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scenarionet/tutorial/read_established_scenarionet_dataset.ipynb
Zhenghao (Mark) Peng 07cb55c1cc Use the utils from MetaDrive to update object summaries; update ScenarioDescription doc (#52) 
* Update

* update

* update

* update
2024-01-21 19:52:57 -08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "J1w-zrxAUr4l",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Read Established ScenarioNet Dataset\n",
"\n",
"Welcome to try out MetaDrive & ScenarioNet!\n",
"\n",
"For the researchers how focus on motion prediction, scenario generation, and more, you probably will not\n",
"use the interactive environment provided by MetaDrive.\n",
"\n",
"In this tutorial, we will navigate you through how to read established ScenarioNet dataset and give you a sense of the data format."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2efvTXdHVptN",
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Installation\n",
"\n",
"Note that even we only need to load the data, you still need to install MetaDrive. But no worry, it's quite easy!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "65J2iQKpUQ1B",
"outputId": "60411b22-c699-4db0-bf23-13b4c83c4d4f",
"pycharm": {
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [],
"source": [
"#@title Collect the MetaDrive & ScenarioNet\n",
"# NOTE: If you are running this notebook locally with installtion finished, this step is not required.\n",
"RunningInCOLAB = 'google.colab' in str(get_ipython()) # Detect if it is running in Colab\n",
"if RunningInCOLAB:\n",
" %pip install git+https://github.com/metadriverse/metadrive.git\n",
" %pip install git+https://github.com/metadriverse/scenarionet.git"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's create a 2D visualization tool for recording the scenario in GIF."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pygame 2.5.2 (SDL 2.28.2, Python 3.9.18)\n",
"Hello from the pygame community. https://www.pygame.org/contribute.html\n"
]
}
],
"source": [
"# visualization\n",
"from IPython.display import Image as IImage\n",
"import pygame\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"def make_GIF(frames, name=\"demo.gif\"):\n",
" print(\"Generate gif...\")\n",
" imgs = [frame for frame in frames]\n",
" imgs = [Image.fromarray(img) for img in imgs]\n",
" imgs[0].save(name, save_all=True, append_images=imgs[1:], duration=50, loop=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuration\n",
"\n",
"Let's import some modules and specify the dataset directory."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fail to pull. Assets already exists, version: 0.4.1.2. Expected version: 0.4.1.2. To overwrite existing assets and update, add flag '--update' and rerun this script\n"
]
}
],
"source": [
"#@title Make some configurations and import some modules\n",
"from metadrive.engine.engine_utils import close_engine\n",
"close_engine()\n",
"from metadrive.pull_asset import pull_asset\n",
"pull_asset(False)\n",
"# NOTE: usually you don't need the above lines. It is only for avoiding a potential bug when running on colab\n",
"\n",
"from metadrive.engine.asset_loader import AssetLoader\n",
"from metadrive.policy.replay_policy import ReplayEgoCarPolicy\n",
"from metadrive.envs.scenario_env import ScenarioEnv\n",
"import os\n",
"\n",
"os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\" # Hide the pygame window"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We prepare two demo datasets, splitting from Waymo Open Dataset and nuScenes Dataset. Here we demonstrate the files structure:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl',\n",
" 'dataset_summary.pkl',\n",
" 'sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl',\n",
" 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waymo_data = AssetLoader.file_path(AssetLoader.asset_path, \"waymo\", unix_style=False) # Use the built-in datasets with simulator\n",
"os.listdir(waymo_data) # there are 3 waymo scenario file with a 'dataset_summary.pkl'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['dataset_summary.pkl',\n",
" 'nuscenes_3',\n",
" 'nuscenes_6',\n",
" 'dataset_mapping.pkl',\n",
" 'nuscenes_0',\n",
" 'nuscenes_5',\n",
" 'nuscenes_7',\n",
" 'nuscenes_1',\n",
" 'nuscenes_4',\n",
" 'nuscenes_2']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nuscenes_data = AssetLoader.file_path(AssetLoader.asset_path, \"nuscenes\", unix_style=False) # Use the built-in datasets with simulator\n",
"os.listdir(nuscenes_data) # there are 10 nuscenes scenario file with a 'dataset_summary.pkl' and a 'dataset_summary.pkl'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## Read Data Easily with Scenario Description\n",
"\n",
"A established ScenarioNet dataset is a folder containing `dataset_mapping.pkl` and `dataset_summary.pkl`. `dataset_mapping.pkl` contains the mapping from the scenario ID to the relative path. `dataset_summary.pkl` summarizes the meta information for each scenario.\n",
"\n",
"You can find that for the Waymo dataset, as we put all scenarios in the same folder, we don't need the `dataset_mapping.pkl` that routes scenario ID to corresponding `.pkl` file. In the nuScenes dataset, we have both `dataset_mapping.pkl` and `dataset_summary.pkl` because we have a hierachy file structure to store the scenarios.\n",
"\n",
"In this section, we demonstrate how to use the utilities from MetaDrive to easily access scenarios.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from scenarionet import read_dataset_summary, read_scenario"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read the dataset summary"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001B[0;31mSignature:\u001B[0m \u001B[0mread_dataset_summary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset_path\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;31mDocstring:\u001B[0m\n",
"Read the dataset and return the metadata of each scenario in this dataset.\n",
"\n",
"Args:\n",
" dataset_path: the path to the root folder of your dataset.\n",
"\n",
"Returns:\n",
" A tuple of three elements:\n",
" 1) the summary dict mapping from scenario ID to its metadata,\n",
" 2) the list of all scenarios IDs, and\n",
" 3) a dict mapping from scenario IDs to the folder that hosts their files.\n",
"\u001B[0;31mFile:\u001B[0m ~/scenarionet/scenarionet/common_utils.py\n",
"\u001B[0;31mType:\u001B[0m function"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"read_dataset_summary?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dataset_summary, scenario_ids, mapping = read_dataset_summary(dataset_path=waymo_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`dataset_summary` is the summary dict mapping from scenario ID to its metadata."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 'metadrive_processed': False,\n",
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" 'dataset': 'waymo',\n",
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" 'track_length': 91,\n",
" 'current_time_index': 10,\n",
" 'sdc_track_index': 210,\n",
" 'objects_of_interest': ['490', '333'],\n",
" 'tracks_to_predict': {'333': {'track_index': 3,\n",
" 'track_id': '333',\n",
" 'difficulty': 0,\n",
" 'object_type': 'VEHICLE'},\n",
" '490': {'track_index': 49,\n",
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" '752': {'track_index': 183,\n",
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" 'object_types_counter': {'PEDESTRIAN': 4, 'CYCLIST': 1, 'VEHICLE': 110},\n",
" 'dynamic_object_states_types': {'LANE_STATE_ARROW_CAUTION',\n",
" 'LANE_STATE_ARROW_STOP',\n",
" 'LANE_STATE_GO',\n",
" 'LANE_STATE_STOP',\n",
" 'LANE_STATE_UNKNOWN'},\n",
" 'dynamic_object_states_counter': {'LANE_STATE_GO': 420,\n",
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" 'LANE_STATE_STOP': 84}}}}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`scenario_ids` is the list of all scenarios IDs."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl',\n",
" 'sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl',\n",
" 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scenario_ids"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`mapping` is a dict mapping from scenario IDs to the folder that hosts their files."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sd_training.tfrecord-00000-of-01000_2a1e44d405a6833f.pkl': '',\n",
" 'sd_training.tfrecord-00000-of-01000_c403d5992cab9e0.pkl': '',\n",
" 'sd_training.tfrecord-00000-of-01000_8a346109094cd5aa.pkl': ''}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mapping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read specified scenario\n",
"\n",
"`read_scenario` will return the ScenarioNet instance. Please refer to the ScenarioDescription class in MetaDrive for more information."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001B[0;31mSignature:\u001B[0m \u001B[0mread_scenario\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset_path\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmapping\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mscenario_file_name\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
"\u001B[0;31mDocstring:\u001B[0m\n",
"Read a scenario pkl file and return the Scenario Description instance.\n",
"\n",
"Args:\n",
" dataset_path: the path to the root folder of your dataset.\n",
" mapping: the dict mapping return from read_dataset_summary.\n",
" scenario_file_name: the file name to a scenario file, should end with `.pkl`.\n",
"\n",
"Returns:\n",
" The Scenario Description instance of that scenario.\n",
"\u001B[0;31mFile:\u001B[0m ~/scenarionet/scenarionet/common_utils.py\n",
"\u001B[0;31mType:\u001B[0m function"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"read_scenario?"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"scenario_file_name = scenario_ids[0]\n",
"\n",
"scenario = read_scenario(dataset_path=waymo_data, mapping=mapping, scenario_file_name=scenario_file_name)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"metadrive.scenario.scenario_description.ScenarioDescription"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(scenario)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['id', 'version', 'length', 'tracks', 'dynamic_map_states', 'map_features', 'metadata'])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scenario.keys()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"scenario.sanity_check(scenario) # Pass check if no error is raised."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scenario.to_dict()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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