benchmark seems to be working a first version. The planner needs further work "
;
This commit is contained in:
felipecode
parent
d9624f6616
commit
85d693032e
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@ -21,21 +21,21 @@ from carla.carla_server_pb2 import Control
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class Agent(object, ):
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def __init__(self, **kwargs):
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def __init__(self,city_name, **kwargs):
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import os
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dir_path = os.path.dirname(__file__)
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planner = Planner(dir_path+'/../planner/' + city_name + '.txt',\
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dir_path+'/../planner/' + city_name + '.png')
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self._planner = Planner(dir_path+'/../carla/planner/' + city_name + '.txt',\
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dir_path+'/../carla/planner/' + city_name + '.png')
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def get_initial_distance(self):
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def get_distance(self,start_point,end_point):
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_,path_distance=planner.get_next_command([positions[start_point].location.x\
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,positions[start_point].location.y,22],[positions[start_point].orientation.x\
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,positions[start_point].orientation.y,22],[positions[end_point].location.x\
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,positions[end_point].location.y,22],(1,0,0))
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_,path_distance=self._planner.get_next_command([start_point.location.x\
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,start_point.location.y,22],[start_point.orientation.x\
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,start_point.orientation.y,22],[end_point.location.x\
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,end_point.location.y,22],(1,0,0))
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# We calculate the timout based on the distance
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return path_distance
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@ -1,10 +1,19 @@
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from .metrics import plot_summary
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try:
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from carla import carla_server_pb2 as carla_protocol
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except ImportError:
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raise RuntimeError('cannot import "carla_server_pb2.py", run the protobuf compiler to generate this file')
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import json, csv, time
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class Benchmark(object,):
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# Param @name to be used for saving purposes
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def __init__(self,name):
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def __init__(self,city_name,name):
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self._city_name = city_name # The name of the city that is going to be used.
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self._base_name = name # Sends a base name, the rest will be saved with respect to what the episode was about
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self._dict_stats = {'exp_id':-1,
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'rep':-1,
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@ -19,10 +28,6 @@ class Benchmark(object,):
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}
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with open(self._base_name + self._suffix_name , 'wb') as ofd:
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w = csv.DictWriter(ofd, self._dict_stats.keys())
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w.writeheader()
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self._dict_rewards = {
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@ -47,7 +52,7 @@ class Benchmark(object,):
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prev_x = -1
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prev_y = -1
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measurements,sensor_data= carla.read_data()
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carla.send_control(Control())
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carla.send_control(carla_protocol.Control())
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t0 = measurements.game_timestamp
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t1=t0
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success = False
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@ -59,9 +64,7 @@ class Benchmark(object,):
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while((t1-t0) < (time_out*1000) and not success):
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capture_time = time.time()
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measurements,sensor_data = carla.read_data()
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print (sensor_data)
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print (measurements)
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print (time.time()-capture_time)
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control = agent.run_step(measurements,sensor_data,target)
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print ('STEER ',control.steer,'GAS ',control.throttle,'Brake ',control.brake)
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carla.send_control(control)
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@ -78,17 +81,12 @@ class Benchmark(object,):
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measurement_vec.append(measurements.player_measurements)
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t1 = measurements.game_timestamp
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print (t1-t0)
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# accumulate layout related signal
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# accum_lane_intersect += reward.road_intersect
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#accum_sidewalk_intersect += reward.sidewalk_intersect
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step += 1
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# This distance is wrong
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# The distance is based on graph but quite not exact.
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distance = agent.get_distance(measurements.player_measurements.transform,target)
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agent.compute_distance()
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#distance = self.compute_distance([curr_x, curr_y], [prev_x, prev_y], [target.location.x, target.location.y])
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# debug
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print('[d=%f] c_x = %f, c_y = %f ---> t_x = %f, t_y = %f' % (float(distance), curr_x, curr_y, target.location.x, target.location.y))
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if(distance < 200.0):
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@ -107,51 +105,46 @@ class Benchmark(object,):
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experiments = self._build_experiments() # Returns a experiment class that is build from a benchmark inherited class
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self._suffix_name = self._get_names(experiments[starting_position:]) # The fixed name considering all the experiments being run
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self._suffix_name = self._get_experiments_names(experiments[starting_position:]) # The fixed name considering all the experiments being run
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with open(self._base_name + self._suffix_name , 'wb') as ofd:
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w = csv.DictWriter(ofd, self._dict_stats.keys())
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w.writeheader()
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with open('rewards_' + self._base_name + self._suffix_name , 'wb') as rfd:
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rw = csv.DictWriter(rfd, self._dict_rewards.keys())
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rw.writeheader()
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self.write_experiment() # write the experiment being run
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for experiment in experiments[starting_position:]:
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self.write_experiment(experiment) # write the experiment being run
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#poses_exp = start_goal_poses[experiment_id]
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#repetitions = repetitions_per_experiment[experiment_id]
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#pedestrians_exp = pedestrians[experiment_id]
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#ehicles_exp = vehicles[experiment_id]
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#for rep in range(repetitions): # CONTROL REPETITION INSIDE EXPERIMENT ???
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# for the different weathers
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#for weather_cond in weathers:
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# let's go through all the starting-goal positions of the experiment
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positions = carla.load_settings(experiment.conditions).player_start_spots
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for pose in experiment.poses:
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for rep in experiment.repetitions:
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for rep in range(experiment.repetitions):
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trajectory = experiment.poses
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#ped = pedestrians_exp[i]
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#vehic = vehicles_exp[i]
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start_point = trajectory[0]
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end_point = trajectory[1]
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start_point = pose[0]
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end_point = pose[1]
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carla.start_episode(start_point)
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print('======== !!!! ==========')
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path_distance = agent.get_initial_distance(positions[start_point],positions[end_point])
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path_distance = agent.get_distance(positions[start_point],positions[end_point])
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time_out = self._calculate_time_out(path_distance)
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# running the agent
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(result, reward_vec, final_time, remaining_distance) = self.run_until(agent,carla,time_out,positions[end_point])
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(result, reward_vec, final_time, remaining_distance) = self.run_navigation_episode(agent,carla,time_out,positions[end_point])
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@ -160,18 +153,11 @@ class Benchmark(object,):
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self.write_summary_results(experiment,pose,rep,path_distance,remaining_distance,final_time,time_out,result)
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#rw.writerow(dict_rewards)
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self.write_reward_results(reward_vec)
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self.write_reward_results(experiment,rep,reward_vec)
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# save results of the experiment
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#list_stats.append(dict_stats)
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#print (dict_stats)
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#w.writerow(dict_stats)
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if(result > 0):
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print('+++++ Target achieved in %f seconds! +++++' % final_time)
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else:
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@ -182,8 +168,11 @@ class Benchmark(object,):
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def write_details(self):
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pass
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def write_experiment(self):
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with open(self._get_details() , 'wb') as ofd:
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pass
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def write_summary_results(self,experiment,pose,rep,path_distance,remaining_distance,final_time,time_out,result):
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@ -205,31 +194,31 @@ class Benchmark(object,):
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w = csv.DictWriter(ofd, self._dict_stats.keys())
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w.writerow(dict_stats)
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w.writerow(self._dict_stats)
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def write_reward_results(self,experiment,rep,reward_vec):
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with open('rewards_' + self._base_name + self._suffix_name , 'a+') as ofd:
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with open('rewards_' + self._base_name + self._suffix_name , 'a+') as rfd:
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rw = csv.DictWriter(rfd, dict_rewards.keys())
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rw = csv.DictWriter(rfd, self._dict_rewards.keys())
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rw.writeheader()
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for i in range(len(reward_vec)):
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dict_rewards['exp_id'] = experiment.id
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dict_rewards['rep'] = rep
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dict_rewards['weather'] = experiment.Conditions.WeatherId
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dict_rewards['collision_gen'] = reward_vec[i].collision_other
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dict_rewards['collision_ped'] = reward_vec[i].collision_pedestrians
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dict_rewards['collision_car'] = reward_vec[i].collision_vehicles
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dict_rewards['lane_intersect'] = reward_vec[i].intersection_otherlane
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dict_rewards['sidewalk_intersect'] = reward_vec[i].intersection_offroad
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dict_rewards['pos_x'] = reward_vec[i].transform.location.x
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dict_rewards['pos_y'] = reward_vec[i].transform.location.y
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self._dict_rewards['exp_id'] = experiment.id
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self._dict_rewards['rep'] = rep
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self._dict_rewards['weather'] = experiment.Conditions.WeatherId
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self._dict_rewards['collision_gen'] = reward_vec[i].collision_other
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self._dict_rewards['collision_ped'] = reward_vec[i].collision_pedestrians
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self._dict_rewards['collision_car'] = reward_vec[i].collision_vehicles
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self._dict_rewards['lane_intersect'] = reward_vec[i].intersection_otherlane
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self._dict_rewards['sidewalk_intersect'] = reward_vec[i].intersection_offroad
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self._dict_rewards['pos_x'] = reward_vec[i].transform.location.x
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self._dict_rewards['pos_y'] = reward_vec[i].transform.location.y
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rw.writerow(dict_rewards)
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rw.writerow(self._dict_rewards)
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@ -240,7 +229,7 @@ class Benchmark(object,):
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summary_weathers = {'train_weather': [1,3,6,8]}
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summary = plot_summary(self._base_name + self._suffix_name,summary_weathers, )
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summary = plot_summary(self._base_name + self._suffix_name,summary_weathers)
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def plot_summary_train(self):
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@ -257,11 +246,11 @@ class Benchmark(object,):
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return 0
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# To be redefined on subclasses
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def build_experiments(self):
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def _build_experiments(self):
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pass
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def _get_experiments(self,experiments):
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def _get_experiments_names(self,experiments):
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name_cat ='_t'
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for experiment in experiments:
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@ -1,7 +1,8 @@
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from .benchmark import Benchmark
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from .experiment import Experiment
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from carla.sensor import Camera
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from carla.settings import CarlaSettings
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# Function to return the timeout ( in miliseconds) that is calculated based on distance to goal.
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# This is the same timeout as used on the CoRL paper.
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@ -10,7 +11,7 @@ from .experiment import Experiment
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class CoRL(Benchmark):
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def _calculate_time_out(self,distance):
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def _calculate_time_out(self,path_distance):
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return ((path_distance/100000.0)/10.0)*3600.0 + 10.0
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def _poses_town01(self):
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@ -72,7 +73,7 @@ class CoRL(Benchmark):
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]
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def build_experiments(self,town):
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def _build_experiments(self):
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# We set the camera that is going to be used for all experiments
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@ -86,7 +87,7 @@ class CoRL(Benchmark):
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camera.set_rotation(-15.0,0,0)
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weathers = [1,3,6,8,4,14]
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if town == 'Town01':
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if self._city_name == 'Town01':
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poses_tasks = self._poses_town01()
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vehicles_tasks =[0,0,0,20]
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pedestrians_tasks = [0,0,0,50]
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@ -130,8 +131,10 @@ class CoRL(Benchmark):
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Conditions=conditions,
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Poses= poses,
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Id = iteration,
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Repetions = 1
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Repetitions = 1
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)
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experiments_vector.append(experiment)
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return experiments_vector
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def _get_details(self): # Function to get automatic information from the experiment for writing purposes
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return 'corl_' + self._city_name
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@ -20,7 +20,7 @@ class Experiment(object):
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setattr(self, key, value)
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@property
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def name(self):
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def id(self):
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return self.Id
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@property
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@ -59,7 +59,7 @@ class Planner(object):
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import os
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dir_path = os.path.dirname(__file__)
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self.central_path_map_image = Image.open(map_file[:-4] +'c.png')
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self.central_path_map_image = Image.open(map_file[:-4] +'Central.png')
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self.central_path_map_image.load()
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self.central_path_map_image = np.asarray(self.central_path_map_image, dtype="int32" )
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self.central_path_map_image =self.central_path_map_image[:,:,0] # Just take the red dimension
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@ -9,11 +9,16 @@ from benchmarks.agent import Agent
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from carla.tcp import TCPConnectionError
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from carla.client import make_carla_client
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import time
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try:
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from carla import carla_server_pb2 as carla_protocol
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except ImportError:
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raise RuntimeError('cannot import "carla_server_pb2.py", run the protobuf compiler to generate this file')
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class Manual(Agent):
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def run_step(self, data,target):
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control = Control()
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def run_step(self,measurements,sensor_data,target):
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control = carla_protocol.Control()
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control.steer = 0.0
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control.throttle = 0.9
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control.brake = 0.0
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@ -55,10 +60,12 @@ if(__name__ == '__main__'):
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while True:
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try:
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with make_carla_client(host, port) as client:
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corl= CoRL('test')
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agent = Manual()
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results = corl.benchmark_agent(agent,carla)
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with make_carla_client(args.host, args.port) as client:
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corl= CoRL('Town02','test')
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agent = Manual('Town02')
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results = corl.benchmark_agent(agent,client)
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corl.plot_summary_test()
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corl.plot_summary_train()
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break
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