File size: 8,473 Bytes
55500d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Tool\n",
"import json\n",
"import os\n",
"import random\n",
"import shutil\n",
"from tqdm import tqdm\n",
"import re\n",
"import pandas as pd\n",
"\n",
"\n",
"def load_jsonl(path):\n",
" datas = []\n",
" with open(path, 'r') as file:\n",
" for line in file:\n",
" data = json.loads(line)\n",
" datas.append(data)\n",
" return datas\n",
"\n",
"def load_jsonl_fromdir(res_dir):\n",
" res_name = sorted(os.listdir(res_dir))\n",
" res_paths = [os.path.join(res_dir, name) for name in res_name]\n",
"\n",
" datas = []\n",
" for path in res_paths:\n",
" datas.extend(load_jsonl(path))\n",
" return datas\n",
"\n",
"def load_json(path):\n",
" with open(path, 'r') as file:\n",
" datas = json.load(file)\n",
" return datas\n",
"\n",
"def save_json(datas, path, indent=4):\n",
" with open(path, 'w') as file:\n",
" json.dump(datas, file, indent=indent)\n",
"\n",
"def parse(generated_text):\n",
" generated_text = generated_text.strip()\n",
" if \"```json\" in generated_text:\n",
" generated_text = re.sub(r\"^```json\\s*|\\s*```$\", \"\", generated_text.strip())\n",
" try:\n",
" data = eval(generated_text)\n",
" except:\n",
" generated_text = generated_text.replace('\\'Q\\': \\'', \"\\\"Q\\\": \\\"\").replace('\\', \\'A\\': \\'', \"\\\", \\\"A\\\": \\\"\").replace('\\'}', \"\\\"}\")\n",
" data = eval(generated_text)\n",
"\n",
" return data\n",
"\n",
"def formating_conversations(data):\n",
" \n",
" question = data['Q']\n",
" options = data['Options']\n",
" answer = data['Answer']\n",
"\n",
" question_inp = question + '\\n' + '\\n'.join(options)\n",
" answer_inp = answer\n",
"\n",
" conversations = [\n",
" {\n",
" \"from\": \"human\",\n",
" \"value\": '<image>\\n' + question_inp\n",
" },\n",
" {\n",
" \"from\": \"gpt\",\n",
" \"value\": answer_inp\n",
" }\n",
" ]\n",
"\n",
" return conversations\n",
"\n",
"def time_to_seconds(time_str):\n",
" # Split the string by the dot to separate seconds and milliseconds\n",
" time_parts = time_str.split('.')\n",
" seconds = 0\n",
" \n",
" # If there are milliseconds, process them\n",
" if len(time_parts) == 2:\n",
" time_str = time_parts[0]\n",
" milliseconds = int(time_parts[1])\n",
" else:\n",
" time_str = time_parts[0]\n",
" milliseconds = 0\n",
"\n",
" # Split the time string by colon to get hours, minutes, and seconds\n",
" time_parts = time_str.split(':')\n",
" hours = int(time_parts[0])\n",
" minutes = int(time_parts[1])\n",
" seconds += float(time_parts[2])\n",
"\n",
" # Convert everything to seconds\n",
" total_seconds = hours * 3600 + minutes * 60 + seconds + milliseconds / 1000\n",
" return total_seconds\n",
"\n",
"def get_datas_from_df(df_path):\n",
" df = pd.read_csv(df_path)\n",
" datas = df.to_dict('records')\n",
" return datas\n",
"\n",
"def list_2_dict(datas, key='video_id'):\n",
" datas_dict = {}\n",
" for data in tqdm(datas, desc='list_2_dict'):\n",
" video_id = data['video_id']\n",
" if video_id not in datas_dict:\n",
" datas_dict[video_id] = [data]\n",
" else:\n",
" datas_dict[video_id].append(data)\n",
" \n",
" return datas_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'get_datas_from_df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/share/minghao/VideoProjects/Sythesis2/LongCaption/tmp.ipynb Cell 2\u001b[0m line \u001b[0;36m4\n\u001b[1;32m <a href='vscode-notebook-cell://dsw-gateway-cn-wulanchabu.data.aliyun.com/share/minghao/VideoProjects/Sythesis2/LongCaption/tmp.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[1;32m <a href='vscode-notebook-cell://dsw-gateway-cn-wulanchabu.data.aliyun.com/share/minghao/VideoProjects/Sythesis2/LongCaption/tmp.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m path \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/share_2/minghao/Datasets/Panda70M/panda70m_training_full.csv\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell://dsw-gateway-cn-wulanchabu.data.aliyun.com/share/minghao/VideoProjects/Sythesis2/LongCaption/tmp.ipynb#W1sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m panda_70M_datas \u001b[39m=\u001b[39m get_datas_from_df(path)\n",
"\u001b[0;31mNameError\u001b[0m: name 'get_datas_from_df' is not defined"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"path = '/share_2/minghao/Datasets/Panda70M/panda70m_training_full.csv'\n",
"panda_70M_datas = get_datas_from_df(path)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"size: 50000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/50000 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'videoID': 'QLfFIVI-Ows', 'url': 'https://www.youtube.com/watch?v=QLfFIVI-Ows', 'timestamp': \"[['0:00:09.000', '0:00:17.360'], ['0:00:21.400', '0:00:32.000'], ['0:00:43.480', '0:00:46.680'], ['0:00:48.040', '0:00:56.000'], ['0:00:57.800', '0:01:04.760'], ['0:01:39.480', '0:01:42.760'], ['0:01:43.440', '0:01:45.880'], ['0:02:43.480', '0:02:48.680'], ['0:03:37.800', '0:03:53.480']]\", 'caption': \"['The guitar player is sitting in front of a microphone and playing an electric guitar.', 'The guitar player is sitting on a stool in front of a wall with a black background, holding an electric guitar and wearing a black shirt.', 'A carbon copy pedal is sitting on a table next to a guitar.', 'A man sitting on a chair with a guitar in front of him.', 'A finger pressing a button on a guitar pedal.', 'A person pressing a button on a guitar amplifier.', 'A person is using a guitar amplifier and plugging in a cable.', 'A person is using a guitar amplifier and plugging it in.', 'The guitarist is playing an electric guitar and sitting on a chair in front of a microphone.']\", 'matching_score': '[0.462158203125, 0.4716796875, 0.44482421875, 0.45166015625, 0.47705078125, 0.48193359375, 0.482421875, 0.482421875, 0.449951171875]', 'desirable_filtering': \"['desirable', 'desirable', '2_tiny_camera_movement', 'desirable', 'desirable', 'desirable', 'desirable', 'desirable', 'desirable']\", 'shot_boundary_detection': \"[[['0:00:00.000', '0:00:08.320']], [['0:00:00.000', '0:00:10.560']], [['0:00:00.000', '0:00:03.160']], [['0:00:00.000', '0:00:07.920']], [['0:00:00.000', '0:00:06.920']], [['0:00:00.000', '0:00:03.240']], [['0:00:00.000', '0:00:02.400']], [['0:00:00.000', '0:00:05.160']], [['0:00:00.000', '0:00:15.640']]]\", 'video_path': '/share_2/minghao/Datasets/Panda70M/0_5min_50k/00036169.mp4'}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"path = '/share/minghao/VideoProjects/Sythesis/Ordering/Task1/Candidates/0_5min.jsonl'\n",
"datas = load_jsonl(path)\n",
"print(f'size: {len(datas)}')\n",
"\n",
"for data in tqdm(datas):\n",
" print(data)\n",
" break"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|