yolo3_keras (1).ipynb

{
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  "metadata": {
    "colab": {
      "name": "yolo3 keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ShreshthSaxena/YOLO/blob/master/yolo3_keras%20(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doSErbjVCoeK",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "19e93789-9690-4df4-f850-37aedc476943"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2ca83d6e-b0e2-4160-a22c-08484a143dab\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2ca83d6e-b0e2-4160-a22c-08484a143dab\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving yolo.py to yolo.py\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'yolo.py': b'# -*- coding: utf-8 -*-\\n\"\"\"\\nClass definition of YOLO_v3 style detection model on image and video\\n\"\"\"\\n\\nimport colorsys\\nimport os\\nfrom timeit import default_timer as timer\\n\\nimport numpy as np\\nfrom keras import backend as K\\nfrom keras.models import load_model\\nfrom keras.layers import Input\\nfrom PIL import Image, ImageFont, ImageDraw\\n\\nfrom yolo3.model import yolo_eval, yolo_body, tiny_yolo_body\\nfrom yolo3.utils import letterbox_image\\nimport os\\nfrom keras.utils import multi_gpu_model\\n\\nclass YOLO(object):\\n    _defaults = {\\n        \"model_path\": \\'model_data/yolo.h5\\',\\n        \"anchors_path\": \\'model_data/yolo_anchors.txt\\',\\n        \"classes_path\": \\'model_data/coco_classes.txt\\',\\n        \"score\" : 0.3,\\n        \"iou\" : 0.45,\\n        \"model_image_size\" : (416, 416),\\n        \"gpu_num\" : 1,\\n    }\\n\\n    @classmethod\\n    def get_defaults(cls, n):\\n        if n in cls._defaults:\\n            return cls._defaults[n]\\n        else:\\n            return \"Unrecognized attribute name \\'\" + n + \"\\'\"\\n\\n    def __init__(self, **kwargs):\\n        self.__dict__.update(self._defaults) # set up default values\\n        self.__dict__.update(kwargs) # and update with user overrides\\n        self.class_names = self._get_class()\\n        self.anchors = self._get_anchors()\\n        self.sess = K.get_session()\\n        self.boxes, self.scores, self.classes = self.generate()\\n\\n    def _get_class(self):\\n        classes_path = os.path.expanduser(self.classes_path)\\n        with open(classes_path) as f:\\n            class_names = f.readlines()\\n        class_names = [c.strip() for c in class_names]\\n        return class_names\\n\\n    def _get_anchors(self):\\n        anchors_path = os.path.expanduser(self.anchors_path)\\n        with open(anchors_path) as f:\\n            anchors = f.readline()\\n        anchors = [float(x) for x in anchors.split(\\',\\')]\\n        return np.array(anchors).reshape(-1, 2)\\n\\n    def generate(self):\\n        model_path = os.path.expanduser(self.model_path)\\n        assert model_path.endswith(\\'.h5\\'), \\'Keras model or weights must be a .h5 file.\\'\\n\\n        # Load model, or construct model and load weights.\\n        num_anchors = len(self.anchors)\\n        num_classes = len(self.class_names)\\n        is_tiny_version = num_anchors==6 # default setting\\n        try:\\n            self.yolo_model = load_model(model_path, compile=False)\\n        except:\\n            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \\\\\\n                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)\\n            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match\\n        else:\\n            assert self.yolo_model.layers[-1].output_shape[-1] == \\\\\\n                num_anchors/len(self.yolo_model.output) * (num_classes + 5), \\\\\\n                \\'Mismatch between model and given anchor and class sizes\\'\\n\\n        print(\\'{} model, anchors, and classes loaded.\\'.format(model_path))\\n\\n        # Generate colors for drawing bounding boxes.\\n        hsv_tuples = [(x / len(self.class_names), 1., 1.)\\n                      for x in range(len(self.class_names))]\\n        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\\n        self.colors = list(\\n            map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\\n                self.colors))\\n        np.random.seed(10101)  # Fixed seed for consistent colors across runs.\\n        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.\\n        np.random.seed(None)  # Reset seed to default.\\n\\n        # Generate output tensor targets for filtered bounding boxes.\\n        self.input_image_shape = K.placeholder(shape=(2, ))\\n        if self.gpu_num>=2:\\n            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)\\n        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,\\n                len(self.class_names), self.input_image_shape,\\n                score_threshold=self.score, iou_threshold=self.iou)\\n        return boxes, scores, classes\\n\\n    def detect_image(self, image,obj):\\n        start = timer()\\n\\n        k=0\\n        if self.model_image_size != (None, None):\\n            assert self.model_image_size[0]%32 == 0, \\'Multiples of 32 required\\'\\n            assert self.model_image_size[1]%32 == 0, \\'Multiples of 32 required\\'\\n            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))\\n        else:\\n            new_image_size = (image.width - (image.width % 32),\\n                              image.height - (image.height % 32))\\n            boxed_image = letterbox_image(image, new_image_size)\\n        image_data = np.array(boxed_image, dtype=\\'float32\\')\\n\\n        print(image_data.shape)\\n        image_data /= 255.\\n        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.\\n\\n        out_boxes, out_scores, out_classes = self.sess.run(\\n            [self.boxes, self.scores, self.classes],\\n            feed_dict={\\n                self.yolo_model.input: image_data,\\n                self.input_image_shape: [image.size[1], image.size[0]],\\n                K.learning_phase(): 0\\n            })\\n\\n        \\n        #print(\\'Found {} boxes for {}\\'.format(len(out_boxes), \\'img\\'))\\n\\n        font = ImageFont.truetype(font=\\'font/FiraMono-Medium.otf\\',\\n                    size=np.floor(3e-2 * image.size[1] + 0.5).astype(\\'int32\\'))\\n        thickness = (image.size[0] + image.size[1]) // 300\\n\\n        for i, c in reversed(list(enumerate(out_classes))):\\n            \\n            predicted_class = self.class_names[c]\\n            if predicted_class == obj:\\n                k=k+1\\n                box = out_boxes[i]\\n                score = out_scores[i]                    \\n                label = \\'{} {:.2f}\\'.format(predicted_class, score)\\n                draw = ImageDraw.Draw(image)\\n                label_size = draw.textsize(label, font)\\n\\n                top, left, bottom, right = box\\n                top = max(0, np.floor(top + 0.5).astype(\\'int32\\'))\\n                left = max(0, np.floor(left + 0.5).astype(\\'int32\\'))\\n                bottom = min(image.size[1], np.floor(bottom + 0.5).astype(\\'int32\\'))\\n                right = min(image.size[0], np.floor(right + 0.5).astype(\\'int32\\'))\\n                print(label, (left, top), (right, bottom))\\n\\n                if top - label_size[1] >= 0:\\n                    text_origin = np.array([left, top - label_size[1]])\\n                else:\\n                    text_origin = np.array([left, top + 1])\\n\\n                # My kingdom for a good redistributable image drawing library.\\n                for i in range(thickness):\\n                    draw.rectangle(\\n                        [left + i, top + i, right - i, bottom - i],\\n                        outline=self.colors[c])\\n                draw.rectangle(\\n                    [tuple(text_origin), tuple(text_origin + label_size)],\\n                    fill=self.colors[c])\\n                draw.text(text_origin, label, fill=(0, 0, 0), font=font)\\n                del draw\\n\\n        print(\\'Found {} boxes for {} in {}\\'.format(k, obj, \\'img\\'))\\n        end = timer()\\n        print(end - start)\\n        return image\\n\\n    def close_session(self):\\n        self.sess.close()\\n\\ndef detect_video(yolo, video_path, output_path=\"\"):\\n    import cv2\\n    vid = cv2.VideoCapture(video_path)\\n    if not vid.isOpened():\\n        raise IOError(\"Couldn\\'t open webcam or video\")\\n    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))\\n    video_fps       = vid.get(cv2.CAP_PROP_FPS)\\n    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),\\n                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))\\n    isOutput = True if output_path != \"\" else False\\n    if isOutput:\\n        print(\"!!! TYPE:\", type(output_path), type(video_FourCC), type(video_fps), type(video_size))\\n        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)\\n    accum_time = 0\\n    curr_fps = 0\\n    fps = \"FPS: ??\"\\n    prev_time = timer()\\n    while True:\\n        return_value, frame = vid.read()\\n        image = Image.fromarray(frame)\\n        image = yolo.detect_image(image)\\n        result = np.asarray(image)\\n        curr_time = timer()\\n        exec_time = curr_time - prev_time\\n        prev_time = curr_time\\n        accum_time = accum_time + exec_time\\n        curr_fps = curr_fps + 1\\n        if accum_time > 1:\\n            accum_time = accum_time - 1\\n            fps = \"FPS: \" + str(curr_fps)\\n            curr_fps = 0\\n        cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,\\n                    fontScale=0.50, color=(255, 0, 0), thickness=2)\\n        cv2.namedWindow(\"result\", cv2.WINDOW_NORMAL)\\n        cv2.imshow(\"result\", result)\\n        if isOutput:\\n            out.write(result)\\n        if cv2.waitKey(1) & 0xFF == ord(\\'q\\'):\\n            break\\n    yolo.close_session()\\n\\n'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELJgNeRDT8Fi",
        "colab_type": "code",
        "outputId": "bad0cbe3-5ea6-4de2-9ce3-e00d830e860d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!git clone https://github.com/qqwweee/keras-yolo3"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'keras-yolo3'...\n",
            "remote: Enumerating objects: 144, done.\u001b[K\n",
            "Receiving objects:   0% (1/144)   \rReceiving objects:   1% (2/144)   \rReceiving objects:   2% (3/144)   \rReceiving objects:   3% (5/144)   \rReceiving objects:   4% (6/144)   \rReceiving objects:   5% (8/144)   \rReceiving objects:   6% (9/144)   \rReceiving objects:   7% (11/144)   \rReceiving objects:   8% (12/144)   \rReceiving objects:   9% (13/144)   \rReceiving objects:  10% (15/144)   \rReceiving objects:  11% (16/144)   \rReceiving objects:  12% (18/144)   \rReceiving objects:  13% (19/144)   \rReceiving objects:  14% (21/144)   \rReceiving objects:  15% (22/144)   \rReceiving objects:  16% (24/144)   \rReceiving objects:  17% (25/144)   \rReceiving objects:  18% (26/144)   \rReceiving objects:  19% (28/144)   \rReceiving objects:  20% (29/144)   \rReceiving objects:  21% (31/144)   \rReceiving objects:  22% (32/144)   \rReceiving objects:  23% (34/144)   \rReceiving objects:  24% (35/144)   \rReceiving objects:  25% (36/144)   \rremote: Total 144 (delta 0), reused 0 (delta 0), pack-reused 144\u001b[K\n",
            "Receiving objects:  26% (38/144)   \rReceiving objects:  27% (39/144)   \rReceiving objects:  28% (41/144)   \rReceiving objects:  29% (42/144)   \rReceiving objects:  30% (44/144)   \rReceiving objects:  31% (45/144)   \rReceiving objects:  32% (47/144)   \rReceiving objects:  33% (48/144)   \rReceiving objects:  34% (49/144)   \rReceiving objects:  35% (51/144)   \rReceiving objects:  36% (52/144)   \rReceiving objects:  37% (54/144)   \rReceiving objects:  38% (55/144)   \rReceiving objects:  39% (57/144)   \rReceiving objects:  40% (58/144)   \rReceiving objects:  41% (60/144)   \rReceiving objects:  42% (61/144)   \rReceiving objects:  43% (62/144)   \rReceiving objects:  44% (64/144)   \rReceiving objects:  45% (65/144)   \rReceiving objects:  46% (67/144)   \rReceiving objects:  47% (68/144)   \rReceiving objects:  48% (70/144)   \rReceiving objects:  49% (71/144)   \rReceiving objects:  50% (72/144)   \rReceiving objects:  51% (74/144)   \rReceiving objects:  52% (75/144)   \rReceiving objects:  53% (77/144)   \rReceiving objects:  54% (78/144)   \rReceiving objects:  55% (80/144)   \rReceiving objects:  56% (81/144)   \rReceiving objects:  57% (83/144)   \rReceiving objects:  58% (84/144)   \rReceiving objects:  59% (85/144)   \rReceiving objects:  60% (87/144)   \rReceiving objects:  61% (88/144)   \rReceiving objects:  62% (90/144)   \rReceiving objects:  63% (91/144)   \rReceiving objects:  64% (93/144)   \rReceiving objects:  65% (94/144)   \rReceiving objects:  66% (96/144)   \rReceiving objects:  67% (97/144)   \rReceiving objects:  68% (98/144)   \rReceiving objects:  69% (100/144)   \rReceiving objects:  70% (101/144)   \rReceiving objects:  71% (103/144)   \rReceiving objects:  72% (104/144)   \rReceiving objects:  73% (106/144)   \rReceiving objects:  74% (107/144)   \rReceiving objects:  75% (108/144)   \rReceiving objects:  76% (110/144)   \rReceiving objects:  77% (111/144)   \rReceiving objects:  78% (113/144)   \rReceiving objects:  79% (114/144)   \rReceiving objects:  80% (116/144)   \rReceiving objects:  81% (117/144)   \rReceiving objects:  82% (119/144)   \rReceiving objects:  83% (120/144)   \rReceiving objects:  84% (121/144)   \rReceiving objects:  85% (123/144)   \rReceiving objects:  86% (124/144)   \rReceiving objects:  87% (126/144)   \rReceiving objects:  88% (127/144)   \rReceiving objects:  89% (129/144)   \rReceiving objects:  90% (130/144)   \rReceiving objects:  91% (132/144)   \rReceiving objects:  92% (133/144)   \rReceiving objects:  93% (134/144)   \rReceiving objects:  94% (136/144)   \rReceiving objects:  95% (137/144)   \rReceiving objects:  96% (139/144)   \rReceiving objects:  97% (140/144)   \rReceiving objects:  98% (142/144)   \rReceiving objects:  99% (143/144)   \rReceiving objects: 100% (144/144)   \rReceiving objects: 100% (144/144), 151.24 KiB | 10.80 MiB/s, done.\n",
            "Resolving deltas:   0% (0/64)   \rResolving deltas:   7% (5/64)   \rResolving deltas:   9% (6/64)   \rResolving deltas:  10% (7/64)   \rResolving deltas:  12% (8/64)   \rResolving deltas:  15% (10/64)   \rResolving deltas:  17% (11/64)   \rResolving deltas:  21% (14/64)   \rResolving deltas:  26% (17/64)   \rResolving deltas:  43% (28/64)   \rResolving deltas:  51% (33/64)   \rResolving deltas:  62% (40/64)   \rResolving deltas:  65% (42/64)   \rResolving deltas:  67% (43/64)   \rResolving deltas:  76% (49/64)   \rResolving deltas:  79% (51/64)   \rResolving deltas:  85% (55/64)   \rResolving deltas:  89% (57/64)   \rResolving deltas:  93% (60/64)   \rResolving deltas:  98% (63/64)   \rResolving deltas: 100% (64/64)   \rResolving deltas: 100% (64/64), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOS0l5DeULdn",
        "colab_type": "code",
        "outputId": "04bcafb8-daec-4763-9d58-d994b3bb8b47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd keras-yolo3"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/keras-yolo3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzPgKeHbUbk8",
        "colab_type": "code",
        "outputId": "0642e4df-ece3-4b1a-8516-bb410ccaae2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget https://pjreddie.com/media/files/yolov3.weights"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-14 18:26:03--  https://pjreddie.com/media/files/yolov3.weights\n",
            "Resolving pjreddie.com (pjreddie.com)... 128.208.4.108\n",
            "Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 248007048 (237M) [application/octet-stream]\n",
            "Saving to: ‘yolov3.weights’\n",
            "\n",
            "yolov3.weights      100%[===================>] 236.52M  63.2MB/s    in 3.5s    \n",
            "\n",
            "2019-05-14 18:26:07 (67.2 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlWe3-_8UrEJ",
        "colab_type": "code",
        "outputId": "cb5931de-377a-4c03-ec0f-20b90d27eb55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 12532
        }
      },
      "source": [
        "!python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "Loading weights.\n",
            "Weights Header:  0 2 0 [32013312]\n",
            "Parsing Darknet config.\n",
            "Creating Keras model.\n",
            "Parsing section net_0\n",
            "Parsing section convolutional_0\n",
            "conv2d bn leaky (3, 3, 3, 32)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "2019-05-14 18:26:11.776119: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2019-05-14 18:26:11.782964: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2854840 executing computations on platform Host. Devices:\n",
            "2019-05-14 18:26:11.783117: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "Parsing section convolutional_1\n",
            "conv2d bn leaky (3, 3, 32, 64)\n",
            "Parsing section convolutional_2\n",
            "conv2d bn leaky (1, 1, 64, 32)\n",
            "Parsing section convolutional_3\n",
            "conv2d bn leaky (3, 3, 32, 64)\n",
            "Parsing section shortcut_0\n",
            "Parsing section convolutional_4\n",
            "conv2d bn leaky (3, 3, 64, 128)\n",
            "Parsing section convolutional_5\n",
            "conv2d bn leaky (1, 1, 128, 64)\n",
            "Parsing section convolutional_6\n",
            "conv2d bn leaky (3, 3, 64, 128)\n",
            "Parsing section shortcut_1\n",
            "Parsing section convolutional_7\n",
            "conv2d bn leaky (1, 1, 128, 64)\n",
            "Parsing section convolutional_8\n",
            "conv2d bn leaky (3, 3, 64, 128)\n",
            "Parsing section shortcut_2\n",
            "Parsing section convolutional_9\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section convolutional_10\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_11\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_3\n",
            "Parsing section convolutional_12\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_13\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_4\n",
            "Parsing section convolutional_14\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_15\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_5\n",
            "Parsing section convolutional_16\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_17\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_6\n",
            "Parsing section convolutional_18\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_19\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_7\n",
            "Parsing section convolutional_20\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_21\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_8\n",
            "Parsing section convolutional_22\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_23\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_9\n",
            "Parsing section convolutional_24\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_25\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section shortcut_10\n",
            "Parsing section convolutional_26\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section convolutional_27\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_28\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_11\n",
            "Parsing section convolutional_29\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_30\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_12\n",
            "Parsing section convolutional_31\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_32\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_13\n",
            "Parsing section convolutional_33\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_34\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_14\n",
            "Parsing section convolutional_35\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_36\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_15\n",
            "Parsing section convolutional_37\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_38\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_16\n",
            "Parsing section convolutional_39\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_40\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_17\n",
            "Parsing section convolutional_41\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_42\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section shortcut_18\n",
            "Parsing section convolutional_43\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section convolutional_44\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_45\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section shortcut_19\n",
            "Parsing section convolutional_46\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_47\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section shortcut_20\n",
            "Parsing section convolutional_48\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_49\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section shortcut_21\n",
            "Parsing section convolutional_50\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_51\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section shortcut_22\n",
            "Parsing section convolutional_52\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_53\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section convolutional_54\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_55\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section convolutional_56\n",
            "conv2d bn leaky (1, 1, 1024, 512)\n",
            "Parsing section convolutional_57\n",
            "conv2d bn leaky (3, 3, 512, 1024)\n",
            "Parsing section convolutional_58\n",
            "conv2d    linear (1, 1, 1024, 255)\n",
            "Parsing section yolo_0\n",
            "Parsing section route_0\n",
            "Parsing section convolutional_59\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section upsample_0\n",
            "Parsing section route_1\n",
            "Concatenating route layers: [<tf.Tensor 'up_sampling2d_1/ResizeNearestNeighbor:0' shape=(?, ?, ?, 256) dtype=float32>, <tf.Tensor 'add_19/add:0' shape=(?, ?, ?, 512) dtype=float32>]\n",
            "Parsing section convolutional_60\n",
            "conv2d bn leaky (1, 1, 768, 256)\n",
            "Parsing section convolutional_61\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section convolutional_62\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_63\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section convolutional_64\n",
            "conv2d bn leaky (1, 1, 512, 256)\n",
            "Parsing section convolutional_65\n",
            "conv2d bn leaky (3, 3, 256, 512)\n",
            "Parsing section convolutional_66\n",
            "conv2d    linear (1, 1, 512, 255)\n",
            "Parsing section yolo_1\n",
            "Parsing section route_2\n",
            "Parsing section convolutional_67\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section upsample_1\n",
            "Parsing section route_3\n",
            "Concatenating route layers: [<tf.Tensor 'up_sampling2d_2/ResizeNearestNeighbor:0' shape=(?, ?, ?, 128) dtype=float32>, <tf.Tensor 'add_11/add:0' shape=(?, ?, ?, 256) dtype=float32>]\n",
            "Parsing section convolutional_68\n",
            "conv2d bn leaky (1, 1, 384, 128)\n",
            "Parsing section convolutional_69\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section convolutional_70\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_71\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section convolutional_72\n",
            "conv2d bn leaky (1, 1, 256, 128)\n",
            "Parsing section convolutional_73\n",
            "conv2d bn leaky (3, 3, 128, 256)\n",
            "Parsing section convolutional_74\n",
            "conv2d    linear (1, 1, 256, 255)\n",
            "Parsing section yolo_2\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, None, None, 3 128         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0           leaky_re_lu_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, None, None, 6 18432       zero_padding2d_1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, None, None, 6 256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, None, None, 3 2048        leaky_re_lu_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, None, None, 3 128         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, None, None, 6 18432       leaky_re_lu_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, None, None, 6 256         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, None, None, 6 0           leaky_re_lu_2[0][0]              \n",
            "                                                                 leaky_re_lu_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, None, None, 1 73728       zero_padding2d_2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, None, None, 1 512         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, None, None, 6 8192        leaky_re_lu_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, None, None, 6 256         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_6 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, None, None, 1 512         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_7 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, None, None, 1 0           leaky_re_lu_5[0][0]              \n",
            "                                                                 leaky_re_lu_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, None, None, 6 8192        add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, None, None, 6 256         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_8 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, None, None, 1 512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_9 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, None, None, 1 0           add_2[0][0]                      \n",
            "                                                                 leaky_re_lu_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, None, None, 2 294912      zero_padding2d_3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, None, None, 2 1024        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_10 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, None, None, 1 512         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_11 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_11[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, None, None, 2 1024        conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_12 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, None, None, 2 0           leaky_re_lu_10[0][0]             \n",
            "                                                                 leaky_re_lu_12[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, None, None, 1 32768       add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, None, None, 1 512         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_13 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_13[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, None, None, 2 1024        conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_14 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, None, None, 2 0           add_4[0][0]                      \n",
            "                                                                 leaky_re_lu_14[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, None, None, 1 32768       add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, None, None, 1 512         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_15 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_15[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, None, None, 2 1024        conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_16 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, None, None, 2 0           add_5[0][0]                      \n",
            "                                                                 leaky_re_lu_16[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, None, None, 1 32768       add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, None, None, 1 512         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_17 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_17[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, None, None, 2 1024        conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_18 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, None, None, 2 0           add_6[0][0]                      \n",
            "                                                                 leaky_re_lu_18[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, None, None, 1 32768       add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, None, None, 1 512         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_19 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_19[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, None, None, 2 1024        conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_20 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, None, None, 2 0           add_7[0][0]                      \n",
            "                                                                 leaky_re_lu_20[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, None, None, 1 32768       add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, None, None, 1 512         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_21 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_21[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, None, None, 2 1024        conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_22 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, None, None, 2 0           add_8[0][0]                      \n",
            "                                                                 leaky_re_lu_22[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, None, None, 1 32768       add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, None, None, 1 512         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_23 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_23[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, None, None, 2 1024        conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_24 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, None, None, 2 0           add_9[0][0]                      \n",
            "                                                                 leaky_re_lu_24[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, None, None, 1 32768       add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, None, None, 1 512         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_25 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_25[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, None, None, 2 1024        conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_26 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, None, None, 2 0           add_10[0][0]                     \n",
            "                                                                 leaky_re_lu_26[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, None, None, 5 1179648     zero_padding2d_4[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, None, None, 5 2048        conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_27 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_27[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, None, None, 2 1024        conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_28 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_28[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, None, None, 5 2048        conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_29 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, None, None, 5 0           leaky_re_lu_27[0][0]             \n",
            "                                                                 leaky_re_lu_29[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, None, None, 2 131072      add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, None, None, 2 1024        conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_30 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_30[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, None, None, 5 2048        conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_31 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, None, None, 5 0           add_12[0][0]                     \n",
            "                                                                 leaky_re_lu_31[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, None, None, 2 131072      add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, None, None, 2 1024        conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_32 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_32[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, None, None, 5 2048        conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_33 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, None, None, 5 0           add_13[0][0]                     \n",
            "                                                                 leaky_re_lu_33[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, None, None, 2 131072      add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, None, None, 2 1024        conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_34 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_34[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, None, None, 5 2048        conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_35 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, None, None, 5 0           add_14[0][0]                     \n",
            "                                                                 leaky_re_lu_35[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, None, None, 2 131072      add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, None, None, 2 1024        conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_36 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_36[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, None, None, 5 2048        conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_37 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, None, None, 5 0           add_15[0][0]                     \n",
            "                                                                 leaky_re_lu_37[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, None, None, 2 131072      add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, None, None, 2 1024        conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_38 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_38[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, None, None, 5 2048        conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_39 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_17 (Add)                    (None, None, None, 5 0           add_16[0][0]                     \n",
            "                                                                 leaky_re_lu_39[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, None, None, 2 131072      add_17[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, None, None, 2 1024        conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_40 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_40[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, None, None, 5 2048        conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_41 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_18 (Add)                    (None, None, None, 5 0           add_17[0][0]                     \n",
            "                                                                 leaky_re_lu_41[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, None, None, 2 131072      add_18[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, None, None, 2 1024        conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_42 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_42[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, None, None, 5 2048        conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_43 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_19 (Add)                    (None, None, None, 5 0           add_18[0][0]                     \n",
            "                                                                 leaky_re_lu_43[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0           add_19[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, None, None, 1 4718592     zero_padding2d_5[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, None, None, 1 4096        conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_44 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_44[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, None, None, 5 2048        conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_45 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_45[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, None, None, 1 4096        conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_46 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_20 (Add)                    (None, None, None, 1 0           leaky_re_lu_44[0][0]             \n",
            "                                                                 leaky_re_lu_46[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, None, None, 5 524288      add_20[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, None, None, 5 2048        conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_47 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_47[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, None, None, 1 4096        conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_48 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_21 (Add)                    (None, None, None, 1 0           add_20[0][0]                     \n",
            "                                                                 leaky_re_lu_48[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, None, None, 5 524288      add_21[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, None, None, 5 2048        conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_49 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_49[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, None, None, 1 4096        conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_50 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_22 (Add)                    (None, None, None, 1 0           add_21[0][0]                     \n",
            "                                                                 leaky_re_lu_50[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, None, None, 5 524288      add_22[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, None, None, 5 2048        conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_51 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_51[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, None, None, 1 4096        conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_52 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "add_23 (Add)                    (None, None, None, 1 0           add_22[0][0]                     \n",
            "                                                                 leaky_re_lu_52[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, None, None, 5 524288      add_23[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, None, None, 5 2048        conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_53 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_53[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, None, None, 1 4096        conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_54 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_54[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, None, None, 5 2048        conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_55 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_55[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, None, None, 1 4096        conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_56 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_56[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, None, None, 5 2048        conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_57 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_57[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, None, None, 2 1024        conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_59 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2D)  (None, None, None, 2 0           leaky_re_lu_59[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, None, None, 7 0           up_sampling2d_1[0][0]            \n",
            "                                                                 add_19[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, None, None, 2 196608      concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, None, None, 2 1024        conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_60 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_60[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, None, None, 5 2048        conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_61 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_61[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, None, None, 2 1024        conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_62 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_62[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, None, None, 5 2048        conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_63 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_63[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, None, None, 2 1024        conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_64 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_64[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, None, None, 1 512         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_66 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2D)  (None, None, None, 1 0           leaky_re_lu_66[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, None, None, 3 0           up_sampling2d_2[0][0]            \n",
            "                                                                 add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, None, None, 1 49152       concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, None, None, 1 512         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_67 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_67[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, None, None, 2 1024        conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_68 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_68[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, None, None, 1 512         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_69 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_69[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, None, None, 2 1024        conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_70 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_70[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, None, None, 1 512         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_71 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_57[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_64[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_71[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, None, None, 1 4096        conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, None, None, 5 2048        conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, None, None, 2 1024        conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_58 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_65 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "leaky_re_lu_72 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, None, None, 2 261375      leaky_re_lu_58[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, None, None, 2 130815      leaky_re_lu_65[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, None, None, 2 65535       leaky_re_lu_72[0][0]             \n",
            "==================================================================================================\n",
            "Total params: 62,001,757\n",
            "Trainable params: 61,949,149\n",
            "Non-trainable params: 52,608\n",
            "__________________________________________________________________________________________________\n",
            "None\n",
            "Saved Keras model to model_data/yolo.h5\n",
            "Read 62001757 of 62001757.0 from Darknet weights.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpqOuC-m2h89",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "627fe03f-c6c5-4500-9f6f-7b44538f9b93"
      },
      "source": [
        "!python yolo_video.py --help"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]\n",
            "                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]\n",
            "                     [--input [INPUT]] [--output [OUTPUT]]\n",
            "\n",
            "optional arguments:\n",
            "  -h, --help         show this help message and exit\n",
            "  --model MODEL      path to model weight file, default model_data/yolo.h5\n",
            "  --anchors ANCHORS  path to anchor definitions, default\n",
            "                     model_data/yolo_anchors.txt\n",
            "  --classes CLASSES  path to class definitions, default\n",
            "                     model_data/coco_classes.txt\n",
            "  --gpu_num GPU_NUM  Number of GPU to use, default 1\n",
            "  --image            Image detection mode, will ignore all positional\n",
            "                     arguments\n",
            "  --input [INPUT]    Video input path\n",
            "  --output [OUTPUT]  [Optional] Video output path\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxq9aigt97u9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "142382e0-13d8-4d1b-9dbe-bb010afa81ea"
      },
      "source": [
        "!wget http://www.beatclub.ie/themes/beatclub/images/beatclub.jpg\n",
        "!python yolo_video.py --image"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "Image detection mode\n",
            " Ignoring remaining command line arguments: ./path2your_video,\n",
            "2019-05-14 18:44:57.064739: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2019-05-14 18:44:57.065102: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x1c82840 executing computations on platform Host. Devices:\n",
            "2019-05-14 18:44:57.065176: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "model_data/yolo.h5 model, anchors, and classes loaded.\n",
            "Input image filename:beatclub.jpg\n",
            "Input object type:person\n",
            "(416, 416, 3)\n",
            "person 0.89 (1971, 184) (2213, 825)\n",
            "person 0.91 (36, 237) (385, 845)\n",
            "person 0.97 (1263, 183) (1531, 896)\n",
            "person 0.98 (1635, 166) (1852, 830)\n",
            "person 0.99 (497, 157) (749, 895)\n",
            "Found 5 boxes for person in img\n",
            "4.561795080000138\n",
            "Input image filename:beatclub.jpg\n",
            "Input object type:car\n",
            "(416, 416, 3)\n",
            "Found 0 boxes for car in img\n",
            "1.8287276209998709\n",
            "Input image filename:Traceback (most recent call last):\n",
            "  File \"yolo_video.py\", line 89, in <module>\n",
            "    detect_img(YOLO(**vars(FLAGS)))\n",
            "  File \"yolo_video.py\", line 17, in detect_img\n",
            "    img = input('Input image filename:')\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G8lj17_qxeY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "3d2ad88f-7786-406d-b517-5b5a9ceede98"
      },
      "source": [
        "from IPython.display import Image\n",
        "Image('last.jpg', width=416, height=416)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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BUgHlyg/wALcH2NAFe5TYwx0IzU1uhaD6nF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\nqUsbyKWMZbgQ4JHTu2RV74lajNL4S0vxroMmy4sZVkDAg5jP3lOOCM1tC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r40+HuseBgsqn7Zp8rYS4iUhgfRl7H9K4u2vr63u45baaWOeNtyFcgg/1rGVOTd0dccRGMEle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\nRhxvPJ/M1qUUyJTlLc5q7zD4/wBPk7TWUkX5HdXSVzmtusPirw6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UnYLGD3A6n86aEz1G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\nO49hjpXTG2p4FRyvH1LU+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elIUYdq0vKba+3nuMdqRmTygrQgknlmPQUAV7E7Rdg5+a3K\njHruWvfPgT/yJt2f+ns/yrw2A/u7sDHy25Bx9Vr3P4EY/wCENu/+vs/ypoD1QDpVtPuiqYNXE+6K\nYh1FFFABRRRQAUUUUAFZXiO2W78NalC/3Wt3PHXgE1q1heMbs2Pg3V7gMEZbVwGPQEjA/nQB8aMS\neT1PJphqVzk59eajNSMlstn9oWwc4QyqCfbIr1C/hubH+0j5ahnt03JGNzxFSdmfbB6+teU9/T0N\nd3pvxQvNOtGiGlWrXMgCzXakiSQev1x7Vw4ylVnaVLVrpt2OvC140r39fuKeiTW+k/EOPUL6YyW0\nEgkklhUuMlc4969Y8QeK7XVfCVtfC0Me47YBIMsSTyfpXndp4u0TWfFML6ppQtbKY7ZHDbiG4GTg\nDA6816/D4a04XEDRQxy26pmA8FF9xXlZhXdKKdSLWivs9uh3YWVNS573d7/MzPCV3nT0hkgMsTsW\nG7kj8K7DT7Cye4dmhiAQ4AwMg1kx6XFZzkWSsbj737xsonPX/wCtVq3uYbOeW30+U3d7MdroOdr/\nAN5vQV4mHpxqV/aON03t3+ReJqKo24aXNrSYA+q3dyoxHGogjA6f3mP5nFbtVbC1FjZRwbtzAZZj\n1Y9zVqvuMNS9lRjBrZHi1Jc0ro4n4m+EF8YeEpoIkzf2uZrY9yw6r9CP6V8llWUlWBDAkEHgg+lf\ndVfJXxV0I6F8QNQVF2w3Tm4QAYHzEkj863ZJxanselTIpVldDioKnt2+bae/SkBs244AYgk9RzWj\ntj8kAkH1A61n2o55PYE1pxqvlKevGcDrQMyNTjSOSFUHGCSfyr1P4Bkf27qgHe2H/oQrzDVMeZAM\ndia9S+AiZ1fU27rCMfTNMR7xRRRTEFFFFABXz948tl0341x8bItSsypPQEkMP5qPxr6BrxL48QPY\nX/h/xBF96CQox9gQR/M0AjpJ777b4t+H93vDGa3uMgDqfLUZ/MmvSa8Y0O+hvPHHh7Sc4m097piv\nX5GYsn0G017PQNhRRRQIKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmn47hj46BP3RbIB+te\nWdK9p+P+mtHrFhqCj5JYfLY+4Jx+leLGpYxRThTQKkA4H60AOGSatAjge2Krxj5qsDJP0oAcOwqZ\nSMMCPTmmhTgZFTRjJ64FAxS3APetDw9pb61r1jYKM+dOFPpjr/IVmyNhc/3jxXqXwa8Pm6199Udf\n3VonBI6u3/1uaAMz44+GIdK1mw1C0hEcN1H5bIgAG5AP6EV5MnQ/Wvp/41afHdeA3umA8y0mEiH6\ngqf5ivmFDgfWhiJFZl6VIspxyM1GjrzmnjHY0AWIrgDAIxWhayK+OKzkTdjHpVm1wkozzzQM3IiF\nFSr6CoYyBHuA461MpLYx1PrTAtW5CyKT619K6M2/RLFs5zAnP4CvmqEcj1r6D8E3LXXhSzdjkquz\n8BQJnQ0UUUxBRRRQAV4r+0GcWOkfWT+Qr2qvE/2hD/oekD3k/kKTBHmfwtIPxC0hSes68fjX0Z4F\nkAstVtByLXU7iMH23k4/WvmfwDe2+meMdNvrqURw28okdm4AAr6T+GsTHwhHqEgKy6lPNeuD1HmO\nSP0xQhnY0UUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUm4AZ7etL\nXj3xg8btYxHQrKa4trjIaaQAAMhHQGgDqPFnxI0vw9DLDA6XN4oOFH3VPue/4V8xa5c3Go6tc6lL\nKJZbiQyOQAACT0+lRXF5LPKzvNvLHJNVyzkYBzmpbGRrIfxqTcDUTI2c45oVuzcH1oAlwK0otZdY\nI4b21t72OP7nm5Vh7blwT+NZZOwbjz6Gq7MzHJpNJ7lRm46o6iHxFpJIWXT7uD3gnDDPrhhn9aux\n6rokg+XU3jJ6CW3I/UE/yriaMVlKhTl0PRpZvi6Wilf1R30X2a4OIL+0kJ6AOQT+YFTPp90ke5os\np6oyt/I153gdxT0lki/1csif7jEVm8JHozvhxHX2nBP0ujujG6D51dfqCKillSGJpHzsUZOOTiud\nstX1GCGd0u5SFUABsNgk++ewNaPh/U7q+1dUv5BLaJG8sweMfdAPpjuRUfVHfc6XxHCUbODT+/8A\nyL1tNHcQrLESUboSKkPFYcWuzsdiadb4J4EW4d+mOat/2jIMNc2gt0Pd5+T+GKmWGlfTYulnlBwS\nne/XQusTnNNLVW/tbSi+0y3gHdvJUjP/AH10/CrVt9m1CYRWV35sjdIxCxY/gM0vYzXQtZnh57SI\nz1zVe6cKOOp6Vfu4FsyYp7iOOT+7KGQ/qKz5IfNwFntm9MTCiNN31RlXxNNwtCSfzK5kIOC34ipb\nB2F8pwDlSOfpUH2W5Ezj92yYG0KwJzVqys7k3G5YvuAsdxAAHrWso2icFGq3Vi30aMnUk8u7uF9M\n/wAq7L4mkPqehSJ8zTaPbnA5Ocelc5qkenW9400t2bndj91bjj8WPT8Aa09amk0pZ47IJGI3SIOc\nyOUZcj5m6fgBXTBPlVzyq7XtZW7s6a28aazoNnok1myWCW1gLW5W+G5ZiCMFUHPQdTiub1nxOmpX\ndxq1w9xqV5KREZJz5UQX0Ea8446EmuWub2e7ybmV5nOMPIcke2fSmCVzB5PSPduI7k1RhfsdDokt\n1rmtWlhczkQTOE8uMBEXPoBWLqcKwalcRIcqrFcjjJ7103w7gW58a6Sjcq1woP0zXr/if4HaVqfm\nXGj3L2Vy2W2SZeNj/MfrQI+bcVcscLp+pOepjVB9dwq7q3h+90LXZNL1CIJcRHDhTkEYznP0qlCA\nmgTSfxS3CqPoAaAKVKPSm5zT1GKAHcCngcVHnn3p+8KOaAHgV6F8IbaSTx5Yuo4XLE+2DXAQxGVg\nz/LGOg7mvfPgl4fZIrjWpl2hx5cI9u9CA9koooqhBXz/APHuXzPFWkQf3bUt+bn/AAr6Ar5z+NLm\nf4mWkH922iXH1Y0mNHufheAReE9KiI4FpHkf8BFcLr/gO28U6fqFjb4i1DS7graSdMowD7CfTJPP\navRtKj8rR7KP+5Ag/wDHRWXpL58V6/H6GA/+OUwPPvh34u1KbxBb+HfEK+Xd2KSRpM5+aQkj5T74\nHXuK9hFeY/Ffwob62tdW0uBhqqzLHmLguMHH4ggYqb4a/EBtbjbQta/c61ajb8+AZwOpx2Ydx+NI\nD0miiimIKo61/wAgLUP+vaT/ANBNXqpax/yBb/8A695P/QTQB84/Brj4m2n/AFxm/wDQTX03XzL8\nHePidaf9cp//AEE19NUkNkVxClzbywSDKSIUYHuCK+U9GlfwZ8TrVpTtWy1AxyFv+ebEoT/3yxNf\nWNfOHxu0U2PjFb5FIiv4Qc9t6jB/oaGCPo1WDqGU5BAIp1c14C1ldd8FaXebt0nkrHKf9tQAf1rp\naYjN1/T/AO1dAvrH+KaFlQ+jY4P54qPw3qL6t4esbyTiZ4gJR6OOG/UGtaua0ELpuv6tpJOBJJ9s\ngH+y/wB4fg386AOlooooAKKKKACiiigAooooAKKKKACiiigArx74u6e1rfw6lGMJLHgkdd6kc/kR\nXsNcp8QtJ/tbwhdqq7pIAZlGMk4Bz+hoBHBeHr+PUdLijdtzBQpweQa6CNiVKZIBPAzkkYrzXwRd\npFJcwO22RcMoz3z1ru7TVY/NEbDPl5zjrnHr2pFFm+cJauFQs6/MBnOKwtSdZPDt07AqEjJx681o\n3d1tMiuzkkcDdn8K5bVLpFE0LggSRlQT0yQf8KAPMr+dvMYrgZJBHYCs1iZJETsWwKu3JT5lYAN7\netUgTHJHIo5VgaQioaSnN94/U0lABSUUUCFpaSigZZsZUt7uKeRdyRuGZf7wB6V9uWUqT2NvKgwr\nxKyj0BFfKnwv8KxeK9fure4jEkMVpI5DDI3EYX9Tmvo34f3j33gPRZZH3yrbLFIT13INp/lTQHTU\nUUUxBRRRQAV8s/G+MQ/Ee6A48yKOQ/8AfIH9K+pq+UvjTdi6+J1+FOVhiii/EKM/qaTA422I/si+\nHfMZH5mqq/cqxan/AIl96PVU/wDQqrKcCkMCMY4/CjFLxgf/AFqKAG4op1JQAlFLzSUAFFH+c0UA\nJRS0lAHvnwYWyHg+WFbsRXd5M8bRreFGYY6heze4ryHxkmlReIp7bSrGezSAmKeOaXzCZQTk7u4r\n1j4NKYPCouZB53nX5trYR2qSNbyEf61m6gV494ojni8V6sl1LHJcLdyB5IxhWOTyB2pgZNA6j6il\n7Uh7UgPsi2hxaxj+IopP5CrqKoUL71XsiDa2xPJMEZOf90VOh5bHHzcVQhkwDSNjriqlhZzW+tT3\nL3cksc7KUibGIsADA+pGauT/AHgPWliGbqMZPBFAG1RRRQAUUUlABXEeOviVpPguAxSE3OpOP3Vt\nHzj3Y/wj9a5P4m/F5NHafRfD06PqC5Wa5GCsJ7gerfyrjfAnwr1jxe41TxCZYNOlbzA0p/ez8+nY\ne5/Cpb7FJLqULLSPF/xb199SY7YYm5mZiscOD91PU/5NbV74XufBHgDWdP1WeAXd0wmjEbEhhzxn\nA54r6A0/TrTS7GGysYEgtoVCpGgwAKra9bR3Oh3sckaPuhcYYA9jRYpT1PnDS9ROieEtMuRbpJHM\nwiM/IZRuyfr1I5rrYfhLcXthd3+d63MxaKDIDKmflYHoeOcGuduVdPh/oNlcnbKylRHjkIHbB/8A\nHq990i4aGJLVo3RUxGpYYB4HP41gkpyafQ7Z89GlCa69PQ8l8Hahrfg/Urm1uphJZK5XywxKls9R\n/dPscV1Xir4p6NZeF9SNndr/AGokexLdgchm4B9CPoa2vEXg+3vr5NVtneGQHNzGgyJ07jHrXzL4\nzXS5fFl0NHluHtnfAWeMq0Z7jnqPeop0qtOo25e727Bia9GvTU1G0+vZmY7fZ9GQFiZ71zJIT12A\n8fm2T+FZ9W9TlSXUJBH/AKuMCJMdMKMZ/PJqpXUee9wooooEFFFFAG74T8KX3jDWV06yIQYLSzP0\njX/H2q/4iuLK00IaNA/+nQ3rLcBISgKKoCnPf5i1ZWi+K9a8Nw3C6PfG1E5BkKAEsVzjn8a9w0Tw\n94CufCmlaz4pSzS91FSWluJihlfJ6c01JxKcYyRxmkfE/TNP8JXNvc21xPrV5CLaaZFACxqCFO4n\nnr04rjrPXn/4R97BD9na3nS5SaNf3mRuHXuPmzg969H8ZeEvD9l4mtIdHtdKhsoIllnWSTd9o3Pj\nb14wDmuvstC+FF5rraFaQafJqQUt5SMxB4zgHoT3wDmnGtdtb2B0nFJvqeO6X8StQ0XT7iwtVeS1\nuoz5iSNgpLn/AFikevcGuNubma8uJLm5leSVzlmc5J/GvRPCng6w8UeKdU0p4lsltN7A7CXcb9vy\njv8AXtVDxJpejaBr1xYReVeWtuwSadeDGxz8pHrx2rJzlfY644aEt6qXyl/kcLWhD5RtLuKZLhr5\no1ECBCfm3Lkn0+UNXR3Gmpp1vbavb2S3ViUWdpU5WP5uAx/hOfWvYdCTwzcwtrdzawQ3/lki4klw\nxGMYz364qr3Whn9Xklf+tTgdH8VWenfDLVNCtdMuJZrlQzL5ZwBkByfT6V51fwW8hd9NguHtkYky\nNERtXjGfTvXqN7qXhiHx99peeSxs5ldr2e3mZw5KnauAOAWOcisma60yw+GOn2tpb3Jl1G/dHuGI\nRZgrDII7rtOKlXSuyXBN8pyui6x/ZdobW4cG3vQYpVxkhGHX2wcGoZvEOsWUa6f9rR4bUGKI7FOB\nn1rVa1sLSFpJII0QDJPtXLCVBdN5WI0kfALDOxc1Ua6rqzW3U6MZl08C1zSV5dF+f9dizLfajrcl\ntZviaQMREqKAWJ/n+NVRbos0kV07QbCVbABw3pXdSeGdL0C9sNUsdatL+FZNs8UuYypwAQAfvcnP\nHavaoPCFlLbR7tIgQMg+dgMZx9ea462KdNpQi36d+xlCimrzdjzDwBr3h3wnp8lxaXyXWsu27ZIh\nhRlx9zdg/niui1HUrHW7mzv7nXRPc2UMt3OqxZSMAE/Jnqvbrmu2t/D/AIanNvC9q1tcyZWJJY9j\nPjqcVy3jRNP0K0kEbRh7Z1eSJIsllJHGO/Ws442tCrTU4aSdr3Tt66GqoU5ppNppX+X9eZ8/X0Tw\nylCCEcCUbgM4bnJx069Kq17pr/iWz1LwmbG7TTrGG+BjF6+XDMgyykAZVwQBz3rxfMl3GyRQbn/i\nYcZHavQcle6OOMHJeZTooII68UUyQooooEFei/CTxW+haveaa9wYra+iJBwSFdQcH24PX2rzqprS\n4ezvIblPvxOGH59PyoGtz1iKW5HhvW9Yg1mW5k06/iuFnkOGCbQoOAOTzj6V0/xIu9bvvhz/AG1J\nfpHZXVvCDZxpkFyMklj0HtVPw1caIvw+8XXSWtxeWk0oNzEAIyxYD5Y/YZ78103jfSni+ClzbO6y\neRArxlBgBAflHuQpAz3pRvYubVz5013/AJC0h9QD+lQQKWtL1FGf3Yb8AQf6VY14Y1NvQop/SrFj\nbKNJvLn+Ixyxn6eWT/SmQYVFFFAgU4ZT6MD+tfb+jNv0Owb1t4//AEEV8Pt0Nfa3hV/M8KaW2c5t\nk5/Cmhmx2rjvHuoXjWVv4f0okajq7NCrr/yxiA/eSH0wCB9TXWzSpDE0jnaigsT6Cuf0CxkudRuv\nEF2hSe6URQI3WOFTwPq3U0PsOOmpb0Hw7YaDpVtYW0KbIFxuIGWY9W+pqK8cWvjHTmzgXdvJEfco\nQw/9CNb3FYXicrbWtrqTDP2K4SQ/7pO0/wAxRawrtu7M/wAVeLh4evrOwudOklh1HfFFMjjG/aTg\nr1x718ua/bG01m9gP3o5WU/nX0D8Yrl7TT9JuoRtniucxSkAgEjaePoTXh/jXY/iCWdOUnRZQfcg\nZ/UGpvrYtx926OVPBoyKU0wjFMgdSUgJFLnNACGkpWpKAADccV3vws8LjxL41to5rfzbK2BluA3T\nb2B9cnArhEr3H4ATrHq+p2oPzSWolYfRgB/6EaAOa+KfwwbwjIdX059+kzzbfLP3oGPIHuOv0rzL\npX2H8RdLGr+AdZtQoZxbtImRnBX5s/pXx4Dkc0MApyOY2DL1puKMYoA2NLktrm9RJWELNxuJ4zXs\nXg60SMRkXaYIAIHUjivG4tJuYJonltnwQGAIIBHY10dlaX8rKAZIsEDCkggZoA+hraJVZWU7hnoT\nmtMOQuNuAO1ed+EbC+spTJJdTtGD0cn5jXdqstxgnIT0FUB5z8Ux5dvLeMQSqeUCRngnpXkTxq4E\no2BYwFf2Hrj0zXqXxXvRPGbG2y0NswErdRvP+ArypZEiIYneGUqwfkfl9aTAjkYmTecjJ6AcZpkO\n+FFZGC54znPFKk83l/ujJkja4zk5FKzYGHlJIIJHJIPpikBGWO4FcSHqRjGDn9acQsZJRcNngYxi\nhpIgCMnawyGwMf8A1qQBnwzYAHAA9KAHZdQu7ewYZByMU5RG2XVeOpJPXmpI3ZQiRsFyvQd+fWor\nj5iCxx6dD3NAD5FJkOcqpA4cY/X0pDEHjVUAY84C9etRspGAOU7ckfp2ofezb2IwwABBAoAQ7CuA\nA2fXgimqVgJDAZ/iB6VKkZBVn3mPGSGOce/0prOkYCKc5PDFR/OgBwfNuUOzYxJDZwM46VXKOikO\nuIzjaeuT9acxVpMIRHk4yOB/9aozlm3PKSqggEHI70APidmMyNyFtmA/Na9v+BDk+Eb5fS6H8jXh\n9uVLThDuH2ZiT05yte1/AVs+GtST+7cqf0NNbgesK2WxWhH/AKsVlpkyZrUi/wBWKYh9FFFABRRR\nQAUUUUAFcV8WJ1g+G+rbjjegQfUkV2tedfGyXZ8Op484824iXP0O7+lAHy8/WmU9+pptSMbikC5p\n4FSJH60ANj6YPWu08P8AxD1jQdPNgs7m3zlWABMf0zXHBSJOPrinMR/9asqtGnVjyzV0aQqSg7xP\npv4eahY+JNMM8V809zwbhW6r6Cu5sdMs9PVxbQLGXOXYDlj7nvXnnwN0gWHgp7xh+8vJixPsOB/W\nvT6yw2BoYfWmte46tedR3fXoLRRRXYYhXifx/wBE82z07V0H+rLQucdzgr/JhXtlcX8VbBb/AOHm\np7vvQIJl+oNAHyQOamhALc1G64kI75qSHl8HrUjNa2YIPb1rTjA8oMpwf5Gse3Y8L784rVDFYvl5\nPoaBlXUlw0Lk7jznP4V6/wDAO3+XWbjoR5afXqf6V43dlzHEzqBgkCvevgXCq+Fr2Yfee5wT7Acf\nzNNCZ6pRRRTEFFFFABXnHxu0w6h8N7uRFzJbSJKD6DIB/Q16PWP4p08ar4W1Oy7y27hfrjj9aAPH\nvhhb/wBpeN9K1UcMdMMjn1IJjx+fP4V7zXgHwSu2S6tIHKboZJ7U5PIBAcAfiDXv9JDYUUUUxBRR\nRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4x+0DcINK0q3/AIzKzH1xg/1r5/K4PqOxr3b9oO1c\nppV0CdmGix2zya8J5BxUsY4CndKQUE80ASxHJ9qsKBz61DbjNWQvOKBkicLzUykAAAfjSRoMEk/h\nTjhcMOuKAIkTzr2KPrt5/GvqP4e6L/YvhG1jZAs02ZZOMHJ6D8q+efBGlNrPiu1tyMh5Bkew5r6u\nRVRFReAoAA9qaEzgPjO5T4c3YB+9Ki/z/wAK+W+mK+qPjDbtcfDq92jPlurn6cj+tfK7nGBQwFwD\nTsYpgXdyDg0vzr15pATRyshHtV+3lEhHrWcGBHIq1aKfNUg8Z5oA6K2wV2nBGOKsoCcY7VUhI2gi\nrsZB4x7mmMswDEqn3r3/AMFW/wBn8NQL2Ylh9K8FtIw8kY7bua+kNKRI9JtFQYURLjH0oEy5RRRT\nEFFFFABXiX7Qh/0TSPrJ/IV7bXiP7Qv/AB76P/20/kKAR4/4W0ltb1u20xeftEgjz7EivsWwtI9P\n0+2s4v8AVwRLEn0UACvlf4TqG8faZ6iYH9a+saSGwooopiCiiigAooooAKKKKACiiigAooooAKKK\nKACiiigAooooAKKKKACiiigArzL4m/DS58ZXNve6fcQxXMSFHWbIDjt0716bRQB8qeIfhNr/AIc0\ndtVvJbM2y43BGYsufbH9a4gW5P3ZYvxYivrvx7o1zr3g++sLNA9xIuUUkDJr5xv/AIb+J7VgG0a5\nKscAKM85pNDOU+xXQjEixFkJIDKQQTUbW9wOWhkx67SRXq3h74Ga/fxxPqtzFp9ufm8sMWkwfYcA\n/U16dafDLw14f0K7zaTX7rAxYysGZiAfu9gaVgPlg71BRhgdwRTNoPavSPAOhprPj0wXWizvpsjS\nERzZ/dDJxk45OK9suvhP4Nu+TpEcZ6fujiiwHyXtHcUYXvkV9Iav8CtAmRmsJbiAgcAsDz+XSvMv\nF3wxfwtpcmpPeiSBZBHhRk5Of8KLAefLGD0OfakZMNtH41ahgt5GwLoRnt5ikDP1pvkqjHcd59ex\nNACMPK04DvNIT+Cgf1Y1o6Igi0fWbo/e8uO3Qepduf0WqF+RuhjHASMAj3JJ/qK9T8B/DO58S+Eb\naZ547e1mumlkY5LsANoA/nzQB5ihmUYjyvb5RzTp9Mv1hW5ntp44nO1ZplKox+p6/hX1Nofwy8N6\nIFYWYupVx884Dc14/wDHjW47nxFZ6FbbFtrCPe6oAB5je30FOwHnNhZ21xqMNs0jzl2+YRcKB9e/\n5Cvon4XeGtPsxJfQWUabV2q5GST35NeE+DrQvdSTkZJxGn1zz/IV9W+GdP8A7N0C1hIwxUM2PUih\nAGt+F9G8Q2rW+o2EMqno4UBgfUGvEfFnwP1HToZrrQ5ft0QYbYMYlAz+R/Svoeo5ZFiieR+FRSxP\ntimI+HLiCW1uJIbhHilRtrI+QQfTFMBb+8eevJru/GfjubWtWv2h07Too5cxCQW4Mu3pndnr71wp\nIqRk95Fs0uzlHRvMX8QRXQeJH8xblwchobSTPp8pFYdw5k0GBP8AnnOwH4gn+lbV6RPpxc/xaZE3\n12uB/WgDmlXPJ6U8/pSZz06UDk0DO5+Fyg+OdIBGf36n9a+rTXy18JYTJ4704/3HB/SvXPjF4nbQ\n/DItba5MV3dNgBCQ2zuQe1NCPIPidqSah491G5Ns9vJAhTOc+YAoAOK4mUBNAskwctLIx/SkluZL\niO4lmleRyACzkknJ5ovTtstPjz0hLn6kmkBU4A460o3kYA/GkBHenCTsooAcsLnqQB61KiRp/tN+\ndMUO/U7RVm3TdIqRruduBQBraDpUuq6nBbKCWdgoQdetfWmh6VFoujWunwgBYUAJHc9/1rzT4Q+D\nEtrca3dqryNkQnqO+TXrtNAwooopiCvm/wCJjfa/jOsY52i3j/X/AOvX0hXzX4g/0746Sp1xexJ+\nQFJjR9IQrshjX0UD9KwNJP8AxWniAf7Fv/6Ca6IVz+mps8Za42T88Vu2O3RqYi/rYxp/mYz5UiSf\nkwrz74j+A57uRPE/h4+Rqtvh5FjyDKOOR7/zFeh60caLeH0iNXIcmCPP90Z/KgDjfh/46h8W6eYL\nkLDq1sMTwnjdz94D0/ka7WvIfiF4RvdC1FfGXhf93NCd11CnAI9QB1HqPxrt/BPjKz8Y6MLqDMdz\nFhLiBuqN6+4PY0AdRVPVRnR70etu/wD6CauZqrqIzpl2PWFx/wCOmgD5w+D4A+J1p/1znH/jpr6Z\nr5m+EnHxQtB/szj/AMdNfTNJDYV5h8cNFN/4MTUUGZNPmDn/AHGwp/XbXp9ZfiLS11vw5qOmEKTc\n27xru6BiPlP54NMR5d8BdZEljqWiuSXif7Qg7BTgH9a9lr5c+GOqP4f+JFlDJkLcSGzkHQZJwD+Y\nr6jpIbCuZ8RbdN1fStd+bYkos58Yx5cpCgn2D7D9K6aqGsabHrGkXenynC3ETRhu6kjhvqDzTEX6\nKxvDGpy6t4ftbm5Xy7sAxXCEY2yKSrD6ZBrZoAKKKKACiiigAooooAKKKKACiiigApkkayxtG6hk\nYEMD0Ip9FAHy7400q58NeMb2G1zHtJlgx0KEk7fpjNdJ4b1qHVbfehRLhSA0Z4K8Y/EV0Pxr0YCL\nT9dQHMb/AGeUDupBx+vFeU6Tb3FjqEV1ayyFHO1j0LL6fmBSGeqTW6GQHKEA/KGzgHHWuR8RqGYM\nvMiY3EnJJHtXUWtyl4BkZdV5PYfhXP67uUvgDJGBgYBP+NAzznxFH5d4koUKZU3HA4zWOCBgkdDm\nuh16Q3WmRPj5rdzGxwOQelc6funjnHWkIhmx58hAwCxIFMp8pzIT60ygBKWkpaAClFJThQB9C/s+\n6OIdA1DV2GHuZ/KXP91QP6mux+HgFpHr+kgFVsdWnVFPZHO9fw+aq3wagEPww0sgYMhkc/i7VoWL\nHT/iVqdrgCPULSO7Xt8y/If0GaYjrqKKKYBRRRQAV8c/EWZZ/iJrrqcr9qK5+lfYpOATXxR4jna8\n8U6rN1aS6f8APJpMCrbD/RbnnIZBwOvUVXXkdPoKmtQRHdKe0fI/EVErExgHovAxSGL70ZPTNJx/\nnFH4UABzTadSUAJRRRQAUUfh+FFABSUUUAe1/Bqy8rRm1GG11SSRr4QyG0udqMvH34+4Hc9a86+I\nGkT6N431SGW0+yxyzvLAnGChJwR7V3Pwie0u9NFhJNqNjcpqCTpd2pcLOAM+S2OOQD1615/401Zt\nb8Z6rfSC4VHuGCRzE740zwOen07UwMEUxuBmngE8d6Y/3TSA+yNMffptm443W0Z/8dFXY8biPxqh\no3z6RYD0tYf/AEBauA4DNVCHXA5J9OlR2znzoyTk8Us7bsjuR/SmWn+tj7jigDoKKKKAEry34tfE\nhfC2nNpWlyg6vcLyVIJt0OPmI9T2ro/H/jm08EaC105WS+lyttb55dvXHoO9eA+BvDN18QfFrX+t\nSSy2rSlp5DnM74J2g+nH4Ck2ikmdL8IPhqursniPW4y9uH3W0L5zIc/fb1H1619DKiooVQAoGAB0\nAqK1tYrO3SCGNI40UKFUYAAHT6VYoQmxO9VNU/5BV3/1xf8Akat96q6kN2m3S+sTD9DTBbnh/iOG\nG903wlfQrtSWwTdjpuXA/nXszKf+JfOTlQAGA6EkDBryGGNp/hf4YmADPF5sTEDPSRhj9K9lgj8z\nS4B/0yUg/gK5KT/fVF6HpYqV8NRf+JfiWzjBJ9M18c+I76LVfH+t6lAAIPtMsiA4A2g4FfV3iTUl\n0zwnqt677DDbSEH32nH8xXxxaqzabfXDHn5FJPUlia6XsedHcpc4yep5NLRRQSFFFFAwNWbBoI76\nGW6t3ntkcGSNTjcPTPaq1TW9wbck7EcNjIcZHBpSV1YcbX1JtZure+1S7ubW2FrbyN+7hXGEGOle\nvT6Zpj/Dbw1rGoRGdPIksjDnn5izBl9CCvX0rxi4lNxPJKVCFyThegruD4ss77w94e0KeYwQ2DO0\nrnOCWPXHcgZ/OsKkWqagr9vT5nVhuR1rzatfr2MMaBLc6hd29kJ7vbGJXkUkLbrnrIT7e45r234X\neH9CstUMVqsU1zawLcLc9Xl3gjj0C/MMf7QrzybU/Cdir22ia9cW6XmBfTSxFjImOgAHBBrP0Lxu\nPCOrzPYTvdxR8QzKCPMXH3WB7Zx+VDjPRvWxq1RvKzte9utvI9T+Ln2i707T5rC0ubFobos9+AI3\nUYIKKMhmJ69MYGa8Nl0+O/1aC2sriWe5nYmVpwRl+cn3GB1r0Xxh8YrfXfDX9m21iDeSgM87DKxN\nj+AdQ3OM15PaXctpeRXKMS6NnJPJHfmt0m3qcd4pWf8AXodfN4tm0vwzqWhpCnm3GIZQwyoQD7w9\n8iup07RtL1wWEuoa59iYQkLG8BffwOcjvxXm+va3/bLK32cR7MhWwASMdKuXOvRyx2iwz+Q0MR3G\nNSAx4+X9OtVJKOidzeOIVpXvra3yOj8QXWoTC70exiEmk2UwVb141hVnAyS27GTjOFJ+lT6/b+EL\nvwev9i3ssl1agtB5mQWORuOO2Sf0rqtE+LXg3TNCjtW0+QShBvjWDKM2f19c1w2va74LuzeXOmW2\noRXM8DxGMfKu8kbX+ntWd1tqc3tFe7OQQ3N9dxw3CtLPIyRRRsdoJJAH860Nd8P3HhnUWtr1Yw7A\nqrRsHCtnnPp+NM1N/DraTZDTJL9dQjI88zD5H45K+mDVLU9Yv9WMf267luRENqGUkkCnFyuuXRFS\nd7ubu+5tL4bs28RadYx6xHLb3DlXlBAETgZI6kY9xXo17f6TpE+mNc6teCOG5QNL5jukeDwxHfkd\nq8UgmMEqyIMsvQHkVbu9VuL2GOOaQlEGAvauLEYWdWpFub5Vulpc66GJp06clbV7H1Xp3i3w7HpU\nZi1mxvnU5aSMgYJP3iP4evU4rgdV0CPWb+6s/EGoRwX99IJGh80KYYuo56ehxXl1r41ax0L+y7Wy\njiUkM0wAJlI6Z9s1Wk8Si91G4utQa7c3A+d1OW/XtSjQqzqXlolt8u/9dDT21GnTlyu7kte/y/X5\nHSXHg640zw/cadq0AjWK/VYtQllxEC5A3YGSRjByKxfEmmW0cAudMDsbVzbahcRyBomlBI3IeykY\nqPUfFcd5pB0xRdm3Ztx8xuvpxUtp4n0iz8Ean4fXTbiSa+cSefIR8jDGP5V6kkkrJ3PPdt0YWp27\nIsE2BtMSKfris+tO41SK4s3g8lwSiqCccEVmVhC9rM1xPs/aXg73/r/ghRRRVnOFFFFAj1XwIPtn\nw31m0upk+yz3UcUQiGZY5sjDY4yuPevbfGVm5+Gmp2n32WxK5HfAH+FeE/CSd5l1bTB826S3uUjP\ncq3P6V9H66R/wjeolhx9kkJH/ATQt7lPY+ONc/4/UJ6mJP5CtSyjx4Wu5AP4pF/OFqz9clLXcLkA\nOYVHQYxj+dWtOvEXQby1LDzD5ku328ph/M0COdpBydo5Y9AOT+VFfRvwHTSL/wAJTZ0+0+32tyUk\nkKKZHBAIY8ZA5I/A0wPE9M8CeK9aH+geH7+RCuRJJEY0I/3nwD+Br6q8DWd/p3gvTLPU4TDeQxFJ\nEJBI5PoSOldEAFGFGB6Diuf8U+IINF0W+n+0Ij28JdzkZQY4/E9qG0tWCTeiJrqVtTuntlJS1iOJ\ncjBc56fT36VsKAEAAAGMADpivIPhd4b1+fxJfeKNcuJXiubfbbEyZEquc5+gAGK9XNm2ciVh6cmk\nm9ypJJ2LearX1st5Yz27gEOhUZ9a5Hx34c8QazogttF1DybjzQ27zTHxjpkV5vD4I+K9lPEz6xcS\nwAgOsd8ScewNHMHKu56Nro0rWvh6uoalAJltY96hic+Yh29vUrjpXzx4glF9p1hdhUQ/vIiFOcfM\nW/DhxXsk1zqOhaH4hstakhtoFQSIzEbd0jYyD6HHT1rzXWzY3Xw809re4t5bmC6cSiJgWAIGCR6c\nVm2+exsopUn6nnp4JpDTn+8abWhzjTSUpptADs5oIpKUGgBycnHpzXr37PxJ8aapn/oH/wDtRK8f\nzggjtXsH7PrA+M9Sx/Fp5OP+2if40ID6ImiWeCSFxlXUqR7EV8T67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szPGWI80uyL75PFfRfizw/b6vpbatpk4jlKiUzwZPmR4zkY68fnXz94r15lkuLC2kJQDy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\nf6hyT27KfX1qn8MPiDJ4L1sRXTO2kXRxcIOfLPZ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2SgDoaK53f4wIx9n0NT6i4lP/ALIK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\nE2i36Y/6YE1RktLqH/WWlzH/AL0LCgBhhccFD+GDTTE4yChHrxTDIBw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vQ9F+B3hbT1Vr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\nf7VtpRssbtiA6v2V/X61c8JeF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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 416,
              "height": 416
            }
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gA8R196hGgSI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}