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| a | b/landmark_extraction/static_landmarks+extraction.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 2, |
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| 6 | "metadata": {}, |
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| 7 | "outputs": [], |
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| 8 | "source": [ |
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| 9 | "import cv2\n", |
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| 10 | "import matplotlib.pyplot as plt\n", |
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| 11 | "import numpy as np\n", |
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| 12 | "import os\n", |
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| 13 | "import urllib.request\n", |
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| 14 | "import sys\n", |
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| 15 | "import torch\n", |
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| 16 | "import time\n", |
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| 17 | "import datetime\n", |
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| 18 | "\n", |
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| 19 | "from torchvision import transforms\n", |
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| 20 | "from PIL import Image\n", |
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| 21 | "\n", |
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| 22 | "%matplotlib inline" |
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| 23 | ] |
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| 24 | }, |
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| 25 | { |
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| 26 | "cell_type": "code", |
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| 27 | "execution_count": 3, |
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| 28 | "metadata": {}, |
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| 29 | "outputs": [], |
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| 30 | "source": [ |
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| 31 | "YOLOV7_MODEL = [\n", |
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| 32 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt\",\n", |
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| 33 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt\",\n", |
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| 34 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt\",\n", |
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| 35 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt\",\n", |
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| 36 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt\",\n", |
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| 37 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt\",\n", |
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| 38 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt\",\n", |
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| 39 | " \"https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt\",\n", |
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| 40 | "]" |
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| 41 | ] |
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| 42 | }, |
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| 43 | { |
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| 44 | "cell_type": "code", |
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| 45 | "execution_count": 4, |
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| 46 | "metadata": {}, |
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| 47 | "outputs": [], |
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| 48 | "source": [ |
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| 49 | "def get_yolov7_model(modelistid=1):\n", |
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| 50 | " \"\"\"\n", |
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| 51 | " Download YoloV7 model from a yoloV7 model list\n", |
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| 52 | " \"\"\"\n", |
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| 53 | " modelid = YOLOV7_MODEL[modelistid]\n", |
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| 54 | "\n", |
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| 55 | " if not os.path.exists(modelid):\n", |
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| 56 | " print(\"Downloading the model:\",\n", |
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| 57 | " os.path.basename(modelid), \"from:\", modelid)\n", |
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| 58 | " urllib.request.urlretrieve(modelid, \n", |
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| 59 | " filename=os.path.basename(modelid))\n", |
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| 60 | " print(\"Done\\n\")\n", |
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| 61 | " !ls yolo*.pt -lh\n", |
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| 62 | "\n", |
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| 63 | " if os.path.exists(modelid):\n", |
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| 64 | " print(\"Downloaded model files:\")\n", |
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| 65 | " !ls yolo*.pt -lh" |
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| 66 | ] |
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| 67 | }, |
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| 68 | { |
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| 69 | "cell_type": "code", |
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| 70 | "execution_count": 5, |
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| 71 | "metadata": {}, |
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| 72 | "outputs": [], |
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| 73 | "source": [ |
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| 74 | "from utils.datasets import letterbox\n", |
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| 75 | "from utils.general import non_max_suppression_kpt\n", |
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| 76 | "from utils.plots import output_to_keypoint, plot_skeleton_kpts" |
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| 77 | ] |
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| 78 | }, |
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| 79 | { |
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| 80 | "cell_type": "code", |
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| 81 | "execution_count": 6, |
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| 82 | "metadata": {}, |
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| 83 | "outputs": [], |
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| 84 | "source": [ |
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| 85 | "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" |
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| 86 | ] |
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| 87 | }, |
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| 88 | { |
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| 89 | "cell_type": "code", |
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| 90 | "execution_count": 8, |
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| 91 | "metadata": {}, |
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| 92 | "outputs": [], |
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| 93 | "source": [ |
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| 94 | "def image_view(imagefile, w=15, h=10):\n", |
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| 95 | " \"\"\"\n", |
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| 96 | " Displaying an image from an image file\n", |
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| 97 | " \"\"\"\n", |
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| 98 | " %matplotlib inline\n", |
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| 99 | " plt.figure(figsize=(w, h))\n", |
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| 100 | " plt.axis('off')\n", |
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| 101 | " plt.imshow(cv2.cvtColor(cv2.imread(imagefile), \n", |
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| 102 | " cv2.COLOR_BGR2RGB))" |
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| 103 | ] |
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| 104 | }, |
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| 105 | { |
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| 106 | "cell_type": "code", |
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| 107 | "execution_count": 23, |
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| 108 | "metadata": {}, |
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| 109 | "outputs": [], |
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| 110 | "source": [ |
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| 111 | "def running_inference(image):\n", |
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| 112 | " global model\n", |
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| 113 | " \"\"\"\n", |
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| 114 | " Running yolov7 model inference\n", |
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| 115 | " \"\"\"\n", |
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| 116 | " image = letterbox(image, 960, \n", |
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| 117 | " stride=64,\n", |
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| 118 | " auto=True)[0] # shape: (567, 960, 3)\n", |
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| 119 | " image = transforms.ToTensor()(image) # torch.Size([3, 567, 960])\n", |
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| 120 | "\n", |
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| 121 | " if torch.cuda.is_available():\n", |
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| 122 | " image = image.half().to(device)\n", |
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| 123 | "\n", |
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| 124 | " image = image.unsqueeze(0) # torch.Size([1, 3, 567, 960])\n", |
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| 125 | "\n", |
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| 126 | " with torch.no_grad():\n", |
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| 127 | " output, _ = model(image)\n", |
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| 128 | "\n", |
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| 129 | " return output, image" |
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| 130 | ] |
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| 131 | }, |
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| 132 | { |
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| 133 | "cell_type": "code", |
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| 134 | "execution_count": 15, |
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| 135 | "metadata": {}, |
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| 136 | "outputs": [], |
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| 137 | "source": [ |
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| 138 | "def loading_yolov7_model(yolomodel):\n", |
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| 139 | " \"\"\"\n", |
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| 140 | " Loading yolov7 model\n", |
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| 141 | " \"\"\"\n", |
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| 142 | " print(\"Loading model:\", yolomodel)\n", |
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| 143 | " model = torch.load(yolomodel, map_location=device)['model']\n", |
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| 144 | " model.float().eval()\n", |
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| 145 | "\n", |
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| 146 | " if torch.cuda.is_available():\n", |
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| 147 | " # half() turns predictions into float16 tensors\n", |
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| 148 | " # which significantly lowers inference time\n", |
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| 149 | " model.half().to(device)\n", |
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| 150 | "\n", |
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| 151 | " return model, yolomodel" |
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| 152 | ] |
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| 153 | }, |
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| 154 | { |
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| 155 | "cell_type": "code", |
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| 156 | "execution_count": 18, |
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| 157 | "metadata": {}, |
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| 158 | "outputs": [ |
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| 159 | { |
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| 160 | "name": "stdout", |
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| 161 | "output_type": "stream", |
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| 162 | "text": [ |
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| 163 | "Downloading the model: yolov7-tiny.pt from: https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt\n", |
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| 164 | "Done\n", |
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| 165 | "\n", |
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| 166 | "-rw-r--r-- 1 g g 13M Aug 16 21:30 yolov7-tiny.pt\n" |
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| 167 | ] |
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| 168 | } |
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| 169 | ], |
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| 170 | "source": [ |
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| 171 | "model = get_yolov7_model(0)\n" |
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| 172 | ] |
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| 173 | }, |
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| 174 | { |
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| 175 | "cell_type": "code", |
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| 176 | "execution_count": 20, |
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| 177 | "metadata": {}, |
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| 178 | "outputs": [ |
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| 179 | { |
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| 180 | "name": "stdout", |
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| 181 | "output_type": "stream", |
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| 182 | "text": [ |
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| 183 | "Loading the model...\n", |
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| 184 | "Loading model: yolov7-tiny.pt\n", |
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| 185 | "Using the yolov7-tiny.pt model\n", |
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| 186 | "Done\n" |
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| 187 | ] |
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| 188 | } |
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| 189 | ], |
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| 190 | "source": [ |
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| 191 | "YOLOV7MODEL = os.path.basename(YOLOV7_MODEL[0])\n", |
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| 192 | "\n", |
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| 193 | "try:\n", |
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| 194 | " print(\"Loading the model...\")\n", |
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| 195 | " model, yolomodel = loading_yolov7_model(yolomodel=YOLOV7MODEL)\n", |
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| 196 | " print(\"Using the\", YOLOV7MODEL, \"model\")\n", |
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| 197 | " print(\"Done\")\n", |
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| 198 | "\n", |
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| 199 | "except:\n", |
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| 200 | " print(\"[Error] Cannot load the model\", YOLOV7MODEL)" |
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| 201 | ] |
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| 202 | }, |
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| 203 | { |
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| 204 | "cell_type": "code", |
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| 205 | "execution_count": 21, |
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| 206 | "metadata": {}, |
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| 207 | "outputs": [], |
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| 208 | "source": [ |
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| 209 | "def draw_keypoints(output, image, confidence=0.25, threshold=0.65):\n", |
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| 210 | " \"\"\"\n", |
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| 211 | " Draw YoloV7 pose keypoints\n", |
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| 212 | " \"\"\"\n", |
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| 213 | " output = non_max_suppression_kpt(\n", |
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| 214 | " output,\n", |
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| 215 | " confidence, # Confidence Threshold\n", |
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| 216 | " threshold, # IoU Threshold\n", |
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| 217 | " nc=model.yaml['nc'], # Number of Classes\n", |
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| 218 | " nkpt=model.yaml['nkpt'], # Number of Keypoints\n", |
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| 219 | " kpt_label=True)\n", |
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| 220 | "\n", |
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| 221 | " with torch.no_grad():\n", |
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| 222 | " output = output_to_keypoint(output)\n", |
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| 223 | "\n", |
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| 224 | " nimg = image[0].permute(1, 2, 0) * 255\n", |
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| 225 | " nimg = cv2.cvtColor(nimg.cpu().numpy().astype(np.uint8), cv2.COLOR_RGB2BGR)\n", |
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| 226 | "\n", |
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| 227 | " for idx in range(output.shape[0]):\n", |
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| 228 | " plot_skeleton_kpts(nimg, output[idx, 7:].T, 3)\n", |
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| 229 | "\n", |
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| 230 | " return nimg" |
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| 231 | ] |
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| 232 | }, |
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| 233 | { |
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| 234 | "cell_type": "code", |
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| 235 | "execution_count": 25, |
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| 236 | "metadata": {}, |
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| 237 | "outputs": [ |
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| 238 | { |
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| 239 | "name": "stdout", |
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| 240 | "output_type": "stream", |
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| 241 | "text": [ |
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| 242 | "True\n" |
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| 243 | ] |
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| 244 | }, |
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| 245 | { |
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| 246 | "ename": "AttributeError", |
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| 247 | "evalue": "'Upsample' object has no attribute 'recompute_scale_factor'", |
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| 248 | "output_type": "error", |
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| 249 | "traceback": [ |
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| 250 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", |
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| 251 | "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", |
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| 252 | "Cell \u001b[0;32mIn[25], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m imagefile \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m../assets/messi.jpg\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[39mprint\u001b[39m(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(imagefile))\n\u001b[0;32m----> 3\u001b[0m output, image \u001b[39m=\u001b[39m running_inference(cv2\u001b[39m.\u001b[39;49mimread(imagefile))\n\u001b[1;32m 4\u001b[0m pose_image \u001b[39m=\u001b[39m draw_keypoints(output, image, confidence\u001b[39m=\u001b[39m\u001b[39m0.25\u001b[39m, threshold\u001b[39m=\u001b[39m\u001b[39m0.65\u001b[39m)\n\u001b[1;32m 6\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m30\u001b[39m, \u001b[39m7\u001b[39m))\n", |
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| 253 | "Cell \u001b[0;32mIn[23], line 17\u001b[0m, in \u001b[0;36mrunning_inference\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m 14\u001b[0m image \u001b[39m=\u001b[39m image\u001b[39m.\u001b[39munsqueeze(\u001b[39m0\u001b[39m) \u001b[39m# torch.Size([1, 3, 567, 960])\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m---> 17\u001b[0m output, _ \u001b[39m=\u001b[39m model(image)\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m output, image\n", |
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| 254 | "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", |
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| 255 | "File \u001b[0;32m~/Documents/autoposture/repo/landmark_extraction_yolo/models/yolo.py:514\u001b[0m, in \u001b[0;36mModel.forward\u001b[0;34m(self, x, augment, profile)\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39mcat(y, \u001b[39m1\u001b[39m), \u001b[39mNone\u001b[39;00m \u001b[39m# augmented inference, train\u001b[39;00m\n\u001b[1;32m 513\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 514\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mforward_once(x, profile)\n", |
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| 256 | "File \u001b[0;32m~/Documents/autoposture/repo/landmark_extraction_yolo/models/yolo.py:540\u001b[0m, in \u001b[0;36mModel.forward_once\u001b[0;34m(self, x, profile)\u001b[0m\n\u001b[1;32m 537\u001b[0m dt\u001b[39m.\u001b[39mappend((time_synchronized() \u001b[39m-\u001b[39m t) \u001b[39m*\u001b[39m \u001b[39m100\u001b[39m)\n\u001b[1;32m 538\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m%10.1f\u001b[39;00m\u001b[39m%10.0f\u001b[39;00m\u001b[39m%10.1f\u001b[39;00m\u001b[39mms \u001b[39m\u001b[39m%-40s\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m (o, m\u001b[39m.\u001b[39mnp, dt[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], m\u001b[39m.\u001b[39mtype))\n\u001b[0;32m--> 540\u001b[0m x \u001b[39m=\u001b[39m m(x) \u001b[39m# run\u001b[39;00m\n\u001b[1;32m 542\u001b[0m y\u001b[39m.\u001b[39mappend(x \u001b[39mif\u001b[39;00m m\u001b[39m.\u001b[39mi \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msave \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m) \u001b[39m# save output\u001b[39;00m\n\u001b[1;32m 544\u001b[0m \u001b[39mif\u001b[39;00m profile:\n", |
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| 257 | "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", |
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| 258 | "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/upsampling.py:157\u001b[0m, in \u001b[0;36mUpsample.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m 156\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39minterpolate(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msize, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscale_factor, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmode, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malign_corners,\n\u001b[0;32m--> 157\u001b[0m recompute_scale_factor\u001b[39m=\u001b[39m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrecompute_scale_factor)\n", |
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| 259 | "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/nn/modules/module.py:1614\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1612\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m modules:\n\u001b[1;32m 1613\u001b[0m \u001b[39mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1614\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 1615\u001b[0m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, name))\n", |
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| 260 | "\u001b[0;31mAttributeError\u001b[0m: 'Upsample' object has no attribute 'recompute_scale_factor'" |
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| 261 | ] |
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| 262 | } |
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| 263 | ], |
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| 264 | "source": [ |
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| 265 | "imagefile = \"../assets/messi.jpg\"\n", |
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| 266 | "output, image = running_inference(cv2.imread(imagefile))\n", |
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| 267 | "pose_image = draw_keypoints(output, image, confidence=0.25, threshold=0.65)\n", |
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| 268 | "\n", |
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| 269 | "plt.figure(figsize=(30, 7))\n", |
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| 270 | "plt.axis(\"off\")\n", |
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| 271 | "plt.imshow(pose_image)" |
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| 272 | ] |
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| 273 | }, |
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| 274 | { |
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| 275 | "cell_type": "code", |
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| 276 | "execution_count": null, |
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| 277 | "metadata": {}, |
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| 278 | "outputs": [], |
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| 279 | "source": [ |
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| 280 | "output, image = running_inference(cv2.imread(imagefile))\n", |
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| 281 | "pose_image = draw_keypoints(output, image, confidence=0.25, threshold=0.65)\n", |
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| 282 | "\n", |
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| 283 | "plt.figure(figsize=(30, 7))\n", |
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| 284 | "plt.axis(\"off\")\n", |
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| 285 | "plt.imshow(pose_image)" |
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| 286 | ] |
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| 287 | } |
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| 288 | ], |
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| 289 | "metadata": { |
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| 290 | "kernelspec": { |
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| 291 | "display_name": "Python 3", |
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| 292 | "language": "python", |
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| 293 | "name": "python3" |
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| 294 | }, |
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| 295 | "language_info": { |
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| 296 | "codemirror_mode": { |
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| 297 | "name": "ipython", |
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| 298 | "version": 3 |
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| 299 | }, |
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| 300 | "file_extension": ".py", |
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| 301 | "mimetype": "text/x-python", |
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| 302 | "name": "python", |
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| 303 | "nbconvert_exporter": "python", |
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| 304 | "pygments_lexer": "ipython3", |
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| 305 | "version": "3.11.3" |
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| 306 | }, |
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| 307 | "orig_nbformat": 4 |
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| 308 | }, |
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| 309 | "nbformat": 4, |
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| 310 | "nbformat_minor": 2 |
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| 311 | } |