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Commit f929321b authored by Edmond Irani Liu's avatar Edmond Irani Liu 🏂
Browse files

update batch processing to handle errors

parent bcf1500b
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GSMP/motion_automata/automata/HelperFunctions.py
View file @ f929321b
import sys
sys.path.append("../../GSMP/tools/")
sys.path.append("../../GSMP/tools/commonroad-collision-checker")
sys.path.append("../../GSMP/tools/commonroad-road-boundary")
sys.path.append("../../GSMP/motion_automata/vehicle_model")
import os
path_commonroad_search = "../../"
sys.path.append(os.path.join(path_commonroad_search, "GSMP/tools/"))
sys.path.append(os.path.join(path_commonroad_search, "GSMP/tools/commonroad-collision-checker"))
sys.path.append(os.path.join(path_commonroad_search, "GSMP/tools/commonroad-road-boundary"))
sys.path.append(os.path.join(path_commonroad_search, "GSMP/motion_automata/vehicle_model"))
import time
from multiprocessing import Manager, Process
......@@ -56,15 +59,25 @@ def generate_automata(veh_type_id: int):
# step 1
automata = MotionAutomata(veh_type_id)
prefix = '../../GSMP/motion_automata/motion_primitives/'
print("Reading motion primitives...")
if veh_type_id == 1:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
elif veh_type_id == 2:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
elif veh_type_id == 3:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
try:
prefix = '../../GSMP/motion_automata/motion_primitives/'
if veh_type_id == 1:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
elif veh_type_id == 2:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
elif veh_type_id == 3:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
except Exception:
prefix = '../../../GSMP/motion_automata/motion_primitives/'
if veh_type_id == 1:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
elif veh_type_id == 2:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
elif veh_type_id == 3:
automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
# step 2
automata.createConnectivityLists()
......
notebooks/batch_processing/batch_processing.ipynb deleted 100644 → 0
View file @ bcf1500b
%% Cell type:markdown id: tags:
# Batch Processing
%% Cell type:markdown id: tags:
This is a script that helps you to batch process your code to solve for solution to different scenarios. It is composed of a configuration file (.yaml) and the codes below. Before you run this code, you should make sure the configuration file is set correctly. Here are the explanation for each of the parameters in the configuration file:
* input_path: the input directory of CommonRoad scenarios that you indend to solve.
* output_path: the output directory of the solution files.
* overwrite: the falg to determine whether to overwrite existing solution files.
* timeout: timeout time for your motion planner, unit in seconds
* trajectory_planner_path: input directory where the module containing the function to execute your motion planner is located
* trajectory_planner_module_name: name of the module taht contains the function to execute your motion planner
* trajectory_planner_function_name: name of the function that executes your motion planner
* default: the parameters specified under this will be applied to all scenarios. if you wish to specify a different paramter for specific scenarios, simply copy the section and replace 'default' with the id of your scenario.
* vehicle_model: model of the vehicle, its value could be PM, KS, ST and MB.
* vehicle_type type of the vehicle, its value could be FORD_ESCORT, BMW_320i and VW_VANAGON.
* cost_function: identifier of cost function. Please refer to [Cost Functions](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf) for more information.
%% Cell type:markdown id: tags:
### Helper functions
%% Cell type:code id: tags:
``` python
import os
import pathlib
import multiprocessing
import yaml
import sys
import warnings
from commonroad.common.file_reader import CommonRoadFileReader
from commonroad.common.solution_writer import CommonRoadSolutionWriter, VehicleModel, VehicleType, CostFunction
def parse_vehicle_model(model):
if model == 'PM':
cr_model = VehicleModel.PM
elif model == 'ST':
cr_model = VehicleModel.ST
elif model == 'KS':
cr_model = VehicleModel.KS
elif model == 'MB':
cr_model = VehicleModel.MB
else:
raise ValueError('Selected vehicle model is not valid: {}.'.format(model))
return cr_model
def parse_vehicle_type(type):
if type == 'FORD_ESCORT':
cr_type = VehicleType.FORD_ESCORT
cr_type_id = 1
elif type == 'BMW_320i':
cr_type = VehicleType.BMW_320i
cr_type_id = 2
elif type == 'VW_VANAGON':
cr_type = VehicleType.VW_VANAGON
cr_type_id = 3
else:
raise ValueError('Selected vehicle type is not valid: {}.'.format(type))
return cr_type, cr_type_id
def parse_cost_function(cost):
if cost == 'JB1':
cr_cost = CostFunction.JB1
elif cost == 'SA1':
cr_cost = CostFunction.SA1
elif cost == 'WX1':
cr_cost = CostFunction.WX1
elif cost == 'SM1':
cr_cost = CostFunction.SM1
elif cost == 'SM2':
cr_cost = CostFunction.SM2
elif cost == 'SM3':
cr_cost = CostFunction.SM3
else:
raise ValueError('Selected cost function is not valid: {}.'.format(cost))
return cr_cost
def call_trajectory_planner(queue, function, scenario, planning_problem_set, vehicle_type_id):
queue.put(function(scenario, planning_problem_set, vehicle_type_id))
```
%% Cell type:markdown id: tags:
### Read configuration
%% Cell type:code id: tags:
``` python
# open config file
with open('batch_processing_config.yaml', 'r') as stream:
try:
settings = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
# get planning wrapper function
sys.path.append(os.getcwd() + os.path.dirname(settings['trajectory_planner_path']))
module = __import__(settings['trajectory_planner_module_name'])
function = getattr(module, settings['trajectory_planner_function_name'])
time_timeout = settings['timeout']
```
%% Cell type:markdown id: tags:
### Start Processing
%% Cell type:code id: tags:
``` python
# iterate through scenarios
num_files = len(os.listdir(settings['input_path']))
print("Total number of files to be processed: {}".format(num_files))
print("Timeout setting: {} seconds\n".format(time_timeout))
count_processed = 0
for filename in os.listdir(settings['input_path']):
count_processed += 1
print("File No. {} / {}".format(count_processed, num_files))
if not filename.endswith('.xml'):
continue
fullname = os.path.join(settings['input_path'], filename)
print("Started processing scenario {}".format(filename))
scenario, planning_problem_set = CommonRoadFileReader(fullname).open()
# get settings for each scenario
if scenario.benchmark_id in settings.keys():
# specific
vehicle_model = parse_vehicle_model(settings[scenario.benchmark_id]['vehicle_model'])
vehicle_type,vehicle_type_id = parse_vehicle_type(settings[scenario.benchmark_id]['vehicle_type'])
cost_function = parse_cost_function(settings[scenario.benchmark_id]['cost_function'])
else:
# default
vehicle_model = parse_vehicle_model(settings['default']['vehicle_model'])
vehicle_type, vehicle_type_id = parse_vehicle_type(settings['default']['vehicle_type'])
cost_function = parse_cost_function(settings['default']['cost_function'])
queue = multiprocessing.Queue()
# create process, pass in required arguements
p = multiprocessing.Process(target=call_trajectory_planner, name="trajectory_planner",
args=(queue, function, scenario, planning_problem_set, vehicle_type_id))
# start planning
p.start()
# wait till process ends or skip if timed out
p.join(timeout=time_timeout)
if p.is_alive():
print("===> Trajectory planner timeout.")
p.terminate()
p.join()
solution_trajectories = {}
else:
print("Planning finished.")
solution_trajectories = queue.get()
# create path for solutions
pathlib.Path(settings['output_path']).mkdir(parents=True, exist_ok=True)
error = False
cr_solution_writer = CommonRoadSolutionWriter(settings['output_path'],
scenario.benchmark_id,
scenario.dt,
vehicle_type,
vehicle_model,
cost_function)
# inspect whether all planning problems are solved
for planning_problem_id, planning_problem in planning_problem_set.planning_problem_dict.items():
if planning_problem_id not in solution_trajectories.keys():
print('Solution for planning problem with ID={} is not provided for scenario {}. Solution skipped.'.format(
planning_problem_id, filename))
error = True
break
else:
cr_solution_writer.add_solution_trajectory(
solution_trajectories[planning_problem_id], planning_problem_id)
if not error:
cr_solution_writer.write_to_file(overwrite=settings['overwrite'])
print("=========================================================")
```
notebooks/batch_processing/batch_processing.py deleted 100644 → 0
View file @ bcf1500b
import os
import pathlib
import multiprocessing
import yaml
import sys
import warnings
from commonroad.common.file_reader import CommonRoadFileReader
from commonroad.common.solution_writer import CommonRoadSolutionWriter, VehicleModel, VehicleType, CostFunction
def parse_vehicle_model(model):
if model == 'PM':
cr_model = VehicleModel.PM
elif model == 'ST':
cr_model = VehicleModel.ST
elif model == 'KS':
cr_model = VehicleModel.KS
elif model == 'MB':
cr_model = VehicleModel.MB
else:
raise ValueError('Selected vehicle model is not valid: {}.'.format(model))
return cr_model
def parse_vehicle_type(type):
if type == 'FORD_ESCORT':
cr_type = VehicleType.FORD_ESCORT
cr_type_id = 1
elif type == 'BMW_320i':
cr_type = VehicleType.BMW_320i
cr_type_id = 2
elif type == 'VW_VANAGON':
cr_type = VehicleType.VW_VANAGON
cr_type_id = 3
else:
raise ValueError('Selected vehicle type is not valid: {}.'.format(type))
return cr_type, cr_type_id
def parse_cost_function(cost):
if cost == 'JB1':
cr_cost = CostFunction.JB1
elif cost == 'SA1':
cr_cost = CostFunction.SA1
elif cost == 'WX1':
cr_cost = CostFunction.WX1
elif cost == 'SM1':
cr_cost = CostFunction.SM1
elif cost == 'SM2':
cr_cost = CostFunction.SM2
elif cost == 'SM3':
cr_cost = CostFunction.SM3
else:
raise ValueError('Selected cost function is not valid: {}.'.format(cost))
return cr_cost
def call_trajectory_planner(queue, function, scenario, planning_problem_set, vehicle_type_id):
queue.put(function(scenario, planning_problem_set, vehicle_type_id))
# open config file
with open('batch_processing_config.yaml', 'r') as stream:
try:
settings = yaml.load(stream)
except yaml.YAMLError as exc:
print(exc)
# get planning wrapper function
sys.path.append(os.getcwd() + os.path.dirname(settings['trajectory_planner_path']))
module = __import__(settings['trajectory_planner_module_name'])
function = getattr(module, settings['trajectory_planner_function_name'])
if __name__ == '__main__':
# iterate through scenarios
for filename in os.listdir(settings['input_path']):
if not filename.endswith('.xml'):
continue
fullname = os.path.join(settings['input_path'], filename)
print("Started processing scenario {}".format(filename))
scenario, planning_problem_set = CommonRoadFileReader(fullname).open()
# get settings for each scenario
if scenario.benchmark_id in settings.keys():
# specific
vehicle_model = parse_vehicle_model(settings[scenario.benchmark_id]['vehicle_model'])
vehicle_type,vehicle_type_id = parse_vehicle_type(settings[scenario.benchmark_id]['vehicle_type'])
cost_function = parse_cost_function(settings[scenario.benchmark_id]['cost_function'])
else:
# default
vehicle_model = parse_vehicle_model(settings['default']['vehicle_model'])
vehicle_type, vehicle_type_id = parse_vehicle_type(settings['default']['vehicle_type'])
cost_function = parse_cost_function(settings['default']['cost_function'])
queue = multiprocessing.Queue()
# create process, pass in required arguements
p = multiprocessing.Process(target=call_trajectory_planner, name="trajectory_planner",
args=(queue, function, scenario, planning_problem_set, vehicle_type_id))
# start planning
p.start()
# wait till process ends or skip if timed out
p.join(timeout=settings['timeout'])
if p.is_alive():
print("Trajectory planner timeout.")
p.terminate()
p.join()
solution_trajectories = {}
else:
print("Planning finished.")
solution_trajectories = queue.get()
# create path for solutions
pathlib.Path(settings['output_path']).mkdir(parents=True, exist_ok=True)
error = False
cr_solution_writer = CommonRoadSolutionWriter(settings['output_path'],
scenario.benchmark_id,
scenario.dt,
vehicle_type,
vehicle_model,
cost_function)
# inspect whether all planning problems are solved
for planning_problem_id, planning_problem in planning_problem_set.planning_problem_dict.items():
if planning_problem_id not in solution_trajectories.keys():
warnings.warn('Solution for planning problem with ID={} is not provided for scenario {}. Solution skipped.'.format(
planning_problem_id, filename))
error = True
break
else:
cr_solution_writer.add_solution_trajectory(
solution_trajectories[planning_problem_id], planning_problem_id)
if not error:
cr_solution_writer.write_to_file(overwrite=settings['overwrite'])
print("Solution written.")
print("=========================================================")
\ No newline at end of file
notebooks/batch_processing/batch_processing_config.yaml deleted 100644 → 0
View file @ bcf1500b
# all paths need to be specified relative to file batch_processing.py
# input directory of your intended CommonRoad scenarios
input_path: ../../scenarios/exercise/
# output directory for CommonRoad solution files
output_path: ../../solutions/
# overwrite solution file if it already exists
overwrite: True
# timeout time for trajectory planner [s]
timeout: 120
# name of function which calls the trajectory planner
# Inputs: scenario and planning problem set
# Output: dictionary, key: planning problem ID, value: commonroad trajectory
trajectory_planner_path: ./
trajectory_planner_module_name: batch_search
trajectory_planner_function_name: execute_search_batch
# benchmark evaluation default parameters
# change 'defult' to scenario id to specify exclusive paramters for that scenario
# e.g. change 'default' to 'USA_US101-6_2_T-1'
default:
# vehicle model, e.g., kinematic single-track model
vehicle_model: KS
# vehicle type, e.g, BMW 320i
vehicle_type: BMW_320i
# cost function
cost_function: SM1
notebooks/batch_processing/configuration/batch_processing_config.yaml 0 → 100644
View file @ f929321b
# paths can be either relative or absolute
# input directory of your intended CommonRoad scenarios
input_path: ../../../scenarios/exercise
# output directory of CommonRoad solution files
output_path: ../../../solutions/
# overwrite solution file if it already exists
overwrite: True
# timeout time for motion planner [s]
timeout: 60
# name of the function which calls the motion planner
motion_planner_path: ../target/
motion_planner_module_name: batch_search
motion_planner_function_name: execute_search_batch
# benchmark evaluation parameters
# change 'defult' to scenario id to specify exclusive parameters for that scenario
# e.g. change 'default' to 'USA_US101-6_2_T-1'
default:
# vehicle model, e.g., kinematic single-track model
vehicle_model: KS
# vehicle type, e.g, BMW 320i
vehicle_type: BMW_320i
# cost function
cost_function: SM1
# used to specify id of planner. in this example, we have
# 1 = Greedy Best First Search, 2 = A*, else = Your own planner
planner_id: 1
# planner problem index. for cooperative scenarios: 0, 1, 2, ..., otherwise: 0
planning_problem_idx: 0
# maximum permissible depth of the search tree
max_tree_depth: 100
notebooks/batch_processing/script/.ipynb_checkpoints/batch_processing-checkpoint.ipynb 0 → 100644
View file @ f929321b
%% Cell type:markdown id: tags:
# Tutorial: Batch Processing
%% Cell type:markdown id: tags:
This tutorial shows how to solve for solutions to different scenarios in a batch processing fashion. You can find all realted files in [this](https://gitlab.lrz.de/tum-cps/commonroad_io/tree/batch_processing) repository. The main components are:
* a configuration file (.yaml format),
* some helper functions,
* and the script below to call the planer function.
Before proceeding, you should make sure the configuration file is set correctly. Here are the explanation for each of the parameters in the configuration file:
* **input_path**: input directory of CommonRoad scenarios that you indend to solve.
* **output_path**: output directory of the solution files.
* **overwrite**: flag to determine whether the existing solution files should be overwritten.
* **timeout**: timeout time for your motion planner, unit in seconds
* **motion_planner_path**: directory where the module containing the function to execute your motion planner is located
* **motion_planner_module_name**: name of the module that contains the function to execute your motion planner
* **motion_planner_function_name**: name of the function that executes your motion planner
* **default**: parameters specified under this section will be applied to all scenarios. If you wish to specify a different paramter for specific scenarios, simply copy the section and replace 'default' with the id of your scenario.
* **vehicle_model**: model of the vehicle, valid values: **PM, KS, ST and MB**. Please refer to [Vehicle Models](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf) for more information.
* **vehicle_type**: type of the vehicle, valid values: **FORD_ESCORT, BMW_320i and VW_VANAGON**.
* **cost_function**: identifier of cost function. Please refer to [Cost Functions](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf) for more information.
* **planner_id**: id of the planner that is used to solve for solutions. in this example, 1 = Greedy Best First Search, 2 = A*, else = Your own planner.
* **planning_problem_idx**: planner problem index. for cooperative scenarios: 0, 1, 2, ..., otherwise: 0
* **max_tree_depth**: maximum permissible depth of the search tree
Note: the paths can be either **relative** to this script or **absolute**.
Simply run the following script to start batch processing. The results here is just exemplary, you will see different output as your configuration varies.
%% Cell type:code id: tags:
``` python
# import helper functions
from helper_functions import *
# specify path to configuration file
path_file_config = "../configuration/batch_processing_config.yaml"
# load configuration file
configuration = load_configuration(path_file_config)
# get target function
function_target = get_target_function(configuration)
# get a list of scenario files
list_files_input = get_input_files(configuration)
# get length of the list and time before timeout
num_files = len(list_files_input)
time_timeout = configuration['timeout']
print("Total number of files to be processed: {}".format(num_files))
print("Timeout setting: {} seconds\n".format(time_timeout))
count_processed = 0
# iterate through scenarios
for file_scenario in list_files_input:
count_processed += 1
print("Processing file No. {} / {}. Scenario ID: {}".format(count_processed, num_files, file_scenario[:-4]))
# parse the scenario file
result_parse = parse_scenario_file(configuration, file_scenario)
# execute target function
solution_trajectories = execute_target_function(function_target, result_parse, time_timeout)
# save solution file
save_solution(configuration, solution_trajectories, result_parse)
print("{:=^70s}".format(''))
```
notebooks/batch_processing/script/batch_processing.ipynb 0 → 100644
View file @ f929321b
%% Cell type:markdown id: tags:
# Tutorial: Batch Processing
%% Cell type:markdown id: tags:
This tutorial shows how to solve for solutions to different scenarios in a batch processing fashion. You can find all realted files in [this](https://gitlab.lrz.de/tum-cps/commonroad_io/tree/batch_processing) repository. The main components are:
* a configuration file (.yaml format),
* some helper functions,
* and the script below to call the planer function.
Before proceeding, you should make sure the configuration file is set correctly. Here are the explanation for each of the parameters in the configuration file:
* **input_path**: input directory of CommonRoad scenarios that you indend to solve.
* **output_path**: output directory of the solution files.
* **overwrite**: flag to determine whether the existing solution files should be overwritten.
* **timeout**: timeout time for your motion planner, unit in seconds
* **motion_planner_path**: directory where the module containing the function to execute your motion planner is located
* **motion_planner_module_name**: name of the module that contains the function to execute your motion planner
* **motion_planner_function_name**: name of the function that executes your motion planner
* **default**: parameters specified under this section will be applied to all scenarios. If you wish to specify a different paramter for specific scenarios, simply copy the section and replace 'default' with the id of your scenario.
* **vehicle_model**: model of the vehicle, valid values: **PM, KS, ST and MB**. Please refer to [Vehicle Models](https://gitlab.lrz.de/tum-cps/commonroad-vehicle-models/blob/master/vehicleModels_commonRoad.pdf) for more information.
* **vehicle_type**: type of the vehicle, valid values: **FORD_ESCORT, BMW_320i and VW_VANAGON**.
* **cost_function**: identifier of cost function. Please refer to [Cost Functions](https://gitlab.lrz.de/tum-cps/commonroad-cost-functions/blob/master/costFunctions_commonRoad.pdf) for more information.
* **planner_id**: id of the planner that is used to solve for solutions. in this example, 1 = Greedy Best First Search, 2 = A*, else = Your own planner.
* **planning_problem_idx**: planner problem index. for cooperative scenarios: 0, 1, 2, ..., otherwise: 0
* **max_tree_depth**: maximum permissible depth of the search tree
Note: the paths can be either **relative** to this script or **absolute**.
Simply run the following script to start batch processing. The results here is just exemplary, you will see different output as your configuration varies.
%% Cell type:code id: tags:
``` python
# import helper functions
from helper_functions import *
# specify path to configuration file
path_file_config = "../configuration/batch_processing_config.yaml"
# load configuration file
configuration = load_configuration(path_file_config)
# get target function
function_target = get_target_function(configuration)
# get a list of scenario files
list_files_input = get_input_files(configuration)
# get length of the list and time before timeout
num_files = len(list_files_input)
time_timeout = configuration['timeout']
print("Total number of files to be processed: {}".format(num_files))
print("Timeout setting: {} seconds\n".format(time_timeout))
count_processed = 0
# iterate through scenarios
for file_scenario in list_files_input:
count_processed += 1
print("Processing file No. {} / {}. Scenario ID: {}".format(count_processed, num_files, file_scenario[:-4]))
# parse the scenario file
result_parse = parse_scenario_file(configuration, file_scenario)
# execute target function
solution_trajectories = execute_target_function(function_target, result_parse, time_timeout)
# save solution file
save_solution(configuration, solution_trajectories, result_parse)
print("{:=^70s}".format(''))
```