.fromfile(fp, dtype=np.int32, count=5)\n",
        "        weights = np.fromfile(fp, dtype=np.float32)\n",
        "\n",
        "    ptr = 0\n",
        "    i = 0\n",
        "    assign_ops = []\n",
        "    while i < len(var_list) - 1:\n",
        "        var1 = var_list[i]\n",
        "        var2 = var_list[i + 1]\n",
        "        if 'Conv' in var1.name.split('/')[-2]:\n",
        "            if 'BatchNorm' in var2.name.split('/')[-2]:\n",
        "                gamma, beta, mean, var = var_list[i + 1:i + 5]\n",
        "                batch_norm_vars = [beta, gamma, mean, var]\n",
        "                for var in batch_norm_vars:\n",
        "                    shape = var.shape.as_list()\n",
        "                    num_params = np.prod(shape)\n",
        "                    var_weights = weights[ptr:ptr + num_params].reshape(shape)\n",
        "                    ptr += num_params\n",
        "                    assign_ops.append(tf.assign(var, var_weights, validate_shape=True))\n",
        "                i += 4\n",
        "            elif 'Conv' in var2.name.split('/')[-2]:\n",
        "                # load biases\n",
        "                bias = var2\n",
        "                bias_shape = bias.shape.as_list()\n",
        "                bias_params = np.prod(bias_shape)\n",
        "                bias_weights = weights[ptr:ptr +\n",
        "                                       bias_params].reshape(bias_shape)\n",
        "                ptr += bias_params\n",
        "                assign_ops.append(tf.assign(bias, bias_weights, validate_shape=True))\n",
        "                i += 1\n",
        "\n",
        "            shape = var1.shape.as_list()\n",
        "            num_params = np.prod(shape)\n",
        "\n",
        "            var_weights = weights[ptr:ptr + num_params].reshape(\n",
        "                (shape[3], shape[2], shape[0], shape[1]))\n",
        "\n",
        "            var_weights = np.transpose(var_weights, (2, 3, 1, 0))\n",
        "            ptr += num_params\n",
        "            assign_ops.append(\n",
        "                tf.assign(var1, var_weights, validate_shape=True))\n",
        "            i += 1\n",
        "\n",
        "    return assign_ops\n",
        "\n",
        "\n",
        "def config_learning_rate(global_step):\n",
        "    ## fixes for removing arg paramter\n",
        "    global lr_type, learning_rate_init, lr_decay_freq, lr_decay_factor, lr_lower_bound, total_epoches, use_warm_up, warm_up_epoch, train_batch_num, lr_lower_bound, pw_boundaries, pw_values\n",
        "\n",
        "    if lr_type == 'exponential':\n",
        "        lr_tmp = tf.train.exponential_decay(learning_rate_init, global_step, lr_decay_freq,\n",
        "                                            lr_decay_factor, staircase=True, name='exponential_learning_rate')\n",
        "        return tf.maximum(lr_tmp, lr_lower_bound)\n",
        "    elif lr_type == 'cosine_decay':\n",
        "        train_steps = (total_epoches - float(use_warm_up) * warm_up_epoch) * train_batch_num\n",
        "        return lr_lower_bound + 0.5 * (learning_rate_init - lr_lower_bound) * \\\n",
        "            (1 + tf.cos(global_step / train_steps * np.pi))\n",
        "    elif lr_type == 'cosine_decay_restart':\n",
        "        return tf.train.cosine_decay_restarts(learning_rate_init, global_step, \n",
        "                                              lr_decay_freq, t_mul=2.0, m_mul=1.0, \n",
        "                                              name='cosine_decay_learning_rate_restart')\n",
        "    elif lr_type == 'fixed':\n",
        "        return tf.convert_to_tensor(learning_rate_init, name='fixed_learning_rate')\n",
        "    elif lr_type == 'piecewise':\n",
        "        return tf.train.piecewise_constant(global_step, boundaries=pw_boundaries, values=pw_values,\n",
        "                                           name='piecewise_learning_rate')\n",
        "    else:\n",
        "        raise ValueError('Unsupported learning rate type!')\n",
        "\n",
        "\n",
        "def config_optimizer(optimizer_name, learning_rate, decay=0.9, momentum=0.9):\n",
        "    if optimizer_name == 'momentum':\n",
        "        return tf.train.MomentumOptimizer(learning_rate, momentum=momentum)\n",
        "    elif optimizer_name == 'rmsprop':\n",
        "        return tf.train.RMSPropOptimizer(learning_rate, decay=decay, momentum=momentum)\n",
        "    elif optimizer_name == 'adam':\n",
        "        return tf.train.AdamOptimizer(learning_rate)\n",
        "    elif optimizer_name == 'sgd':\n",
        "        return tf.train.GradientDescentOptimizer(learning_rate)\n",
        "    else:\n",
        "        raise ValueError('Unsupported optimizer type!')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIlZhFLYD0d8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## data utils\n",
        "\n",
        "import sys\n",
        "\n",
        "PY_VERSION = sys.version_info[0]\n",
        "iter_cnt = 0\n",
        "\n",
        "def _parse_tfrecord(data):\n",
        "    example = tf.train.Example()\n",
        "    example.ParseFromString(data)\n",
        "    features = example.features.feature\n",
        "    return features\n",
        "\n",
        "def parse_tfrecord(data):\n",
        "    # tfrecord parser for TFRecordDataset (raw data)\n",
        "    features = _parse_tfrecord(data)\n",
        "    index = features['index'].int64_list.value[0]\n",
        "    encoded_image = np.frombuffer(features['image'].bytes_list.value[0], dtype = np.uint8)\n",
        "    width = features['width'].int64_list.value[0]\n",
        "    height = features['height'].int64_list.value[0]\n",
        "    boxes = features['boxes'].int64_list.value\n",
        "\n",
        "    assert len(boxes) % 5 == 0, 'Annotation error occured in box array.'\n",
        "    box_cnt = len(boxes) // 5\n",
        "\n",
        "    aligned_boxes = []\n",
        "    labels = []\n",
        "\n",
        "    for i in range(box_cnt):\n",
        "        label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]), float(boxes[i * 5 + 4]) ## do we need to change int to float? is there float rectangle sample?\n",
        "        aligned_boxes.append([x_min, y_min, x_max, y_max])\n",
        "        labels.append(label)\n",
        "\n",
        "    aligned_boxes = np.asarray(aligned_boxes, np.float32)\n",
        "    labels = np.asarray(labels, np.int64)\n",
        "\n",
        "    return index, encoded_image, aligned_boxes, labels, width, height\n",
        "\n",
        "def parse_record(features):\n",
        "    # tfrecord parser for TFRecordIterator (primitive data)\n",
        "\n",
        "    index = int(features['index'][0])\n",
        "    encoded_image = np.frombuffer(features['image'][0], dtype = np.uint8)\n",
        "    width = int(features['width'][0])\n",
        "    height = int(features['height'][0])\n",
        "    boxes = features['boxes']\n",
        "\n",
        "    assert len(boxes) % 5 == 0, 'Annotation error occured in box array.'\n",
        "    box_cnt = len(boxes) // 5\n",
        "\n",
        "    aligned_boxes = []\n",
        "    labels = []\n",
        "\n",
        "    for i in range(box_cnt):\n",
        "        label, x_min, y_min, x_max, y_max = int(boxes[i * 5]), float(boxes[i * 5 + 1]), float(boxes[i * 5 + 2]), float(boxes[i * 5 + 3]), float(boxes[i * 5 + 4])\n",
        "        aligned_boxes.append([x_min, y_min, x_max, y_max])\n",
        "        labels.append(label)\n",
        "\n",
        "    aligned_boxes = np.asarray(aligned_boxes, np.float32)\n",
        "    labels = np.asarray(labels, np.int64)\n",
        "\n",
        "    return index, encoded_image, aligned_boxes, labels, width, height\n",
        "\n",
        "def bbox_crop(bbox, crop_box=None, allow_outside_center=True):\n",
        "    bbox = bbox.copy()\n",
        "    if crop_box is None:\n",
        "        return bbox\n",
        "    if not len(crop_box) == 4:\n",
        "        raise ValueError(\n",
        "            \"Invalid crop_box parameter, requires length 4, given {}\".format(str(crop_box)))\n",
        "    if sum([int(c is None) for c in crop_box]) == 4:\n",
        "        return bbox\n",
        "\n",
        "    l, t, w, h = crop_box\n",
        "\n",
        "    left = l if l else 0\n",
        "    top = t if t else 0\n",
        "    right = left + (w if w else np.inf)\n",
        "    bottom = top + (h if h else np.inf)\n",
        "    crop_bbox = np.array((left, top, right, bottom))\n",
        "\n",
        "    if allow_outside_center:\n",
        "        mask = np.ones(bbox.shape[0], dtype=bool)\n",
        "    else:\n",
        "        centers = (bbox[:, :2] + bbox[:, 2:4]) / 2\n",
        "        mask = np.logical_and(crop_bbox[:2] <= centers, centers < crop_bbox[2:]).all(axis=1)\n",
        "\n",
        "    # transform borders\n",
        "    bbox[:, :2] = np.maximum(bbox[:, :2], crop_bbox[:2])\n",
        "    bbox[:, 2:4] = np.minimum(bbox[:, 2:4], crop_bbox[2:4])\n",
        "    bbox[:, :2] -= crop_bbox[:2]\n",
        "    bbox[:, 2:4] -= crop_bbox[:2]\n",
        "\n",
        "    mask = np.logical_and(mask, (bbox[:, :2] < bbox[:, 2:4]).all(axis=1))\n",
        "    bbox = bbox[mask]\n",
        "    return bbox\n",
        "\n",
        "def bbox_iou(bbox_a, bbox_b, offset=0):\n",
        "    if bbox_a.shape[1] < 4 or bbox_b.shape[1] < 4:\n",
        "        raise IndexError(\"Bounding boxes axis 1 must have at least length 4\")\n",
        "\n",
        "    tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])\n",
        "    br = np.minimum(bbox_a[:, None, 2:4], bbox_b[:, 2:4])\n",
        "\n",
        "    area_i = np.prod(br - tl + offset, axis=2) * (tl < br).all(axis=2)\n",
        "    area_a = np.prod(bbox_a[:, 2:4] - bbox_a[:, :2] + offset, axis=1)\n",
        "    area_b = np.prod(bbox_b[:, 2:4] - bbox_b[:, :2] + offset, axis=1)\n",
        "    return area_i / (area_a[:, None] + area_b - area_i)\n",
        "\n",
        "\n",
        "def random_crop_with_constraints(bbox, size, min_scale=0.3, max_scale=1,\n",
        "                                 max_aspect_ratio=2, constraints=None,\n",
        "                                 max_trial=50):\n",
        "    # default params in paper\n",
        "    if constraints is None:\n",
        "        constraints = (\n",
        "            (0.1, None),\n",
        "            (0.3, None),\n",
        "            (0.5, None),\n",
        "            (0.7, None),\n",
        "            (0.9, None),\n",
        "            (None, 1),\n",
        "        )\n",
        "\n",
        "    w, h = size\n",
        "\n",
        "    candidates = [(0, 0, w, h)]\n",
        "    for min_iou, max_iou in constraints:\n",
        "        min_iou = -np.inf if min_iou is None else min_iou\n",
        "        max_iou = np.inf if max_iou is None else max_iou\n",
        "\n",
        "        for _ in range(max_trial):\n",
        "            scale = random.uniform(min_scale, max_scale)\n",
        "            aspect_ratio = random.uniform(\n",
        "                max(1 / max_aspect_ratio, scale * scale),\n",
        "                min(max_aspect_ratio, 1 / (scale * scale)))\n",
        "            crop_h = int(h * scale / np.sqrt(aspect_ratio))\n",
        "            crop_w = int(w * scale * np.sqrt(aspect_ratio))\n",
        "\n",
        "            crop_t = random.randrange(h - crop_h)\n",
        "            crop_l = random.randrange(w - crop_w)\n",
        "            crop_bb = np.array((crop_l, crop_t, crop_l + crop_w, crop_t + crop_h))\n",
        "\n",
        "            if len(bbox) == 0:\n",
        "                top, bottom = crop_t, crop_t + crop_h\n",
        "                left, right = crop_l, crop_l + crop_w\n",
        "                return bbox, (left, top, right-left, bottom-top)\n",
        "\n",
        "            iou = bbox_iou(bbox, crop_bb[np.newaxis])\n",
        "            if min_iou <= iou.min() and iou.max() <= max_iou:\n",
        "                top, bottom = crop_t, crop_t + crop_h\n",
        "                left, right = crop_l, crop_l + crop_w\n",
        "                candidates.append((left, top, right-left, bottom-top))\n",
        "                break\n",
        "\n",
        "    # random select one\n",
        "    while candidates:\n",
        "        crop = candidates.pop(np.random.randint(0, len(candidates)))\n",
        "        new_bbox = bbox_crop(bbox, crop, allow_outside_center=False)\n",
        "        if new_bbox.size < 1:\n",
        "            continue\n",
        "        new_crop = (crop[0], crop[1], crop[2], crop[3])\n",
        "        return new_bbox, new_crop\n",
        "    return bbox, (0, 0, w, h)\n",
        "\n",
        "\n",
        "def random_color_distort(img, brightness_delta=32, hue_vari=18, sat_vari=0.5, val_vari=0.5):\n",
        "    def random_hue(img_hsv, hue_vari, p=0.5):\n",
        "        if np.random.uniform(0, 1) > p:\n",
        "            hue_delta = np.random.randint(-hue_vari, hue_vari)\n",
        "            img_hsv[:, :, 0] = (img_hsv[:, :, 0] + hue_delta) % 180\n",
        "        return img_hsv\n",
        "\n",
        "    def random_saturation(img_hsv, sat_vari, p=0.5):\n",
        "        if np.random.uniform(0, 1) > p:\n",
        "            sat_mult = 1 + np.random.uniform(-sat_vari, sat_vari)\n",
        "            img_hsv[:, :, 1] *= sat_mult\n",
        "        return img_hsv\n",
        "\n",
        "    def random_value(img_hsv, val_vari, p=0.5):\n",
        "        if np.random.uniform(0, 1) > p:\n",
        "            val_mult = 1 + np.random.uniform(-val_vari, val_vari)\n",
        "            img_hsv[:, :, 2] *= val_mult\n",
        "        return img_hsv\n",
        "\n",
        "    def random_brightness(img, brightness_delta, p=0.5):\n",
        "        if np.random.uniform(0, 1) > p:\n",
        "            img = img.astype(np.float32)\n",
        "            brightness_delta = int(np.random.uniform(-brightness_delta, brightness_delta))\n",
        "            img = img + brightness_delta\n",
        "        return np.clip(img, 0, 255)\n",
        "\n",
        "    # brightness\n",
        "    img = random_brightness(img, brightness_delta)\n",
        "    img = img.astype(np.uint8)\n",
        "\n",
        "    # color jitter\n",
        "    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.float32)\n",
        "\n",
        "    if np.random.randint(0, 2):\n",
        "        img_hsv = random_value(img_hsv, val_vari)\n",
        "        img_hsv = random_saturation(img_hsv, sat_vari)\n",
        "        img_hsv = random_hue(img_hsv, hue_vari)\n",
        "    else:\n",
        "        img_hsv = random_saturation(img_hsv, sat_vari)\n",
        "        img_hsv = random_hue(img_hsv, hue_vari)\n",
        "        img_hsv = random_value(img_hsv, val_vari)\n",
        "\n",
        "    img_hsv = np.clip(img_hsv, 0, 255)\n",
        "    img = cv2.cvtColor(img_hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)\n",
        "\n",
        "    return img\n",
        "\n",
        "\n",
        "def letterbox_resize(img, new_width, new_height, interp=0):\n",
        "    ori_height, ori_width = img.shape[:2]\n",
        "\n",
        "    resize_ratio = min(new_width / ori_width, new_height / ori_height)\n",
        "\n",
        "    resize_w = int(resize_ratio * ori_width)\n",
        "    resize_h = int(resize_ratio * ori_height)\n",
        "\n",
        "    img = cv2.resize(img, (resize_w, resize_h), interpolation=interp)\n",
        "    image_padded = np.full((new_height, new_width, 3), 128, np.uint8)\n",
        "\n",
        "    dw = int((new_width - resize_w) / 2)\n",
        "    dh = int((new_height - resize_h) / 2)\n",
        "\n",
        "    image_padded[dh: resize_h + dh, dw: resize_w + dw, :] = img\n",
        "\n",
        "    return image_padded, resize_ratio, dw, dh\n",
        "\n",
        "\n",
        "def resize_with_bbox(img, bbox, new_width, new_height, interp=0, letterbox=False):\n",
        "    if letterbox:\n",
        "        image_padded, resize_ratio, dw, dh = letterbox_resize(img, new_width, new_height, interp)\n",
        "\n",
        "        # xmin, xmax\n",
        "        bbox[:, [0, 2]] = bbox[:, [0, 2]] * resize_ratio + dw\n",
        "        # ymin, ymax\n",
        "        bbox[:, [1, 3]] = bbox[:, [1, 3]] * resize_ratio + dh\n",
        "\n",
        "        return image_padded, bbox\n",
        "    else:\n",
        "        ori_height, ori_width = img.shape[:2]\n",
        "\n",
        "        img = cv2.resize(img, (new_width, new_height), interpolation=interp)\n",
        "\n",
        "        # xmin, xmax\n",
        "        bbox[:, [0, 2]] = bbox[:, [0, 2]] / ori_width * new_width\n",
        "        # ymin, ymax\n",
        "        bbox[:, [1, 3]] = bbox[:, [1, 3]] / ori_height * new_height\n",
        "\n",
        "        return img, bbox\n",
        "\n",
        "\n",
        "def random_flip(img, bbox, px=0, py=0):\n",
        "    height, width = img.shape[:2]\n",
        "    if np.random.uniform(0, 1) < px:\n",
        "        img = cv2.flip(img, 1)\n",
        "        xmax = width - bbox[:, 0]\n",
        "        xmin = width - bbox[:, 2]\n",
        "        bbox[:, 0] = xmin\n",
        "        bbox[:, 2] = xmax\n",
        "\n",
        "    if np.random.uniform(0, 1) < py:\n",
        "        img = cv2.flip(img, 0)\n",
        "        ymax = height - bbox[:, 1]\n",
        "        ymin = height - bbox[:, 3]\n",
        "        bbox[:, 1] = ymin\n",
        "        bbox[:, 3] = ymax\n",
        "    return img, bbox\n",
        "\n",
        "\n",
        "def random_expand(img, bbox, max_ratio=4, fill=0, keep_ratio=True):\n",
        "    h, w, c = img.shape\n",
        "    ratio_x = random.uniform(1, max_ratio)\n",
        "    if keep_ratio:\n",
        "        ratio_y = ratio_x\n",
        "    else:\n",
        "        ratio_y = random.uniform(1, max_ratio)\n",
        "\n",
        "    oh, ow = int(h * ratio_y), int(w * ratio_x)\n",
        "    off_y = random.randint(0, oh - h)\n",
        "    off_x = random.randint(0, ow - w)\n",
        "\n",
        "    dst = np.full(shape=(oh, ow, c), fill_value=fill, dtype=img.dtype)\n",
        "\n",
        "    dst[off_y:off_y + h, off_x:off_x + w, :] = img\n",
        "\n",
        "    # correct bbox\n",
        "    bbox[:, :2] += (off_x, off_y)\n",
        "    bbox[:, 2:4] += (off_x, off_y)\n",
        "\n",
        "    return dst, bbox\n",
        "\n",
        "def process_box(boxes, labels, img_size, class_num, anchors):\n",
        "    anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]\n",
        "\n",
        "    # convert boxes form:\n",
        "    # shape: [N, 2]\n",
        "    # (x_center, y_center)\n",
        "    box_centers = (boxes[:, 0:2] + boxes[:, 2:4]) / 2\n",
        "    # (width, height)\n",
        "    box_sizes = boxes[:, 2:4] - boxes[:, 0:2]\n",
        "\n",
        "    # [13, 13, 3, 5+num_class+1] `5` means coords and labels. `1` means mix up weight. \n",
        "    y_true_13 = np.zeros((img_size[1] // 32, img_size[0] // 32, 3, 6 + class_num), np.float32)\n",
        "    y_true_26 = np.zeros((img_size[1] // 16, img_size[0] // 16, 3, 6 + class_num), np.float32)\n",
        "    y_true_52 = np.zeros((img_size[1] // 8, img_size[0] // 8, 3, 6 + class_num), np.float32)\n",
        "\n",
        "    # mix up weight default to 1.\n",
        "    y_true_13[..., -1] = 1.\n",
        "    y_true_26[..., -1] = 1.\n",
        "    y_true_52[..., -1] = 1.\n",
        "\n",
        "    y_true = [y_true_13, y_true_26, y_true_52]\n",
        "\n",
        "    # [N, 1, 2]\n",
        "    box_sizes = np.expand_dims(box_sizes, 1)\n",
        "    # broadcast tricks\n",
        "    # [N, 1, 2] & [9, 2] ==> [N, 9, 2]\n",
        "    mins = np.maximum(- box_sizes / 2, - anchors / 2)\n",
        "    maxs = np.minimum(box_sizes / 2, anchors / 2)\n",
        "    # [N, 9, 2]\n",
        "    whs = maxs - mins\n",
        "\n",
        "    # [N, 9]\n",
        "    iou = (whs[:, :, 0] * whs[:, :, 1]) / (\n",
        "                box_sizes[:, :, 0] * box_sizes[:, :, 1] + anchors[:, 0] * anchors[:, 1] - whs[:, :, 0] * whs[:, :,\n",
        "                                                                                                         1] + 1e-10)\n",
        "    # [N]\n",
        "    best_match_idx = np.argmax(iou, axis=1)\n",
        "\n",
        "    ratio_dict = {1.: 8., 2.: 16., 3.: 32.}\n",
        "    for i, idx in enumerate(best_match_idx):\n",
        "        # idx: 0,1,2 ==> 2; 3,4,5 ==> 1; 6,7,8 ==> 0\n",
        "        feature_map_group = 2 - idx // 3\n",
        "        # scale ratio: 0,1,2 ==> 8; 3,4,5 ==> 16; 6,7,8 ==> 32\n",
        "        ratio = ratio_dict[np.ceil((idx + 1) / 3.)]\n",
        "        x = int(np.floor(box_centers[i, 0] / ratio))\n",
        "        y = int(np.floor(box_centers[i, 1] / ratio))\n",
        "        k = anchors_mask[feature_map_group].index(idx)\n",
        "        c = labels[i]\n",
        "        # print(feature_map_group, '|', y,x,k,c)\n",
        "\n",
        "        y_true[feature_map_group][y, x, k, :2] = box_centers[i]\n",
        "        y_true[feature_map_group][y, x, k, 2:4] = box_sizes[i]\n",
        "        y_true[feature_map_group][y, x, k, 4] = 1.\n",
        "        y_true[feature_map_group][y, x, k, 5 + c] = 1.\n",
        "        y_true[feature_map_group][y, x, k, -1] = boxes[i, -1]\n",
        "\n",
        "    return y_true_13, y_true_26, y_true_52\n",
        "\n",
        "\n",
        "def parse_data(data, class_num, img_size, anchors, is_training, letterbox_resize):\n",
        "    \n",
        "    img_idx, encoded_img, boxes, labels, _, _ = parse_tfrecord(data)\n",
        "    img = cv2.imdecode(encoded_img, cv2.IMREAD_COLOR)\n",
        "    boxes = np.concatenate((boxes, np.full(shape=(boxes.shape[0], 1), fill_value=1., dtype=np.float32)), axis=-1)\n",
        "\n",
        "    ## I erased mix-up method here\n",
        "\n",
        "    if is_training:\n",
        "        # random color distortion\n",
        "        img = random_color_distort(img)\n",
        "\n",
        "        # random expansion with prob 0.5\n",
        "        if np.random.uniform(0, 1) > 0.5:\n",
        "            img, boxes = random_expand(img, boxes, 4)\n",
        "\n",
        "        # random cropping\n",
        "        h, w, _ = img.shape\n",
        "        boxes, crop = random_crop_with_constraints(boxes, (w, h))\n",
        "        x0, y0, w, h = crop\n",
        "        img = img[y0: y0+h, x0: x0+w]\n",
        "\n",
        "        # resize with random interpolation\n",
        "        h, w, _ = img.shape\n",
        "        interp = np.random.randint(0, 5)\n",
        "        img, boxes = resize_with_bbox(img, boxes, img_size[0], img_size[1], interp=interp, letterbox=letterbox_resize)\n",
        "\n",
        "        # random horizontal flip\n",
        "        h, w, _ = img.shape\n",
        "        img, boxes = random_flip(img, boxes, px=0.5)\n",
        "    else:\n",
        "        img, boxes = resize_with_bbox(img, boxes, img_size[0], img_size[1], interp=1, letterbox=letterbox_resize)\n",
        "\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)\n",
        "\n",
        "    # the input of yolo_v3 should be in range 0~1\n",
        "    img = img / 255.\n",
        "\n",
        "    y_true_13, y_true_26, y_true_52 = process_box(boxes, labels, img_size, class_num, anchors)\n",
        "\n",
        "    return img_idx, img, y_true_13, y_true_26, y_true_52\n",
        "\n",
        "\n",
        "def get_batch_data(records, class_num, img_size, anchors, is_training, multi_scale=False, mix_up=False, letterbox_resize=True, interval=10):\n",
        "    global iter_cnt\n",
        "\n",
        "    # multi_scale training\n",
        "    if multi_scale and is_training:\n",
        "        random.seed(iter_cnt // interval)\n",
        "        random_img_size = [[x * 32, x * 32] for x in range(10, 20)]\n",
        "        img_size = random.sample(random_img_size, 1)[0]\n",
        "    iter_cnt += 1\n",
        "\n",
        "    img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch = [], [], [], [], []\n",
        "\n",
        "    # deleted mix up strategy\n",
        "    \n",
        "    for data in records:\n",
        "        img_idx, img, y_true_13, y_true_26, y_true_52 = parse_data(data, class_num, img_size, anchors, is_training, letterbox_resize)\n",
        "\n",
        "        img_idx_batch.append(img_idx)\n",
        "        img_batch.append(img)\n",
        "        y_true_13_batch.append(y_true_13)\n",
        "        y_true_26_batch.append(y_true_26)\n",
        "        y_true_52_batch.append(y_true_52)\n",
        "\n",
        "    img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch = np.asarray(img_idx_batch, np.int64), np.asarray(img_batch), np.asarray(y_true_13_batch), np.asarray(y_true_26_batch), np.asarray(y_true_52_batch)\n",
        "\n",
        "    return img_idx_batch, img_batch, y_true_13_batch, y_true_26_batch, y_true_52_batch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sd9Pk3XgDqxt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## evaluation utils\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "def calc_iou(pred_boxes, true_boxes):\n",
        "    pred_boxes = np.expand_dims(pred_boxes, -2)\n",
        "    true_boxes = np.expand_dims(true_boxes, 0)\n",
        "\n",
        "    intersect_mins = np.maximum(pred_boxes[..., :2], true_boxes[..., :2])\n",
        "    intersect_maxs = np.minimum(pred_boxes[..., 2:], true_boxes[..., 2:])\n",
        "    intersect_wh = np.maximum(intersect_maxs - intersect_mins, 0.)\n",
        "\n",
        "    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
        "    pred_box_wh = pred_boxes[..., 2:] - pred_boxes[..., :2]\n",
        "    pred_box_area = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
        "    true_boxes_wh = true_boxes[..., 2:] - true_boxes[..., :2]\n",
        "    true_boxes_area = true_boxes_wh[..., 0] * true_boxes_wh[..., 1]\n",
        "\n",
        "    iou = intersect_area / (pred_box_area + true_boxes_area - intersect_area + 1e-10)\n",
        "\n",
        "    return iou\n",
        "\n",
        "\n",
        "def evaluate_on_cpu(y_pred, y_true, num_classes, calc_now=True, max_boxes=50, score_thresh=0.5, iou_thresh=0.5):\n",
        "    num_images = y_true[0].shape[0]\n",
        "    true_labels_dict = {i: 0 for i in range(num_classes)}\n",
        "    pred_labels_dict = {i: 0 for i in range(num_classes)}\n",
        "    true_positive_dict = {i: 0 for i in range(num_classes)}\n",
        "\n",
        "    for i in range(num_images):\n",
        "        true_labels_list, true_boxes_list = [], []\n",
        "        for j in range(3):\n",
        "            true_probs_temp = y_true[j][i][..., 5:-1]\n",
        "            true_boxes_temp = y_true[j][i][..., 0:4]\n",
        "\n",
        "            object_mask = true_probs_temp.sum(axis=-1) > 0\n",
        "\n",
        "            true_probs_temp = true_probs_temp[object_mask]\n",
        "            true_boxes_temp = true_boxes_temp[object_mask]\n",
        "\n",
        "            true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()\n",
        "            true_boxes_list += true_boxes_temp.tolist()\n",
        "\n",
        "        if len(true_labels_list) != 0:\n",
        "            for cls, count in Counter(true_labels_list).items():\n",
        "                true_labels_dict[cls] += count\n",
        "\n",
        "        true_boxes = np.array(true_boxes_list)\n",
        "        box_centers, box_sizes = true_boxes[:, 0:2], true_boxes[:, 2:4]\n",
        "        true_boxes[:, 0:2] = box_centers - box_sizes / 2.\n",
        "        true_boxes[:, 2:4] = true_boxes[:, 0:2] + box_sizes\n",
        "\n",
        "        pred_boxes = y_pred[0][i:i + 1]\n",
        "        pred_confs = y_pred[1][i:i + 1]\n",
        "        pred_probs = y_pred[2][i:i + 1]\n",
        "\n",
        "        pred_boxes, pred_confs, pred_labels = cpu_nms(pred_boxes, pred_confs * pred_probs, num_classes, max_boxes=max_boxes, score_thresh=score_thresh, iou_thresh=iou_thresh)\n",
        "\n",
        "        pred_labels_list = [] if pred_labels is None else pred_labels.tolist()\n",
        "        if pred_labels_list == []:\n",
        "            continue\n",
        "\n",
        "        # calc iou\n",
        "        iou_matrix = calc_iou(pred_boxes, true_boxes)\n",
        "        max_iou_idx = np.argmax(iou_matrix, axis=-1)\n",
        "\n",
        "        correct_idx = []\n",
        "        correct_conf = []\n",
        "\n",
        "        for k in range(max_iou_idx.shape[0]):\n",
        "            pred_labels_dict[pred_labels_list[k]] += 1\n",
        "            match_idx = max_iou_idx[k]  # V level\n",
        "            if iou_matrix[k, match_idx] > iou_thresh and true_labels_list[match_idx] == pred_labels_list[k]:\n",
        "                if match_idx not in correct_idx:\n",
        "                    correct_idx.append(match_idx)\n",
        "                    correct_conf.append(pred_confs[k])\n",
        "                else:\n",
        "                    same_idx = correct_idx.index(match_idx)\n",
        "                    if pred_confs[k] > correct_conf[same_idx]:\n",
        "                        correct_idx.pop(same_idx)\n",
        "                        correct_conf.pop(same_idx)\n",
        "                        correct_idx.append(match_idx)\n",
        "                        correct_conf.append(pred_confs[k])\n",
        "\n",
        "        for t in correct_idx:\n",
        "            true_positive_dict[true_labels_list[t]] += 1\n",
        "\n",
        "    if calc_now:\n",
        "        # avoid divided by 0\n",
        "        recall = sum(true_positive_dict.values()) / (sum(true_labels_dict.values()) + 1e-6)\n",
        "        precision = sum(true_positive_dict.values()) / (sum(pred_labels_dict.values()) + 1e-6)\n",
        "\n",
        "        return recall, precision\n",
        "    else:\n",
        "        return true_positive_dict, true_labels_dict, pred_labels_dict\n",
        "\n",
        "\n",
        "def evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, y_pred, y_true, num_classes, iou_thresh=0.5, calc_now=True):\n",
        "    num_images = y_true[0].shape[0]\n",
        "    true_labels_dict = {i: 0 for i in range(num_classes)}\n",
        "    pred_labels_dict = {i: 0 for i in range(num_classes)}\n",
        "    true_positive_dict = {i: 0 for i in range(num_classes)}\n",
        "\n",
        "    for i in range(num_images):\n",
        "        true_labels_list, true_boxes_list = [], []\n",
        "        for j in range(3):\n",
        "            true_probs_temp = y_true[j][i][..., 5:-1]\n",
        "            true_boxes_temp = y_true[j][i][..., 0:4]\n",
        "\n",
        "            object_mask = true_probs_temp.sum(axis=-1) > 0\n",
        "\n",
        "            true_probs_temp = true_probs_temp[object_mask]\n",
        "            true_boxes_temp = true_boxes_temp[object_mask]\n",
        "\n",
        "            true_labels_list += np.argmax(true_probs_temp, axis=-1).tolist()\n",
        "            true_boxes_list += true_boxes_temp.tolist()\n",
        "\n",
        "        if len(true_labels_list) != 0:\n",
        "            for cls, count in Counter(true_labels_list).items():\n",
        "                true_labels_dict[cls] += count\n",
        "\n",
        "        true_boxes = np.array(true_boxes_list)\n",
        "        box_centers, box_sizes = true_boxes[:, 0:2], true_boxes[:, 2:4]\n",
        "        true_boxes[:, 0:2] = box_centers - box_sizes / 2.\n",
        "        true_boxes[:, 2:4] = true_boxes[:, 0:2] + box_sizes\n",
        "\n",
        "        pred_boxes = y_pred[0][i:i + 1]\n",
        "        pred_confs = y_pred[1][i:i + 1]\n",
        "        pred_probs = y_pred[2][i:i + 1]\n",
        "\n",
        "        pred_boxes, pred_confs, pred_labels = sess.run(gpu_nms_op, feed_dict={pred_boxes_flag: pred_boxes, pred_scores_flag: pred_confs * pred_probs})\n",
        "\n",
        "        pred_labels_list = [] if pred_labels is None else pred_labels.tolist()\n",
        "        if pred_labels_list == []:\n",
        "            continue\n",
        "\n",
        "        # calc iou\n",
        "        iou_matrix = calc_iou(pred_boxes, true_boxes)\n",
        "        max_iou_idx = np.argmax(iou_matrix, axis=-1)\n",
        "\n",
        "        correct_idx = []\n",
        "        correct_conf = []\n",
        "        for k in range(max_iou_idx.shape[0]):\n",
        "            pred_labels_dict[pred_labels_list[k]] += 1\n",
        "            match_idx = max_iou_idx[k]  # V level\n",
        "            if iou_matrix[k, match_idx] > iou_thresh and true_labels_list[match_idx] == pred_labels_list[k]:\n",
        "                if match_idx not in correct_idx:\n",
        "                    correct_idx.append(match_idx)\n",
        "                    correct_conf.append(pred_confs[k])\n",
        "                else:\n",
        "                    same_idx = correct_idx.index(match_idx)\n",
        "                    if pred_confs[k] > correct_conf[same_idx]:\n",
        "                        correct_idx.pop(same_idx)\n",
        "                        correct_conf.pop(same_idx)\n",
        "                        correct_idx.append(match_idx)\n",
        "                        correct_conf.append(pred_confs[k])\n",
        "\n",
        "        for t in correct_idx:\n",
        "            true_positive_dict[true_labels_list[t]] += 1\n",
        "\n",
        "    if calc_now:\n",
        "        # avoid divided by 0\n",
        "        recall = sum(true_positive_dict.values()) / (sum(true_labels_dict.values()) + 1e-6)\n",
        "        precision = sum(true_positive_dict.values()) / (sum(pred_labels_dict.values()) + 1e-6)\n",
        "\n",
        "        return recall, precision\n",
        "    else:\n",
        "        return true_positive_dict, true_labels_dict, pred_labels_dict\n",
        "\n",
        "\n",
        "def get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, image_ids, y_pred):\n",
        "    image_id = image_ids[0]\n",
        "\n",
        "    pred_boxes = y_pred[0][0:1]\n",
        "    pred_confs = y_pred[1][0:1]\n",
        "    pred_probs = y_pred[2][0:1]\n",
        "\n",
        "    boxes, scores, labels = sess.run(gpu_nms_op, feed_dict={pred_boxes_flag: pred_boxes, pred_scores_flag: pred_confs * pred_probs})\n",
        "\n",
        "    pred_content = []\n",
        "    for i in range(len(labels)):\n",
        "        x_min, y_min, x_max, y_max = boxes[i]\n",
        "        score = scores[i]\n",
        "        label = labels[i]\n",
        "        pred_content.append([image_id, x_min, y_min, x_max, y_max, score, label])\n",
        "\n",
        "    return pred_content\n",
        "\n",
        "gt_dict = {}  # key: img_id, value: gt object list\n",
        "def parse_gt_rec(gt_filename, compression_type, target_img_size, letterbox_resize=True):\n",
        "    global gt_dict\n",
        "\n",
        "    if not gt_dict:\n",
        "        new_width, new_height = target_img_size\n",
        "\n",
        "        with TFRecordIterator(gt_filename, compression_type) as reader:\n",
        "            for data in reader:\n",
        "                img_id, image, boxes, labels, ori_width, ori_height = parse_record(data)\n",
        "\n",
        "                objects = []\n",
        "                for i in range(len(labels)):\n",
        "                    x_min, y_min, x_max, y_max = boxes[i]\n",
        "                    label = labels[i]\n",
        "\n",
        "                    if letterbox_resize:\n",
        "                        resize_ratio = min(new_width / ori_width, new_height / ori_height)\n",
        "\n",
        "                        resize_w = int(resize_ratio * ori_width)\n",
        "                        resize_h = int(resize_ratio * ori_height)\n",
        "\n",
        "                        dw = int((new_width - resize_w) / 2)\n",
        "                        dh = int((new_height - resize_h) / 2)\n",
        "\n",
        "                        objects.append([x_min * resize_ratio + dw,\n",
        "                                        y_min * resize_ratio + dh,\n",
        "                                        x_max * resize_ratio + dw,\n",
        "                                        y_max * resize_ratio + dh,\n",
        "                                        label])\n",
        "                    else:\n",
        "                        objects.append([x_min * new_width / ori_width,\n",
        "                                        y_min * new_height / ori_height,\n",
        "                                        x_max * new_width / ori_width,\n",
        "                                        y_max * new_height / ori_height,\n",
        "                                        label])\n",
        "                gt_dict[img_id] = objects\n",
        "    return gt_dict\n",
        "\n",
        "\n",
        "# The following two functions are modified from FAIR's Detectron repo to calculate mAP:\n",
        "# https://github.com/facebookresearch/Detectron/blob/master/detectron/datasets/voc_eval.py\n",
        "def voc_ap(rec, prec, use_07_metric=False):\n",
        "    if use_07_metric:\n",
        "        ap = 0.\n",
        "        for t in np.arange(0., 1.1, 0.1):\n",
        "            if np.sum(rec >= t) == 0:\n",
        "                p = 0\n",
        "            else:\n",
        "                p = np.max(prec[rec >= t])\n",
        "            ap = ap + p / 11.\n",
        "    else:\n",
        "        mrec = np.concatenate(([0.], rec, [1.]))\n",
        "        mpre = np.concatenate(([0.], prec, [0.]))\n",
        "\n",
        "        for i in range(mpre.size - 1, 0, -1):\n",
        "            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n",
        "\n",
        "        i = np.where(mrec[1:] != mrec[:-1])[0]\n",
        "\n",
        "        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n",
        "    return ap\n",
        "\n",
        "\n",
        "def voc_eval(gt_dict, val_preds, classidx, iou_thres=0.5, use_07_metric=False):\n",
        "    # 1.obtain gt: extract all gt objects for this class\n",
        "    class_recs = {}\n",
        "    npos = 0\n",
        "    for img_id in gt_dict:\n",
        "        R = [obj for obj in gt_dict[img_id] if obj[-1] == classidx]\n",
        "        bbox = np.array([x[:4] for x in R])\n",
        "        det = [False] * len(R)\n",
        "        npos += len(R)\n",
        "        class_recs[img_id] = {'bbox': bbox, 'det': det}\n",
        "\n",
        "    # 2. obtain pred results\n",
        "    pred = [x for x in val_preds if x[-1] == classidx]\n",
        "    img_ids = [x[0] for x in pred]\n",
        "    confidence = np.array([x[-2] for x in pred])\n",
        "    BB = np.array([[x[1], x[2], x[3], x[4]] for x in pred])\n",
        "\n",
        "    # 3. sort by confidence\n",
        "    sorted_ind = np.argsort(-confidence)\n",
        "    try:\n",
        "        BB = BB[sorted_ind, :]\n",
        "    except:\n",
        "        print('no box, ignore')\n",
        "        return 1e-6, 1e-6, 0, 0, 0\n",
        "    img_ids = [img_ids[x] for x in sorted_ind]\n",
        "\n",
        "    # 4. mark TPs and FPs\n",
        "    nd = len(img_ids)\n",
        "    tp = np.zeros(nd)\n",
        "    fp = np.zeros(nd)\n",
        "\n",
        "    for d in range(nd):\n",
        "        R = class_recs[img_ids[d]]\n",
        "        bb = BB[d, :]\n",
        "        ovmax = -np.Inf\n",
        "        BBGT = R['bbox']\n",
        "\n",
        "        if BBGT.size > 0:\n",
        "            ixmin = np.maximum(BBGT[:, 0], bb[0])\n",
        "            iymin = np.maximum(BBGT[:, 1], bb[1])\n",
        "            ixmax = np.minimum(BBGT[:, 2], bb[2])\n",
        "            iymax = np.minimum(BBGT[:, 3], bb[3])\n",
        "            iw = np.maximum(ixmax - ixmin + 1., 0.)\n",
        "            ih = np.maximum(iymax - iymin + 1., 0.)\n",
        "            inters = iw * ih\n",
        "\n",
        "            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + (BBGT[:, 2] - BBGT[:, 0] + 1.) * (\n",
        "                        BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\n",
        "\n",
        "            overlaps = inters / uni\n",
        "            ovmax = np.max(overlaps)\n",
        "            jmax = np.argmax(overlaps)\n",
        "\n",
        "        if ovmax > iou_thres:\n",
        "            # gt not matched yet\n",
        "            if not R['det'][jmax]:\n",
        "                tp[d] = 1.\n",
        "                R['det'][jmax] = 1\n",
        "            else:\n",
        "                fp[d] = 1.\n",
        "        else:\n",
        "            fp[d] = 1.\n",
        "\n",
        "    fp = np.cumsum(fp)\n",
        "    tp = np.cumsum(tp)\n",
        "    rec = tp / float(npos)\n",
        "    # avoid divide by zero in case the first detection matches a difficult\n",
        "    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)\n",
        "    ap = voc_ap(rec, prec, use_07_metric)\n",
        "\n",
        "    # return rec, prec, ap\n",
        "    return npos, nd, tp[-1] / float(npos), tp[-1] / float(nd), ap"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4uQxNl0FRli",
        "colab_type": "code",
        "outputId": "ac68765b-313c-4810-8565-901ab71ef470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "## model\n",
        "\n",
        "slim = tf.contrib.slim\n",
        "\n",
        "def conv2d(inputs, filters, kernel_size, strides=1):\n",
        "    def _fixed_padding(inputs, kernel_size):\n",
        "        pad_total = kernel_size - 1\n",
        "        pad_beg = pad_total // 2\n",
        "        pad_end = pad_total - pad_beg\n",
        "\n",
        "        padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],\n",
        "                                        [pad_beg, pad_end], [0, 0]], mode='CONSTANT')\n",
        "        return padded_inputs\n",
        "    if strides > 1: \n",
        "        inputs = _fixed_padding(inputs, kernel_size)\n",
        "    inputs = slim.conv2d(inputs, filters, kernel_size, stride=strides,\n",
        "                         padding=('SAME' if strides == 1 else 'VALID'))\n",
        "    return inputs\n",
        "\n",
        "def darknet53_body(inputs):\n",
        "    def res_block(inputs, filters):\n",
        "        shortcut = inputs\n",
        "        net = conv2d(inputs, filters * 1, 1)\n",
        "        net = conv2d(net, filters * 2, 3)\n",
        "\n",
        "        net = net + shortcut\n",
        "\n",
        "        return net\n",
        "    \n",
        "    # first two conv2d layers\n",
        "    net = conv2d(inputs, 32,  3, strides=1)\n",
        "    net = conv2d(net, 64,  3, strides=2)\n",
        "\n",
        "    # res_block * 1\n",
        "    net = res_block(net, 32)\n",
        "\n",
        "    net = conv2d(net, 128, 3, strides=2)\n",
        "\n",
        "    # res_block * 2\n",
        "    for i in range(2):\n",
        "        net = res_block(net, 64)\n",
        "\n",
        "    net = conv2d(net, 256, 3, strides=2)\n",
        "\n",
        "    # res_block * 8\n",
        "    for i in range(8):\n",
        "        net = res_block(net, 128)\n",
        "\n",
        "    route_1 = net\n",
        "    net = conv2d(net, 512, 3, strides=2)\n",
        "\n",
        "    # res_block * 8\n",
        "    for i in range(8):\n",
        "        net = res_block(net, 256)\n",
        "\n",
        "    route_2 = net\n",
        "    net = conv2d(net, 1024, 3, strides=2)\n",
        "\n",
        "    # res_block * 4\n",
        "    for i in range(4):\n",
        "        net = res_block(net, 512)\n",
        "    route_3 = net\n",
        "\n",
        "    return route_1, route_2, route_3\n",
        "\n",
        "\n",
        "def yolo_block(inputs, filters):\n",
        "    net = conv2d(inputs, filters * 1, 1)\n",
        "    net = conv2d(net, filters * 2, 3)\n",
        "    net = conv2d(net, filters * 1, 1)\n",
        "    net = conv2d(net, filters * 2, 3)\n",
        "    net = conv2d(net, filters * 1, 1)\n",
        "    route = net\n",
        "    net = conv2d(net, filters * 2, 3)\n",
        "    return route, net\n",
        "\n",
        "\n",
        "def upsample_layer(inputs, out_shape):\n",
        "    new_height, new_width = out_shape[1], out_shape[2]\n",
        "    # NOTE: here height is the first\n",
        "    inputs = tf.image.resize_nearest_neighbor(inputs, (new_height, new_width), name='upsampled')\n",
        "    return inputs\n",
        "\n",
        "class yolov3(object):\n",
        "\n",
        "    def __init__(self, class_num, anchors, use_label_smooth=False, use_focal_loss=False, batch_norm_decay=0.999, weight_decay=5e-4, use_static_shape=True):\n",
        "        self.class_num = class_num\n",
        "        self.anchors = anchors\n",
        "        self.batch_norm_decay = batch_norm_decay\n",
        "        self.use_label_smooth = use_label_smooth\n",
        "        self.use_focal_loss = use_focal_loss\n",
        "        self.weight_decay = weight_decay\n",
        "        self.use_static_shape = use_static_shape\n",
        "\n",
        "    def forward(self, inputs, is_training=False, reuse=False):\n",
        "        # the input size: [height, weight] format\n",
        "        self.img_size = tf.shape(inputs)[1:3]\n",
        "        print(\"Img size:\", self.img_size)\n",
        "\t\t\n",
        "        batch_norm_params = {\n",
        "            'decay': self.batch_norm_decay,\n",
        "            'epsilon': 1e-05,\n",
        "            'scale': True,\n",
        "            'is_training': is_training,\n",
        "            'fused': None,\n",
        "        }\n",
        "\n",
        "        with slim.arg_scope([slim.conv2d, slim.batch_norm], reuse=reuse):\n",
        "            with slim.arg_scope([slim.conv2d], \n",
        "                                normalizer_fn=slim.batch_norm,\n",
        "                                normalizer_params=batch_norm_params,\n",
        "                                biases_initializer=None,\n",
        "                                activation_fn=lambda x: tf.nn.leaky_relu(x, alpha=0.1),\n",
        "                                weights_regularizer=slim.l2_regularizer(self.weight_decay)):\n",
        "\n",
        "                with tf.variable_scope('darknet53_body'):\n",
        "                    route_1, route_2, route_3 = darknet53_body(inputs)\n",
        "\n",
        "                with tf.variable_scope('yolov3_head'):\n",
        "                    inter1, net = yolo_block(route_3, 512)\n",
        "                    feature_map_1 = slim.conv2d(net, 3 * (5 + self.class_num), 1,\n",
        "                                                stride=1, normalizer_fn=None,\n",
        "                                                activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
        "                    feature_map_1 = tf.identity(feature_map_1, name='feature_map_1')\n",
        "\n",
        "                    inter1 = conv2d(inter1, 256, 1)\n",
        "                    inter1 = upsample_layer(inter1, route_2.get_shape().as_list() if self.use_static_shape else tf.shape(route_2))\n",
        "                    concat1 = tf.concat([inter1, route_2], axis=3)\n",
        "\n",
        "                    inter2, net = yolo_block(concat1, 256)\n",
        "                    feature_map_2 = slim.conv2d(net, 3 * (5 + self.class_num), 1,\n",
        "                                                stride=1, normalizer_fn=None,\n",
        "                                                activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
        "                    feature_map_2 = tf.identity(feature_map_2, name='feature_map_2')\n",
        "\n",
        "                    inter2 = conv2d(inter2, 128, 1)\n",
        "                    inter2 = upsample_layer(inter2, route_1.get_shape().as_list() if self.use_static_shape else tf.shape(route_1))\n",
        "                    concat2 = tf.concat([inter2, route_1], axis=3)\n",
        "\n",
        "                    _, feature_map_3 = yolo_block(concat2, 128)\n",
        "                    feature_map_3 = slim.conv2d(feature_map_3, 3 * (5 + self.class_num), 1,\n",
        "                                                stride=1, normalizer_fn=None,\n",
        "                                                activation_fn=None, biases_initializer=tf.zeros_initializer())\n",
        "                    feature_map_3 = tf.identity(feature_map_3, name='feature_map_3')\n",
        "\n",
        "            return feature_map_1, feature_map_2, feature_map_3\n",
        "\n",
        "    def reorg_layer(self, feature_map, anchors):\t\n",
        "        # size : [h, w] format\n",
        "        grid_size = feature_map.get_shape().as_list()[1:3] if self.use_static_shape else tf.shape(feature_map)[1:3]  # [13, 13]\n",
        "        ratio = tf.cast(self.img_size / grid_size, tf.float32)\n",
        "\t\t\n",
        "        # anchor : [w, h] format\n",
        "        rescaled_anchors = [(anchor[0] / ratio[1], anchor[1] / ratio[0]) for anchor in anchors]\n",
        "\n",
        "        feature_map = tf.reshape(feature_map, [-1, grid_size[0], grid_size[1], 3, 5 + self.class_num])\n",
        "\t\t\n",
        "        box_centers, box_sizes, conf_logits, prob_logits = tf.split(feature_map, [2, 2, 1, self.class_num], axis=-1)\n",
        "        box_centers = tf.nn.sigmoid(box_centers)\n",
        "\n",
        "        grid_x = tf.range(grid_size[1], dtype=tf.int32)\n",
        "        grid_y = tf.range(grid_size[0], dtype=tf.int32)\n",
        "        grid_x, grid_y = tf.meshgrid(grid_x, grid_y)\n",
        "        x_offset = tf.reshape(grid_x, (-1, 1))\n",
        "        y_offset = tf.reshape(grid_y, (-1, 1))\n",
        "        x_y_offset = tf.concat([x_offset, y_offset], axis=-1)\n",
        "\t\t\n",
        "        x_y_offset = tf.cast(tf.reshape(x_y_offset, [grid_size[0], grid_size[1], 1, 2]), tf.float32)\n",
        "\n",
        "        box_centers = box_centers + x_y_offset\n",
        "        box_centers = box_centers * ratio[::-1]\n",
        "\n",
        "        box_sizes = tf.exp(box_sizes) * rescaled_anchors\n",
        "        box_sizes = box_sizes * ratio[::-1]\n",
        "\n",
        "        boxes = tf.concat([box_centers, box_sizes], axis=-1)\n",
        "\n",
        "        return x_y_offset, boxes, conf_logits, prob_logits\n",
        "    \n",
        "    def predict(self, feature_maps):\n",
        "        feature_map_1, feature_map_2, feature_map_3 = feature_maps\n",
        "\n",
        "        feature_map_anchors = [(feature_map_1, self.anchors[6:9]),\n",
        "                               (feature_map_2, self.anchors[3:6]),\n",
        "                               (feature_map_3, self.anchors[0:3])]\n",
        "        reorg_results = [self.reorg_layer(feature_map, anchors) for (feature_map, anchors) in feature_map_anchors]\n",
        "\n",
        "        def _reshape_logit(result):\n",
        "            x_y_offset, boxes, conf_logits, prob_logits = result\n",
        "            grid_size = x_y_offset.get_shape().as_list()[:2] if self.use_static_shape else tf.shape(x_y_offset)[:2]\n",
        "            boxes = tf.reshape(boxes, [-1, grid_size[0] * grid_size[1] * 3, 4])\n",
        "            conf_logits = tf.reshape(conf_logits, [-1, grid_size[0] * grid_size[1] * 3, 1])\n",
        "            prob_logits = tf.reshape(prob_logits, [-1, grid_size[0] * grid_size[1] * 3, self.class_num])\n",
        "            return boxes, conf_logits, prob_logits\n",
        "\n",
        "        boxes_list, confs_list, probs_list = [], [], []\n",
        "\t\t\n",
        "        for result in reorg_results:\n",
        "            boxes, conf_logits, prob_logits = _reshape_logit(result)\n",
        "            confs = tf.sigmoid(conf_logits)\n",
        "            probs = tf.sigmoid(prob_logits)\n",
        "            boxes_list.append(boxes)\n",
        "            confs_list.append(confs)\n",
        "            probs_list.append(probs)\n",
        "        \n",
        "        boxes = tf.concat(boxes_list, axis=1)\n",
        "        confs = tf.concat(confs_list, axis=1)\n",
        "        probs = tf.concat(probs_list, axis=1)\n",
        "\n",
        "        center_x, center_y, width, height = tf.split(boxes, [1, 1, 1, 1], axis=-1)\n",
        "        x_min = center_x - width / 2\n",
        "        y_min = center_y - height / 2\n",
        "        x_max = center_x + width / 2\n",
        "        y_max = center_y + height / 2\n",
        "\n",
        "        boxes = tf.concat([x_min, y_min, x_max, y_max], axis=-1)\n",
        "\n",
        "        return boxes, confs, probs\n",
        "    \n",
        "    def loss_layer(self, feature_map_i, y_true, anchors):\n",
        "        grid_size = tf.shape(feature_map_i)[1:3]\n",
        "        ratio = tf.cast(self.img_size / grid_size, tf.float32)\n",
        "        # N: batch_size\n",
        "        N = tf.cast(tf.shape(feature_map_i)[0], tf.float32)\n",
        "\n",
        "        x_y_offset, pred_boxes, pred_conf_logits, pred_prob_logits = self.reorg_layer(feature_map_i, anchors)\n",
        "\n",
        "\t\t### mask\n",
        "        object_mask = y_true[..., 4:5]\n",
        "        ignore_mask = tf.TensorArray(tf.float32, size=0, dynamic_size=True)\n",
        "\t\t\n",
        "        def loop_cond(idx, ignore_mask):\n",
        "            return tf.less(idx, tf.cast(N, tf.int32))\n",
        "\t\t\t\n",
        "        def loop_body(idx, ignore_mask):\n",
        "            valid_true_boxes = tf.boolean_mask(y_true[idx, ..., 0:4], tf.cast(object_mask[idx, ..., 0], 'bool'))\n",
        "\t\t\t\n",
        "            iou = self.box_iou(pred_boxes[idx], valid_true_boxes)\t\t\t\n",
        "            best_iou = tf.reduce_max(iou, axis=-1)\n",
        "\t\t\t\n",
        "            ignore_mask_tmp = tf.cast(best_iou < 0.5, tf.float32)\n",
        "\t\t\t\n",
        "            ignore_mask = ignore_mask.write(idx, ignore_mask_tmp)\n",
        "            return idx + 1, ignore_mask\n",
        "\t\t\t\n",
        "        _, ignore_mask = tf.while_loop(cond=loop_cond, body=loop_body, loop_vars=[0, ignore_mask])\n",
        "        ignore_mask = ignore_mask.stack()\n",
        "        ignore_mask = tf.expand_dims(ignore_mask, -1)\n",
        "\n",
        "        pred_box_xy = pred_boxes[..., 0:2]\n",
        "        pred_box_wh = pred_boxes[..., 2:4]\n",
        "\n",
        "        true_xy = y_true[..., 0:2] / ratio[::-1] - x_y_offset\n",
        "        pred_xy = pred_box_xy / ratio[::-1] - x_y_offset\n",
        "\n",
        "        true_tw_th = y_true[..., 2:4] / anchors\n",
        "        pred_tw_th = pred_box_wh / anchors\n",
        "\t\t\n",
        "        true_tw_th = tf.where(condition=tf.equal(true_tw_th, 0),\n",
        "                              x=tf.ones_like(true_tw_th), y=true_tw_th)\n",
        "        pred_tw_th = tf.where(condition=tf.equal(pred_tw_th, 0),\n",
        "                              x=tf.ones_like(pred_tw_th), y=pred_tw_th)\n",
        "        true_tw_th = tf.log(tf.clip_by_value(true_tw_th, 1e-9, 1e9))\n",
        "        pred_tw_th = tf.log(tf.clip_by_value(pred_tw_th, 1e-9, 1e9))\n",
        "\n",
        "        box_loss_scale = 2. - (y_true[..., 2:3] / tf.cast(self.img_size[1], tf.float32)) * (y_true[..., 3:4] / tf.cast(self.img_size[0], tf.float32))\n",
        "\n",
        "        ### loss\n",
        "\t\t\n",
        "        mix_w = y_true[..., -1:]\n",
        "\t\t\n",
        "        xy_loss = tf.reduce_sum(tf.square(true_xy - pred_xy) * object_mask * box_loss_scale * mix_w) / N\n",
        "        wh_loss = tf.reduce_sum(tf.square(true_tw_th - pred_tw_th) * object_mask * box_loss_scale * mix_w) / N\n",
        "\n",
        "        conf_pos_mask = object_mask\n",
        "        conf_neg_mask = (1 - object_mask) * ignore_mask\n",
        "        conf_loss_pos = conf_pos_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)\n",
        "        conf_loss_neg = conf_neg_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=object_mask, logits=pred_conf_logits)\n",
        "\t\t\n",
        "        conf_loss = conf_loss_pos + conf_loss_neg\n",
        "\n",
        "        if self.use_focal_loss:\n",
        "            alpha = 1.0\n",
        "            gamma = 2.0\n",
        "            focal_mask = alpha * tf.pow(tf.abs(object_mask - tf.sigmoid(pred_conf_logits)), gamma)\n",
        "            conf_loss *= focal_mask\n",
        "        conf_loss = tf.reduce_sum(conf_loss * mix_w) / N\n",
        "\n",
        "        if self.use_label_smooth:\n",
        "            delta = 0.01\n",
        "            label_target = (1 - delta) * y_true[..., 5:-1] + delta * 1. / self.class_num\n",
        "        else:\n",
        "            label_target = y_true[..., 5:-1]\n",
        "\t\t\t\n",
        "        class_loss = object_mask * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_target, logits=pred_prob_logits) * mix_w\n",
        "        class_loss = tf.reduce_sum(class_loss) / N\n",
        "\n",
        "        return xy_loss, wh_loss, conf_loss, class_loss\n",
        "    \n",
        "\n",
        "    def box_iou(self, pred_boxes, valid_true_boxes):\n",
        "        pred_box_xy = pred_boxes[..., 0:2]\n",
        "        pred_box_wh = pred_boxes[..., 2:4]\n",
        "\n",
        "        pred_box_xy = tf.expand_dims(pred_box_xy, -2)\n",
        "        pred_box_wh = tf.expand_dims(pred_box_wh, -2)\n",
        "\n",
        "        true_box_xy = valid_true_boxes[:, 0:2]\n",
        "        true_box_wh = valid_true_boxes[:, 2:4]\n",
        "\n",
        "        intersect_mins = tf.maximum(pred_box_xy - pred_box_wh / 2.,\n",
        "                                    true_box_xy - true_box_wh / 2.)\n",
        "        intersect_maxs = tf.minimum(pred_box_xy + pred_box_wh / 2.,\n",
        "                                    true_box_xy + true_box_wh / 2.)\n",
        "        intersect_wh = tf.maximum(intersect_maxs - intersect_mins, 0.)\n",
        "\n",
        "        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
        "        pred_box_area = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
        "        true_box_area = true_box_wh[..., 0] * true_box_wh[..., 1]\n",
        "        true_box_area = tf.expand_dims(true_box_area, axis=0)\n",
        "\n",
        "        iou = intersect_area / (pred_box_area + true_box_area - intersect_area + 1e-10)\n",
        "\n",
        "        return iou\n",
        "\n",
        "    \n",
        "    def compute_loss(self, y_pred, y_true):\n",
        "        loss_xy, loss_wh, loss_conf, loss_class = 0., 0., 0., 0.\n",
        "        anchor_group = [self.anchors[6:9], self.anchors[3:6], self.anchors[0:3]]\n",
        "\n",
        "        for i in range(len(y_pred)):\n",
        "            result = self.loss_layer(y_pred[i], y_true[i], anchor_group[i])\n",
        "            loss_xy += result[0]\n",
        "            loss_wh += result[1]\n",
        "            loss_conf += result[2]\n",
        "            loss_class += result[3]\n",
        "        total_loss = loss_xy + loss_wh + loss_conf + loss_class\n",
        "        return [total_loss, loss_xy, loss_wh, loss_conf, loss_class]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nlddq-K7AJin",
        "colab_type": "code",
        "outputId": "cfb2f6e5-c6f3-4d27-8a3c-f6037571ea7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "## arguments\n",
        "\n",
        "import math\n",
        "\n",
        "\n",
        "### Some paths\n",
        "\n",
        "data_path = '/content/gdrive/My Drive/yolo/data/'\n",
        "train_file = data_path + 'train.tfrecord'  # The path of the training txt file.\n",
        "val_file = data_path + 'val.tfrecord'  # The path of the validation txt file.\n",
        "restore_path = '/content/gdrive/My Drive/yolo/weights/yolov3.ckpt'  # The path of the weights to restore (pretrained weights).\n",
        "save_dir = '/content/gdrive/My Drive/yolo/checkpoint/'  # The directory of the weights to save.\n",
        "\n",
        "### we are not using tensorboard logs in this code\n",
        "\n",
        "log_dir = data_path + 'logs/'  # The directory to store the tensorboard log files.\n",
        "progress_log_path = data_path + 'progress.log'  # The path to record the training progress.\n",
        "\n",
        "anchor_path = data_path + 'yolo_anchors.txt'  # The path of the anchor txt file.\n",
        "class_name_path = data_path + 'classes.txt'  # The path of the class names.\n",
        "\n",
        "### Training releated numbers\n",
        "batch_size = 4\n",
        "img_size = [416, 416]  # Images will be resized to `img_size` and fed to the network, size format: [width, height]\n",
        "letterbox_resizing = True  # Whether to use the letterbox resize, i.e., keep the original aspect ratio in the resized image.\n",
        "total_epoches = 10\n",
        "train_evaluation_step = 10  # Evaluate on the training batch after some steps.\n",
        "val_evaluation_epoch = 2  # Evaluate on the whole validation dataset after some epochs. Set to None to evaluate every epoch.\n",
        "save_epoch = 5  # Save the model after some epochs.\n",
        "batch_norm_decay = 0.99  # decay in bn ops\n",
        "weight_decay = 5e-4  # l2 weight decay\n",
        "current_global_step = 0  # used when resuming training\n",
        "\n",
        "### tf.data parameters\n",
        "num_threads = 10  # Number of threads for image processing used in tf.data pipeline.\n",
        "prefetech_buffer = 5  # Prefetech_buffer used in tf.data pipeline.\n",
        "\n",
        "### Learning rate and optimizer\n",
        "optimizer_name = 'momentum'  # Chosen from [sgd, momentum, adam, rmsprop]\n",
        "save_optimizer = True  # Whether to save the optimizer parameters into the checkpoint file.\n",
        "learning_rate_init = 1e-4\n",
        "lr_type = 'exponential'  # Chosen from [fixed, exponential, cosine_decay, cosine_decay_restart, piecewise]\n",
        "lr_decay_epoch = 5  # Epochs after which learning rate decays. Int or float. Used when chosen `exponential` and `cosine_decay_restart` lr_type.\n",
        "lr_decay_factor = 1.3  # The learning rate decay factor. Used when chosen `exponential` lr_type.\n",
        "lr_lower_bound = 1e-6  # The minimum learning rate.\n",
        "# only used in piecewise lr type\n",
        "pw_boundaries = [30, 50]  # epoch based boundaries\n",
        "pw_values = [learning_rate_init, 3e-5, 1e-5]\n",
        "\n",
        "### Load and finetune\n",
        "# Choose the parts you want to restore the weights. List form.\n",
        "# restore_include: None, restore_exclude: None  => restore the whole model\n",
        "# restore_include: None, restore_exclude: scope  => restore the whole model except `scope`\n",
        "# restore_include: scope1, restore_exclude: scope2  => if scope1 contains scope2, restore scope1 and not restore scope2 (scope1 - scope2)\n",
        "# choise 1: only restore the darknet body\n",
        "# restore_include = ['yolov3/darknet53_body']\n",
        "# restore_exclude = None\n",
        "# choise 2: restore all layers except the last 3 conv2d layers in 3 scale\n",
        "restore_include = None\n",
        "restore_exclude = ['yolov3/yolov3_head/Conv_14', 'yolov3/yolov3_head/Conv_6', 'yolov3/yolov3_head/Conv_22']\n",
        "# Choose the parts you want to finetune. List form.\n",
        "# Set to None to train the whole model.\n",
        "\n",
        "update_part = None\n",
        "\n",
        "### other training strategies\n",
        "multi_scale_train = True  # Whether to apply multi-scale training strategy. Image size varies from [320, 320] to [640, 640] by default.\n",
        "use_label_smooth = True # Whether to use class label smoothing strategy.\n",
        "use_focal_loss = True  # Whether to apply focal loss on the conf loss.\n",
        "use_mix_up = False  # Whether to use mix up data augmentation strategy. \n",
        "use_warm_up = True  # whether to use warm up strategy to prevent from gradient exploding.\n",
        "warm_up_epoch = 2  # Warm up training epoches. Set to a larger value if gradient explodes.\n",
        "\n",
        "### some constants in validation\n",
        "# nms\n",
        "nms_threshold = 0.45  # iou threshold in nms operation\n",
        "score_threshold = 0.01  # threshold of the probability of the classes in nms operation, i.e. score = pred_confs * pred_probs. set lower for higher recall.\n",
        "nms_topk = 150  # keep at most nms_topk outputs after nms\n",
        "# mAP eval\n",
        "eval_threshold = 0.5  # the iou threshold applied in mAP evaluation\n",
        "use_voc_07_metric = False  # whether to use voc 2007 evaluation metric, i.e. the 11-point metric\n",
        "\n",
        "### parse some params\n",
        "anchors = parse_anchors(anchor_path)\n",
        "classes = read_class_names(class_name_path)\n",
        "class_num = len(classes)\n",
        "train_img_cnt = TFRecordIterator(train_file, 'GZIP').count()\n",
        "val_img_cnt = TFRecordIterator(val_file, 'GZIP').count()\n",
        "train_batch_num = int(math.ceil(float(train_img_cnt) / batch_size))\n",
        "\n",
        "lr_decay_freq = int(train_batch_num * lr_decay_epoch)\n",
        "pw_boundaries = [float(i) * train_batch_num + current_global_step for i in pw_boundaries]\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-ea7f0591b13c>:7: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use eager execution and: \n",
            "`tf.data.TFRecordDataset(path)`\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWqx8g6I7fcv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## convert pretrained weights\n",
        "\n",
        "import os\n",
        "\n",
        "def save_pretrained_weights():\n",
        "  img_size = 416\n",
        "  weight_path = '/content/gdrive/My Drive/yolo/weights/yolov3.weights'\n",
        "  save_path = '/content/gdrive/My Drive/yolo/weights/yolov3.ckpt'\n",
        "  anchors = parse_anchors('/content/gdrive/My Drive/yolo/data/yolo_anchors.txt')\n",
        "\n",
        "  model = yolov3(80, anchors)\n",
        "  with tf.Session() as sess:\n",
        "      inputs = tf.placeholder(tf.float32, [1, img_size, img_size, 3])\n",
        "\n",
        "      with tf.variable_scope('yolov3'):\n",
        "          feature_map = model.forward(inputs)\n",
        "\n",
        "      saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))\n",
        "\n",
        "      load_ops = load_weights(tf.global_variables(scope='yolov3'), weight_path)\n",
        "      sess.run(load_ops)\n",
        "      saver.save(sess, save_path=save_path)\n",
        "      print('TensorFlow model checkpoint has been saved to {}'.format(save_path))\n",
        "\n",
        "\n",
        "if not os.path.exists(restore_path+'.meta'):\n",
        "  save_pretrained_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NagT2oNZFf0q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## train\n",
        "\n",
        "from tqdm import trange\n",
        "\n",
        "if trainingMode == 1:\n",
        "  is_training = tf.placeholder(tf.bool, name=\"phase_train\")\n",
        "  handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')\n",
        "\n",
        "  pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])\n",
        "  pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])\n",
        "  gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, class_num, nms_topk, score_threshold, nms_threshold)\n",
        "\n",
        "  ### tf.data pipeline\n",
        "  train_dataset = tf.data.TFRecordDataset(filenames=train_file, compression_type='GZIP')\n",
        "  train_dataset = train_dataset.shuffle(train_img_cnt)\n",
        "  train_dataset = train_dataset.batch(batch_size)\n",
        "  train_dataset = train_dataset.map(\n",
        "      lambda x: tf.py_func(get_batch_data,\n",
        "                          inp=[x, class_num, img_size, anchors, True, multi_scale_train, use_mix_up, letterbox_resizing],\n",
        "                          Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
        "      num_parallel_calls=num_threads\n",
        "  )\n",
        "  train_dataset = train_dataset.prefetch(prefetech_buffer)\n",
        "\n",
        "  val_dataset = tf.data.TFRecordDataset(filenames=val_file, compression_type='GZIP')\n",
        "  val_dataset = val_dataset.batch(1)\n",
        "  val_dataset = val_dataset.map(\n",
        "      lambda x: tf.py_func(get_batch_data,\n",
        "                          inp=[x, class_num, img_size, anchors, False, False, False, letterbox_resizing],\n",
        "                          Tout=[tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
        "      num_parallel_calls=num_threads\n",
        "  )\n",
        "  val_dataset.prefetch(prefetech_buffer)\n",
        "\n",
        "  iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)\n",
        "  train_init_op = iterator.make_initializer(train_dataset)\n",
        "  val_init_op = iterator.make_initializer(val_dataset)\n",
        "\n",
        "  image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()\n",
        "  y_true = [y_true_13, y_true_26, y_true_52]\n",
        "\n",
        "  image_ids.set_shape([None])\n",
        "  image.set_shape([None, None, None, 3])\n",
        "  for y in y_true:\n",
        "      y.set_shape([None, None, None, None, None])\n",
        "\n",
        "\n",
        "  ### Model definition\n",
        "  yolo_model = yolov3(class_num, anchors, use_label_smooth, use_focal_loss, batch_norm_decay, weight_decay, use_static_shape=False)\n",
        "\n",
        "  with tf.variable_scope('yolov3', reuse=tf.AUTO_REUSE):\n",
        "      pred_feature_maps = yolo_model.forward(image, is_training=is_training)\n",
        "\n",
        "  loss = yolo_model.compute_loss(pred_feature_maps, y_true)\n",
        "  y_pred = yolo_model.predict(pred_feature_maps)\n",
        "\n",
        "  l2_loss = tf.losses.get_regularization_loss()\n",
        "\n",
        "  saver_to_restore = tf.train.Saver(var_list=tf.contrib.framework.get_variables_to_restore(include=restore_include, exclude=restore_exclude))\n",
        "  update_vars = tf.contrib.framework.get_variables_to_restore(include=update_part)\n",
        "\n",
        "\n",
        "  global_step = tf.Variable(float(current_global_step), trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])\n",
        "  if use_warm_up:\n",
        "      learning_rate = tf.cond(tf.less(global_step, train_batch_num * warm_up_epoch), \n",
        "                              lambda: learning_rate_init * global_step / (train_batch_num * warm_up_epoch),\n",
        "                              lambda: config_learning_rate(global_step - train_batch_num * warm_up_epoch))\n",
        "  else:\n",
        "      learning_rate = config_learning_rate(global_step)\n",
        "\n",
        "  optimizer = config_optimizer(optimizer_name, learning_rate)\n",
        "\n",
        "  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
        "\n",
        "  with tf.control_dependencies(update_ops):\n",
        "      gvs = optimizer.compute_gradients(loss[0] + l2_loss, var_list=update_vars)\n",
        "      clip_grad_var = [gv if gv[0] is None else [\n",
        "            tf.clip_by_norm(gv[0], 100.), gv[1]] for gv in gvs]\n",
        "      train_op = optimizer.apply_gradients(clip_grad_var, global_step=global_step)\n",
        "\n",
        "  if save_optimizer:\n",
        "      print('Saving optimizer parameters: ON')\n",
        "      saver_to_save = tf.train.Saver()\n",
        "      saver_best = tf.train.Saver()\n",
        "  else:\n",
        "      print('Saving optimizer parameters: OFF')\n",
        "\n",
        "\n",
        "  with tf.Session() as sess:\n",
        "      sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])\n",
        "\n",
        "      try:\n",
        "          saver_to_restore.restore(sess, restore_path)\n",
        "          print(\"Restoring parameters...\")\n",
        "      except:\n",
        "          print(\"*** Failed to restore parameters!!! You would need pretrained weights ***\")\n",
        "\n",
        "      print('\\nStart training...: Total epoches =', total_epoches, '\\n')\n",
        "\n",
        "      best_mAP = -np.Inf\n",
        "\n",
        "      for epoch in range(total_epoches):\n",
        "          sess.run(train_init_op)\n",
        "          loss_total, loss_xy, loss_wh, loss_conf, loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
        "\n",
        "          ### train part\n",
        "          for i in trange(train_batch_num):\n",
        "              _, __y_pred, __y_true, __loss, __global_step, __lr = sess.run(\n",
        "                  [train_op, y_pred, y_true, loss, global_step, learning_rate],\n",
        "                  feed_dict={is_training: True})\n",
        "\n",
        "              loss_total.update(__loss[0], len(__y_pred[0]))\n",
        "              loss_xy.update(__loss[1], len(__y_pred[0]))\n",
        "              loss_wh.update(__loss[2], len(__y_pred[0]))\n",
        "              loss_conf.update(__loss[3], len(__y_pred[0]))\n",
        "              loss_class.update(__loss[4], len(__y_pred[0]))\n",
        "\n",
        "              if __global_step % train_evaluation_step == 0 and __global_step > 0:\n",
        "                  recall, precision = evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __y_pred, __y_true, class_num, nms_threshold)\n",
        "\n",
        "                  info = \"Epoch: {}, global_step: {} | loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f} | \".format(\n",
        "                          epoch, int(__global_step), loss_total.average, loss_xy.average, loss_wh.average, loss_conf.average, loss_class.average)\n",
        "                  info += 'Last batch: rec: {:.3f}, prec: {:.3f} | lr: {:.5g}'.format(recall, precision, __lr)\n",
        "                  print(info)\n",
        "          \n",
        "                  if np.isnan(loss_total.average):\n",
        "                      print('****' * 10)\n",
        "                      raise ArithmeticError('Gradient exploded!')\n",
        "\n",
        "          ## train end (saving parameters)\n",
        "          if save_optimizer and epoch % save_epoch == 0 and epoch > 0:\n",
        "              if loss_total.average <= 2.:\n",
        "                  saver_to_save.save(sess, save_dir + 'model-epoch_{}_step_{}_loss_{:.4f}_lr_{:.5g}'.format(epoch, int(__global_step), loss_total.average, __lr))\n",
        "\n",
        "          ### validation part\n",
        "          if epoch % val_evaluation_epoch == 0 and epoch >= warm_up_epoch:\n",
        "              sess.run(val_init_op)\n",
        "\n",
        "              val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
        "\n",
        "              val_preds = []\n",
        "\n",
        "              for j in trange(val_img_cnt):\n",
        "                  __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss],\n",
        "                                                          feed_dict={is_training: False})\n",
        "                  pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)\n",
        "                  val_preds.extend(pred_content)\n",
        "                  val_loss_total.update(__loss[0])\n",
        "                  val_loss_xy.update(__loss[1])\n",
        "                  val_loss_wh.update(__loss[2])\n",
        "                  val_loss_conf.update(__loss[3])\n",
        "                  val_loss_class.update(__loss[4])\n",
        "\n",
        "              # calc mAP\n",
        "              rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()\n",
        "              gt_dict = parse_gt_rec(val_file, 'GZIP', img_size, letterbox_resize)\n",
        "\n",
        "              info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\\n'.format(epoch, __global_step, __lr)\n",
        "\n",
        "              for ii in range(class_num):\n",
        "                  npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=eval_threshold, use_07_metric=use_voc_07_metric)\n",
        "                  info += 'EVAL: Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}\\n'.format(ii, rec, prec, ap)\n",
        "                  rec_total.update(rec, npos)\n",
        "                  prec_total.update(prec, nd)\n",
        "                  ap_total.update(ap, 1)\n",
        "\n",
        "              mAP = ap_total.average\n",
        "              info += 'EVAL: Recall: {:.4f}, Precison: {:.4f}, mAP: {:.4f}\\n'.format(rec_total.average, prec_total.average, mAP)\n",
        "              info += 'EVAL: loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f}\\n'.format(\n",
        "                  val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average)\n",
        "              print(info)\n",
        "\n",
        "              if save_optimizer and mAP > best_mAP:\n",
        "                  best_mAP = mAP\n",
        "                  saver_best.save(sess, save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(\n",
        "                                    epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))\n",
        "                  print(\"Saved parameters...\")\n",
        "      \n",
        "\n",
        "      ## all epoches end\n",
        "      sess.run(val_init_op)\n",
        "\n",
        "      val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
        "\n",
        "      val_preds = []\n",
        "\n",
        "      for j in trange(val_img_cnt):\n",
        "          __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss],\n",
        "                                                  feed_dict={is_training: False})\n",
        "          pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)\n",
        "          val_preds.extend(pred_content)\n",
        "          val_loss_total.update(__loss[0])\n",
        "          val_loss_xy.update(__loss[1])\n",
        "          val_loss_wh.update(__loss[2])\n",
        "          val_loss_conf.update(__loss[3])\n",
        "          val_loss_class.update(__loss[4])\n",
        "\n",
        "      # calc mAP\n",
        "      rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()\n",
        "      gt_dict = parse_gt_rec(val_file, 'GZIP', img_size, letterbox_resize)\n",
        "\n",
        "      info = '======> Epoch: {}, global_step: {}, lr: {:.6g} <======\\n'.format(epoch, __global_step, __lr)\n",
        "\n",
        "      for ii in range(class_num):\n",
        "          npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=eval_threshold, use_07_metric=use_voc_07_metric)\n",
        "          info += 'EVAL: Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}\\n'.format(ii, rec, prec, ap)\n",
        "          rec_total.update(rec, npos)\n",
        "          prec_total.update(prec, nd)\n",
        "          ap_total.update(ap, 1)\n",
        "\n",
        "      mAP = ap_total.average\n",
        "      info += 'EVAL: Recall: {:.4f}, Precison: {:.4f}, mAP: {:.4f}\\n'.format(rec_total.average, prec_total.average, mAP)\n",
        "      info += 'EVAL: loss: total: {:.2f}, xy: {:.2f}, wh: {:.2f}, conf: {:.2f}, class: {:.2f}\\n'.format(\n",
        "          val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average)\n",
        "      print(info)\n",
        "\n",
        "      if save_optimizer and mAP > best_mAP:\n",
        "          best_mAP = mAP\n",
        "          saver_best.save(sess, save_dir + 'best_model_Epoch_{}_step_{}_mAP_{:.4f}_loss_{:.4f}_lr_{:.7g}'.format(\n",
        "                            epoch, int(__global_step), best_mAP, val_loss_total.average, __lr))\n",
        "          print(\"Saved parameters...\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmoSmKIuOpyC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## evaluation (test)\n",
        "\n",
        "class ArgumentObject(object):\n",
        "    pass\n",
        "\n",
        "if trainingMode == 2:\n",
        "\n",
        "  args = ArgumentObject()\n",
        "  args.eval_file = \"/content/gdrive/My Drive/yolo/data/test.tfrecord\"\n",
        "  args.restore_path = \"/content/gdrive/My Drive/yolo/data/yolov3.ckpt\"\n",
        "  args.anchor_path = \"/content/gdrive/My Drive/yolo/data/yolo_anchors.txt\"\n",
        "  args.class_name_path = \"/content/gdrive/My Drive/yolo/data/classes.txt\"\n",
        "  args.img_size = [416, 416]\n",
        "  args.letterbox_resize = False\n",
        "  args.num_threads = 10\n",
        "  args.prefetech_buffer = 5\n",
        "  args.nms_threshold = 0.45\n",
        "  args.score_threshold = 0.01\n",
        "  args.nms_topk = 400\n",
        "  args.use_voc_07_metric = False\n",
        "\n",
        "  # args params\n",
        "  args.anchors = parse_anchors(args.anchor_path)\n",
        "  args.classes = read_class_names(args.class_name_path)\n",
        "  args.class_num = len(args.classes)\n",
        "\n",
        "\n",
        "  args.img_cnt = TFRecordIterator(args.eval_file, 'GZIP').count()\n",
        "\n",
        "  # setting placeholders\n",
        "  is_training = tf.placeholder(dtype=tf.bool, name=\"phase_train\")\n",
        "  handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')\n",
        "  pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])\n",
        "  pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])\n",
        "  gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold)\n",
        "\n",
        "  ### tf.data pipeline\n",
        "  val_dataset = tf.data.TFRecordDataset(filenames=args.eval_file, compression_type='GZIP')\n",
        "  val_dataset = val_dataset.batch(1)\n",
        "  val_dataset = val_dataset.map(\n",
        "      lambda x: tf.py_func(get_batch_data, [x, args.class_num, args.img_size, args.anchors, False, False, False, args.letterbox_resize], [tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),\n",
        "      num_parallel_calls=args.num_threads\n",
        "  )\n",
        "  val_dataset.prefetch(args.prefetech_buffer)\n",
        "  iterator = val_dataset.make_one_shot_iterator()\n",
        "\n",
        "  image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()\n",
        "  image_ids.set_shape([None])\n",
        "  y_true = [y_true_13, y_true_26, y_true_52]\n",
        "  image.set_shape([None, args.img_size[1], args.img_size[0], 3])\n",
        "  for y in y_true:\n",
        "      y.set_shape([None, None, None, None, None])\n",
        "\n",
        "  ### Model definition\n",
        "  yolo_model = yolov3(args.class_num, args.anchors)\n",
        "  with tf.variable_scope('yolov3', reuse=tf.AUTO_REUSE):\n",
        "      pred_feature_maps = yolo_model.forward(image, is_training=is_training)\n",
        "  loss = yolo_model.compute_loss(pred_feature_maps, y_true)\n",
        "  y_pred = yolo_model.predict(pred_feature_maps)\n",
        "\n",
        "  saver_to_restore = tf.train.Saver()\n",
        "\n",
        "\n",
        "  with tf.Session() as sess:\n",
        "      sess.run([tf.global_variables_initializer()])\n",
        "      try:\n",
        "          saver_to_restore.restore(sess, args.restore_path)\n",
        "      except:\n",
        "          raise ValueError('there is no model to evaluate. You should move/create the checkpoint file to restore path')\n",
        "\n",
        "      print('\\nStart evaluation...\\n')\n",
        "\n",
        "      val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = \\\n",
        "          AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n",
        "      val_preds = []\n",
        "\n",
        "      for j in trange(args.img_cnt):\n",
        "          __image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss], feed_dict={is_training: False})\n",
        "          pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)\n",
        "\n",
        "          val_preds.extend(pred_content)\n",
        "          val_loss_total.update(__loss[0])\n",
        "          val_loss_xy.update(__loss[1])\n",
        "          val_loss_wh.update(__loss[2])\n",
        "          val_loss_conf.update(__loss[3])\n",
        "          val_loss_class.update(__loss[4])\n",
        "\n",
        "      rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()\n",
        "      gt_dict = parse_gt_rec(args.eval_file, 'GZIP', args.img_size, args.letterbox_resize)\n",
        "      print('mAP eval:')\n",
        "      for ii in range(args.class_num):\n",
        "          npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=0.5, use_07_metric=args.use_voc_07_metric)\n",
        "          rec_total.update(rec, npos)\n",
        "          prec_total.update(prec, nd)\n",
        "          ap_total.update(ap, 1)\n",
        "          print('Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}'.format(ii, rec, prec, ap))\n",
        "\n",
        "      mAP = ap_total.average\n",
        "      print('final mAP: {:.4f}'.format(mAP))\n",
        "      print(\"recall: {:.3f}, precision: {:.3f}\".format(rec_total.average, prec_total.average))\n",
        "      print(\"total_loss: {:.3f}, loss_xy: {:.3f}, loss_wh: {:.3f}, loss_conf: {:.3f}, loss_class: {:.3f}\".format(\n",
        "          val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average\n",
        "      ))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWV3Gd-J1AHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "a6d10748-cef3-452a-a77d-058554f2a437"
      },
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "if trainingMode == 3:\n",
        "  args = ArgumentObject()\n",
        "\n",
        "  args.input_image = \"/content/gdrive/My Drive/yolo/input/dog4.png\"\n",
        "  args.anchor_path = \"/content/gdrive/My Drive/yolo/data/yolo_anchors.txt\"\n",
        "  args.new_size = [416, 416]\n",
        "  args.letterbox_resize = True\n",
        "  args.class_name_path = \"/content/gdrive/My Drive/yolo/data/classes.txt\"\n",
        "  args.restore_path = \"/content/gdrive/My Drive/yolo/data/yolov3.ckpt\"\n",
        "  args.anchors = parse_anchors(args.anchor_path)\n",
        "  args.classes = read_class_names(args.class_name_path)\n",
        "  args.num_class = len(args.classes)\n",
        "\n",
        "  color_table = get_color_table(args.num_class)\n",
        "\n",
        "  img_ori = cv2.imread(args.input_image)\n",
        "  if args.letterbox_resize:\n",
        "      img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0], args.new_size[1])\n",
        "  else:\n",
        "      height_ori, width_ori = img_ori.shape[:2]\n",
        "      img = cv2.resize(img_ori, tuple(args.new_size))\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  img = np.asarray(img, np.float32)\n",
        "  img = img[np.newaxis, :] / 255.\n",
        "\n",
        "  with tf.Session() as sess:\n",
        "      input_data = tf.placeholder(tf.float32, [1, args.new_size[1], args.new_size[0], 3], name='input_data')\n",
        "      yolo_model = yolov3(args.num_class, args.anchors)\n",
        "      with tf.variable_scope('yolov3', reuse=tf.AUTO_REUSE):\n",
        "          pred_feature_maps = yolo_model.forward(input_data, False)\n",
        "      pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)\n",
        "\n",
        "      pred_scores = pred_confs * pred_probs\n",
        "\n",
        "      boxes, scores, labels = gpu_nms(pred_boxes, pred_scores, args.num_class, max_boxes=200, score_thresh=0.3, nms_thresh=0.45)\n",
        "\n",
        "      saver = tf.train.Saver()\n",
        "      saver.restore(sess, args.restore_path)\n",
        "\n",
        "      boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})\n",
        "\n",
        "      if args.letterbox_resize:\n",
        "          boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio\n",
        "          boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio\n",
        "      else:\n",
        "          boxes_[:, [0, 2]] *= (width_ori/float(args.new_size[0]))\n",
        "          boxes_[:, [1, 3]] *= (height_ori/float(args.new_size[1]))\n",
        "\n",
        "      print(\"box coords:\")\n",
        "      print(boxes_)\n",
        "      print('*' * 30)\n",
        "      print(\"scores:\")\n",
        "      print(scores_)\n",
        "      print('*' * 30)\n",
        "      print(\"labels:\")\n",
        "      print(labels_)\n",
        "\n",
        "      for i in range(len(boxes_)):\n",
        "          x0, y0, x1, y1 = boxes_[i]\n",
        "          plot_one_box(img_ori, [x0, y0, x1, y1], label=args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100), color=color_table[labels_[i]])\n",
        "      cv2_imshow(img_ori)\n",
        "      cv2.waitKey(0)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Img size: Tensor(\"yolov3_7/strided_slice:0\", shape=(2,), dtype=int32)\n",
            "INFO:tensorflow:Restoring parameters from /content/gdrive/My Drive/yolo/data/yolov3.ckpt\n",
            "box coords:\n",
            "[]\n",
            "******************************\n",
            "scores:\n",
            "[]\n",
            "******************************\n",
            "labels:\n",
            "[]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=741x388 at 0x7F6FBC4D5BA8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}