def __init__(self,
agent_name,
s3_dict_metrics,
is_continuous,
pause_time_before_start=0.0):
"""Init eval metrics
Args:
agent_name (string): agent name
s3_dict_metrics (dict): Dictionary containing the required
s3 info for the metrics bucket with keys specified by MetricsS3Keys
is_continuous (bool): True if continuous race, False otherwise
pause_time_before_start (float): second to pause before race start
"""
self._pause_time_before_start = pause_time_before_start
self._is_pause_time_subtracted = False
self._agent_name_ = agent_name
self._s3_metrics = Metrics(
bucket=s3_dict_metrics[MetricsS3Keys.METRICS_BUCKET.value],
s3_key=s3_dict_metrics[MetricsS3Keys.METRICS_KEY.value],
region_name=s3_dict_metrics[MetricsS3Keys.REGION.value],
)
self._is_continuous = is_continuous
self._start_time_ = time.time()
self._number_of_trials_ = 0
self._progress_ = 0.0
self._episode_status = ""
self._metrics_ = list()
# This is used to calculate the actual distance traveled by the car
self._agent_xy = list()
self._prev_step_time = time.time()
self.is_save_simtrace_enabled = rospy.get_param(
"SIMTRACE_S3_BUCKET", None)
# Create the agent specific directories needed for storing the metric files
self._simtrace_local_path = SIMTRACE_EVAL_LOCAL_PATH_FORMAT.format(
self._agent_name_)
simtrace_dirname = os.path.dirname(self._simtrace_local_path)
if simtrace_dirname or not os.path.exists(simtrace_dirname):
os.makedirs(simtrace_dirname)
self.reset_count_dict = {
EpisodeStatus.CRASHED.value: 0,
EpisodeStatus.OFF_TRACK.value: 0,
EpisodeStatus.IMMOBILIZED.value: 0,
EpisodeStatus.REVERSED.value: 0,
}
self._best_lap_time = float("inf")
self._total_evaluation_time = 0
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
self._reset_count_sum = 0
self._current_sim_time = 0
self.track_data = TrackData.get_instance()
rospy.Service(
"/{}/{}".format(self._agent_name_, "mp4_video_metrics"),
VideoMetricsSrv,
self._handle_get_video_metrics,
)
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self,
agent_name,
s3_dict_metrics,
deepracer_checkpoint_json,
ckpnt_dir,
run_phase_sink,
use_model_picker=True):
'''s3_dict_metrics - Dictionary containing the required s3 info for the metrics
bucket with keys specified by MetricsS3Keys
deepracer_checkpoint_json - DeepracerCheckpointJson instance
ckpnt_dir - Directory where the current checkpont is to be stored
run_phase_sink - Sink to recieve notification of a change in run phase
use_model_picker - Flag to whether to use model picker or not.
'''
self._agent_name_ = agent_name
self._deepracer_checkpoint_json = deepracer_checkpoint_json
self._s3_metrics = Metrics(
bucket=s3_dict_metrics[MetricsS3Keys.METRICS_BUCKET.value],
s3_key=s3_dict_metrics[MetricsS3Keys.METRICS_KEY.value],
region_name=s3_dict_metrics[MetricsS3Keys.REGION.value])
self._start_time_ = time.time()
self._episode_ = 0
self._episode_reward_ = 0.0
self._progress_ = 0.0
self._episode_status = ''
self._metrics_ = list()
self._is_eval_ = True
self._eval_trials_ = 0
self._checkpoint_state_ = CheckpointStateFile(ckpnt_dir)
self._use_model_picker = use_model_picker
self._eval_stats_dict_ = {'chkpnt_name': None, 'avg_eval_metric': None}
self._best_chkpnt_stats = {
'name': None,
'avg_eval_metric': None,
'time_stamp': time.time()
}
self._current_eval_best_model_metric_list_ = list()
self.is_save_simtrace_enabled = rospy.get_param(
'SIMTRACE_S3_BUCKET', None)
self._best_model_metric_type = BestModelMetricType(
rospy.get_param('BEST_MODEL_METRIC',
BestModelMetricType.PROGRESS.value).lower())
self.track_data = TrackData.get_instance()
run_phase_sink.register(self)
# Create the agent specific directories needed for storing the metric files
self._simtrace_local_path = SIMTRACE_TRAINING_LOCAL_PATH_FORMAT.format(
self._agent_name_)
simtrace_dirname = os.path.dirname(self._simtrace_local_path)
if simtrace_dirname or not os.path.exists(simtrace_dirname):
os.makedirs(simtrace_dirname)
self._current_sim_time = 0
rospy.Service("/{}/{}".format(self._agent_name_, "mp4_video_metrics"),
VideoMetricsSrv, self._handle_get_video_metrics)
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self):
self.lock = threading.Lock()
# Read ros parameters
self.num_bot_cars = int(rospy.get_param("NUMBER_OF_BOT_CARS", 0))
self.min_bot_car_dist = float(
rospy.get_param("MIN_DISTANCE_BETWEEN_BOT_CARS", 2.0))
self.randomize = utils.str2bool(
rospy.get_param("RANDOMIZE_BOT_CAR_LOCATIONS", False))
self.bot_car_speed = float(rospy.get_param("BOT_CAR_SPEED", 0.2))
self.is_lane_change = utils.str2bool(
rospy.get_param("IS_LANE_CHANGE", False))
self.lower_lane_change_time = float(
rospy.get_param("LOWER_LANE_CHANGE_TIME", 3.0))
self.upper_lane_change_time = float(
rospy.get_param("UPPER_LANE_CHANGE_TIME", 5.0))
self.lane_change_distance = float(
rospy.get_param("LANE_CHANGE_DISTANCE", 1.0))
self.penalty_seconds = float(rospy.get_param("PENALTY_SECONDS", 2.0))
self.lane_change_duration = self.lane_change_distance / self.bot_car_speed
self.bot_car_names = [
"bot_car_{}".format(i) for i in range(self.num_bot_cars)
]
self.bot_car_poses = []
self.bot_car_progresses = {}
self.bot_car_phase = AgentPhase.RUN.value
self.bot_car_dimensions = ObstacleDimensions.BOT_CAR_DIMENSION
self.bot_car_crash_count = 0
self.pause_duration = 0.0
# track date
self.track_data = TrackData.get_instance()
self.reverse_dir = self.track_data.reverse_dir
# Wait for ros services
rospy.wait_for_service(SPAWN_SDF_MODEL)
self.spawn_sdf_model = ServiceProxyWrapper(SPAWN_SDF_MODEL, SpawnModel)
# Build splines for inner/outer lanes
self.inner_lane = TrackSpline(lane_name=TrackLane.INNER_LANE.value)
self.outer_lane = TrackSpline(lane_name=TrackLane.OUTER_LANE.value)
# Spawn the bot cars
self.current_sim_time = 0.0
self._reset_sim_time()
self._spawn_bot_cars()
self._configure_randomizer()
AbstractTracker.__init__(self, priority=TrackerPriority.HIGH)
def __init__(self, agent_name, s3_dict_metrics, is_continuous):
'''Init eval metrics
Args:
agent_name (string): agent name
s3_dict_metrics (dict): Dictionary containing the required
s3 info for the metrics bucket with keys specified by MetricsS3Keys
is_continuous (bool): True if continuous race, False otherwise
'''
self._agent_name_ = agent_name
self._s3_dict_metrics_ = s3_dict_metrics
self._is_continuous = is_continuous
self._start_time_ = time.time()
self._number_of_trials_ = 0
self._progress_ = 0.0
self._episode_status = ''
self._metrics_ = list()
# This is used to calculate the actual distance traveled by the car
self._agent_xy = list()
self._prev_step_time = time.time()
self.is_save_simtrace_enabled = rospy.get_param('SIMTRACE_S3_BUCKET', None)
# Create the agent specific directories needed for storing the metric files
simtrace_dirname = os.path.dirname(IterationDataLocalFileNames.SIM_TRACE_EVALUATION_LOCAL_FILE.value)
if not os.path.exists(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_, simtrace_dirname)):
os.makedirs(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_, simtrace_dirname))
self.reset_count_dict = {EpisodeStatus.CRASHED.value: 0,
EpisodeStatus.OFF_TRACK.value: 0,
EpisodeStatus.IMMOBILIZED.value: 0,
EpisodeStatus.REVERSED.value: 0}
self._best_lap_time = float('inf')
self._total_evaluation_time = 0
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
self._reset_count_sum = 0
self._current_sim_time = 0
self.track_data = TrackData.get_instance()
rospy.Service("/{}/{}".format(self._agent_name_, "mp4_video_metrics"), VideoMetricsSrv,
self._handle_get_video_metrics)
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self, agent_name, s3_dict_metrics, s3_dict_model, ckpnt_dir, run_phase_sink, use_model_picker=True):
'''s3_dict_metrics - Dictionary containing the required s3 info for the metrics
bucket with keys specified by MetricsS3Keys
s3_dict_model - Dictionary containing the required s3 info for the model
bucket, which is where the best model info will be saved with
keys specified by MetricsS3Keys
ckpnt_dir - Directory where the current checkpont is to be stored
run_phase_sink - Sink to recieve notification of a change in run phase
use_model_picker - Flag to whether to use model picker or not.
'''
self._agent_name_ = agent_name
self._s3_dict_metrics_ = s3_dict_metrics
self._s3_dict_model_ = s3_dict_model
self._start_time_ = time.time()
self._episode_ = 0
self._episode_reward_ = 0.0
self._progress_ = 0.0
self._episode_status = ''
self._metrics_ = list()
self._is_eval_ = True
self._eval_trials_ = 0
self._checkpoint_state_ = CheckpointStateFile(ckpnt_dir)
self._use_model_picker = use_model_picker
self._eval_stats_dict_ = {'chkpnt_name': None, 'avg_comp_pct': -1.0}
self._best_chkpnt_stats = {'name': None, 'avg_comp_pct': -1.0, 'time_stamp': time.time()}
self._current_eval_pct_list_ = list()
self.is_save_simtrace_enabled = rospy.get_param('SIMTRACE_S3_BUCKET', None)
self.track_data = TrackData.get_instance()
run_phase_sink.register(self)
# Create the agent specific directories needed for storing the metric files
simtrace_dirname = os.path.dirname(IterationDataLocalFileNames.SIM_TRACE_TRAINING_LOCAL_FILE.value)
if not os.path.exists(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_, simtrace_dirname)):
os.makedirs(os.path.join(ITERATION_DATA_LOCAL_FILE_PATH, self._agent_name_, simtrace_dirname))
self._current_sim_time = 0
rospy.Service("/{}/{}".format(self._agent_name_, "mp4_video_metrics"), VideoMetricsSrv,
self._handle_get_video_metrics)
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self, config_dict, run_phase_sink, metrics):
'''config_dict (dict): containing all the keys in ConfigParams
run_phase_sink (RunPhaseSubject): Sink to receive notification of a change in run phase
metrics (EvalMetrics/TrainingMetrics): Training or evaluation metrics
'''
# reset rules manager
self._metrics = metrics
self._is_continuous = config_dict[
const.ConfigParams.IS_CONTINUOUS.value]
self._reset_rules_manager = construct_reset_rules_manager(config_dict)
self._ctrl_status = dict()
self._ctrl_status[
AgentCtrlStatus.AGENT_PHASE.value] = AgentPhase.RUN.value
self._config_dict = config_dict
self._done_condition = config_dict.get(
const.ConfigParams.DONE_CONDITION.value, any)
self._number_of_resets = config_dict[
const.ConfigParams.NUMBER_OF_RESETS.value]
self._off_track_penalty = config_dict[
const.ConfigParams.OFF_TRACK_PENALTY.value]
self._collision_penalty = config_dict[
const.ConfigParams.COLLISION_PENALTY.value]
self._pause_duration = 0.0
self._reset_count = 0
self._curr_crashed_object_name = ''
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(
config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Set start lane. This only support for two agents H2H race
self._agent_idx_ = get_racecar_idx(self._agent_name_)
# Get track data
self._track_data_ = TrackData.get_instance()
if self._agent_idx_ is not None:
self._start_lane_ = self._track_data_.inner_lane \
if self._agent_idx_ % 2 else self._track_data_.outer_lane
else:
self._start_lane_ = self._track_data_.center_line
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[
const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Create the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {
'prev_point': Point(0, 0),
'prev_point_2': Point(0, 0)
}
# Normalized distance of new start line from the original start line of the track.
start_ndist = 0.0
# Normalized start position offset w.r.t to start_ndist, which is the start line of the track.
start_pos_offset = config_dict.get(
const.ConfigParams.START_POSITION.value, 0.0)
self._start_line_ndist_offset = start_pos_offset / self._track_data_.get_track_length(
)
# Dictionary containing some of the data for the agent
# - During the reset call, every value except start_ndist will get wiped out by self._clear_data
# (reset happens prior to every episodes begin)
# - If self._start_line_ndist_offset is not 0 (usually some minus value),
# then initial current_progress suppose to be non-zero (usually some minus value) as progress
# suppose to be based on start_ndist.
# - This will be correctly calculated by first call of utils.compute_current_prog function.
# As prev_progress will be initially 0.0 and physical position is not at start_ndist,
# utils.compute_current_prog will return negative progress if self._start_line_ndist_offset is negative value
# (meaning behind start line) and will return positive progress if self._start_line_ndist_offset is
# positive value (meaning ahead of start line).
self._data_dict_ = {
'max_progress': 0.0,
'current_progress': 0.0,
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': start_ndist,
'prev_car_pose': 0.0
}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_,
self._json_actions_)
# subscriber to time to update camera position
self.camera_manager = CameraManager.get_instance()
# True if the agent is in the training phase
self._is_training_ = False
# Register to the phase sink
run_phase_sink.register(self)
# Make sure velocity and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0,
0.0)
# start_dist should be hypothetical start line (start_ndist) plus
# start position offset (start_line_ndist_offset).
start_pose = self._start_lane_.interpolate_pose(
(self._data_dict_['start_ndist'] + self._start_line_ndist_offset) *
self._track_data_.get_track_length(),
finite_difference=FiniteDifference.FORWARD_DIFFERENCE)
self._track_data_.initialize_object(self._agent_name_, start_pose, \
ObstacleDimensions.BOT_CAR_DIMENSION)
self.make_link_points = lambda link_state: Point(
link_state.pose.position.x, link_state.pose.position.y)
self.reference_frames = ['' for _ in self._agent_link_name_list_]
self._pause_car_model_pose = None
self._park_position = DEFAULT_PARK_POSITION
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self, config_dict, run_phase_sink, metrics):
'''agent_name - String containing the name of the agent
config_dict - Dictionary containing all the keys in ConfigParams
run_phase_sink - Sink to recieve notification of a change in run phase
'''
# reset rules manager
self._metrics = metrics
self._is_continuous = config_dict[
const.ConfigParams.IS_CONTINUOUS.value]
self._is_reset = False
self._reset_rules_manager = construct_reset_rules_manager(config_dict)
self._ctrl_status = dict()
self._ctrl_status[
AgentCtrlStatus.AGENT_PHASE.value] = AgentPhase.RUN.value
self._config_dict = config_dict
self._done_condition = config_dict.get(
const.ConfigParams.DONE_CONDITION.value, any)
self._number_of_resets = config_dict[
const.ConfigParams.NUMBER_OF_RESETS.value]
self._off_track_penalty = config_dict[
const.ConfigParams.OFF_TRACK_PENALTY.value]
self._collision_penalty = config_dict[
const.ConfigParams.COLLISION_PENALTY.value]
self._pause_duration = 0.0
self._reset_count = 0
self._curr_crashed_object_name = None
self._last_crashed_object_name = None
# simapp_version speed scale
self._speed_scale_factor_ = get_speed_factor(
config_dict[const.ConfigParams.VERSION.value])
# Store the name of the agent used to set agents position on the track
self._agent_name_ = config_dict[const.ConfigParams.AGENT_NAME.value]
# Set start lane. This only support for two agents H2H race
self._agent_idx_ = get_racecar_idx(self._agent_name_)
# Get track data
self._track_data_ = TrackData.get_instance()
if self._agent_idx_ is not None:
self._start_lane_ = self._track_data_.inner_lane \
if self._agent_idx_ % 2 else self._track_data_.outer_lane
else:
self._start_lane_ = self._track_data_.center_line
# Store the name of the links in the agent, this should be const
self._agent_link_name_list_ = config_dict[
const.ConfigParams.LINK_NAME_LIST.value]
# Store the reward function
self._reward_ = config_dict[const.ConfigParams.REWARD.value]
# Create publishers for controlling the car
self._velocity_pub_dict_ = OrderedDict()
self._steering_pub_dict_ = OrderedDict()
for topic in config_dict[const.ConfigParams.VELOCITY_LIST.value]:
self._velocity_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
for topic in config_dict[const.ConfigParams.STEERING_LIST.value]:
self._steering_pub_dict_[topic] = rospy.Publisher(topic,
Float64,
queue_size=1)
#Create default reward parameters
self._reward_params_ = const.RewardParam.make_default_param()
#Creat the default metrics dictionary
self._step_metrics_ = StepMetrics.make_default_metric()
# Dictionary of bools indicating starting position behavior
self._start_pos_behavior_ = \
{'change_start' : config_dict[const.ConfigParams.CHANGE_START.value],
'alternate_dir' : config_dict[const.ConfigParams.ALT_DIR.value]}
# Dictionary to track the previous way points
self._prev_waypoints_ = {
'prev_point': Point(0, 0),
'prev_point_2': Point(0, 0)
}
# Normalize start position in meter to normalized start_ndist in percentage
start_ndist = config_dict.get(const.ConfigParams.START_POSITION.value, 0.0) / \
self._track_data_.get_track_length()
# Dictionary containing some of the data for the agent
self._data_dict_ = {
'max_progress': 0.0,
'current_progress': 0.0,
'prev_progress': 0.0,
'steps': 0.0,
'start_ndist': start_ndist
}
#Load the action space
self._action_space_, self._json_actions_ = \
load_action_space(config_dict[const.ConfigParams.ACTION_SPACE_PATH.value])
#! TODO evaluate if this is the best way to reset the car
# Adding the reward data publisher
self.reward_data_pub = RewardDataPublisher(self._agent_name_,
self._json_actions_)
# subscriber to time to update camera position
self.camera_manager = CameraManager.get_instance()
# True if the agent is in the training phase
self._is_training_ = False
# Register to the phase sink
run_phase_sink.register(self)
# Make sure velicty and angle are set to 0
send_action(self._velocity_pub_dict_, self._steering_pub_dict_, 0.0,
0.0)
start_pose = self._track_data_.center_line.interpolate_pose(
self._data_dict_['start_ndist'] *
self._track_data_.get_track_length(),
finite_difference=FiniteDifference.FORWARD_DIFFERENCE)
self._track_data_.initialize_object(self._agent_name_, start_pose, \
ObstacleDimensions.BOT_CAR_DIMENSION)
self.make_link_points = lambda link_state: Point(
link_state.pose.position.x, link_state.pose.position.y)
self.reference_frames = ['' for _ in self._agent_link_name_list_]
self._pause_car_model_pose = None
self._park_position = DEFAULT_PARK_POSITION
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self,
agent_name,
s3_dict_metrics,
deepracer_checkpoint_json,
ckpnt_dir,
run_phase_sink,
use_model_picker=True):
'''s3_dict_metrics - Dictionary containing the required s3 info for the metrics
bucket with keys specified by MetricsS3Keys
deepracer_checkpoint_json - DeepracerCheckpointJson instance
ckpnt_dir - Directory where the current checkpont is to be stored
run_phase_sink - Sink to recieve notification of a change in run phase
use_model_picker - Flag to whether to use model picker or not.
'''
self._agent_name_ = agent_name
self._deepracer_checkpoint_json = deepracer_checkpoint_json
self._s3_metrics = Metrics(
bucket=s3_dict_metrics[MetricsS3Keys.METRICS_BUCKET.value],
s3_key=s3_dict_metrics[MetricsS3Keys.METRICS_KEY.value],
region_name=s3_dict_metrics[MetricsS3Keys.REGION.value],
s3_endpoint_url=s3_dict_metrics[MetricsS3Keys.ENDPOINT_URL.value])
self._start_time_ = time.time()
self._episode_ = 0
self._episode_reward_ = 0.0
self._progress_ = 0.0
self._episode_status = ''
self._metrics_ = list()
self._is_eval_ = True
self._eval_trials_ = 0
self._checkpoint_state_ = CheckpointStateFile(ckpnt_dir)
self._use_model_picker = use_model_picker
self._eval_stats_dict_ = {'chkpnt_name': None, 'avg_eval_metric': None}
self._best_chkpnt_stats = {
'name': None,
'avg_eval_metric': None,
'time_stamp': time.time()
}
self._current_eval_best_model_metric_list_ = list()
self.is_save_simtrace_enabled = rospy.get_param(
'SIMTRACE_S3_BUCKET', None)
self._best_model_metric_type = BestModelMetricType(
rospy.get_param('BEST_MODEL_METRIC',
BestModelMetricType.PROGRESS.value).lower())
self.track_data = TrackData.get_instance()
run_phase_sink.register(self)
# Create the agent specific directories needed for storing the metric files
self._simtrace_local_path = SIMTRACE_TRAINING_LOCAL_PATH_FORMAT.format(
self._agent_name_)
simtrace_dirname = os.path.dirname(self._simtrace_local_path)
# addressing mkdir and check directory race condition:
# https://stackoverflow.com/questions/12468022/python-fileexists-error-when-making-directory/30174982#30174982
# TODO: change this to os.makedirs(simtrace_dirname, exist_ok=True) when we migrate off python 2.7
try:
os.makedirs(simtrace_dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
LOGGER.error("File already exist %s", simtrace_dirname)
self._current_sim_time = 0
rospy.Service("/{}/{}".format(self._agent_name_, "mp4_video_metrics"),
VideoMetricsSrv, self._handle_get_video_metrics)
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
AbstractTracker.__init__(self, TrackerPriority.HIGH)
def __init__(self,
agent_name,
s3_dict_metrics,
is_continuous,
pause_time_before_start=0.0):
'''Init eval metrics
Args:
agent_name (string): agent name
s3_dict_metrics (dict): Dictionary containing the required
s3 info for the metrics bucket with keys specified by MetricsS3Keys
is_continuous (bool): True if continuous race, False otherwise
pause_time_before_start (float): second to pause before race start
'''
self._pause_time_before_start = pause_time_before_start
self._is_pause_time_subtracted = False
self._agent_name_ = agent_name
self._s3_metrics = Metrics(
bucket=s3_dict_metrics[MetricsS3Keys.METRICS_BUCKET.value],
s3_key=s3_dict_metrics[MetricsS3Keys.METRICS_KEY.value],
region_name=s3_dict_metrics[MetricsS3Keys.REGION.value],
s3_endpoint_url=s3_dict_metrics[MetricsS3Keys.ENDPOINT_URL.value])
self._is_continuous = is_continuous
self._start_time_ = time.time()
self._number_of_trials_ = 0
self._progress_ = 0.0
self._episode_status = ''
self._metrics_ = list()
# This is used to calculate the actual distance traveled by the car
self._agent_xy = list()
self._prev_step_time = time.time()
self.is_save_simtrace_enabled = rospy.get_param(
'SIMTRACE_S3_BUCKET', None)
# Create the agent specific directories needed for storing the metric files
self._simtrace_local_path = SIMTRACE_EVAL_LOCAL_PATH_FORMAT.format(
self._agent_name_)
simtrace_dirname = os.path.dirname(self._simtrace_local_path)
# addressing mkdir and check directory race condition:
# https://stackoverflow.com/questions/12468022/python-fileexists-error-when-making-directory/30174982#30174982
# TODO: change this to os.makedirs(simtrace_dirname, exist_ok=True) when we migrate off python 2.7
try:
os.makedirs(simtrace_dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
LOGGER.error("File already exist %s", simtrace_dirname)
self.reset_count_dict = {
EpisodeStatus.CRASHED.value: 0,
EpisodeStatus.OFF_TRACK.value: 0,
EpisodeStatus.IMMOBILIZED.value: 0,
EpisodeStatus.REVERSED.value: 0
}
self._best_lap_time = float('inf')
self._total_evaluation_time = 0
self._video_metrics = Mp4VideoMetrics.get_empty_dict()
self._reset_count_sum = 0
self._current_sim_time = 0
self.track_data = TrackData.get_instance()
rospy.Service("/{}/{}".format(self._agent_name_, "mp4_video_metrics"),
VideoMetricsSrv, self._handle_get_video_metrics)
AbstractTracker.__init__(self, TrackerPriority.HIGH)