def dump_tabular(self):
"""
Write all of the diagnostics from the current iteration
"""
if (MPIUtil.is_root_proc()):
if (self.first_row):
self._dump_str_template = self._build_str_template()
vals = []
for key in self.log_headers:
val = self.log_current_row.get(key, "")
vals.append(val)
if self.output_file is not None:
if self.first_row:
header_str = self._dump_str_template.format(*self.log_headers)
self.output_file.write(header_str + "\n")
val_str = self._dump_str_template.format(*map(str,vals))
self.output_file.write(val_str + "\n")
self.output_file.flush()
self.log_current_row.clear()
self.first_row=False
return
def _end_record_data(self):
if MPIUtil.is_root_proc():
records_output_path = self._get_records_output_path()
records_output_dir = os.path.dirname(records_output_path)
if not os.path.exists(records_output_dir):
os.makedirs(records_output_dir)
trajectories = []
record_files = glob.glob(records_output_dir + '/*.txt')
for record_file in record_files:
print('Read file ' + record_file)
_trajectories = self._parse_record_data(record_file)
trajectories += _trajectories
states, goals, weights = [], [], []
if self.control_type == 'speed':
for records in trajectories:
for i in range(len(records)):
states += [list(records[i][0])]
goals += [list(records[i][1])]
weights += [list(records[i][2])]
elif self.control_type == 'heading':
n_previous_steps = 1
for records in trajectories:
for i in range(n_previous_steps, len(records)):
state_curr = records[i][0]
goal_curr = records[i][1]
goal_prev = records[i-n_previous_steps][1]
weight_curr = records[i][2]
theta_curr = np.arctan2(-goal_curr[2], goal_curr[0])
theta_prev = np.arctan2(-goal_prev[2], goal_prev[0])
theta_delta = theta_curr - theta_prev
goal_curr_new = np.concatenate([goal_prev, [theta_delta]], axis=-1)
states += [list(state_curr)]
goals += [list(goal_curr_new)]
weights += [list(weight_curr)]
else:
Logger.print('Unsupported control type')
assert(0)
states = np.array(states, dtype=np.float32)
goals = np.array(goals, dtype=np.float32)
weights = np.array(weights, dtype=np.float32)
output_path = self._get_output_path()
output_dir = os.path.dirname(output_path)
if not os.path.exists(records_output_dir):
os.makedirs(records_output_dir)
np.save(output_dir + '/states', states)
np.save(output_dir + '/goals', goals)
np.save(output_dir + '/weights', weights)
print('Saved record data with %d samples' % (states.shape[0]))
return
def _update_iter(self, iter):
super()._update_iter(iter)
if (self._enable_record_data and self.iter % self.record_output_iters == 0):
records_output_path = self._get_records_output_path()
records_output_dir = os.path.dirname(records_output_path)
if MPIUtil.is_root_proc() and not os.path.exists(records_output_dir):
os.makedirs(records_output_dir)
self.save_records(records_output_path)
return
def check_synced(self):
synced = True
if MPIUtil.is_root_proc():
vars = np.concatenate([self.mean, self.mean_sq])
MPIUtil.bcast(vars)
else:
vars_local = np.concatenate([self.mean, self.mean_sq])
vars_root = np.empty_like(vars_local)
MPIUtil.bcast(vars_root)
synced = (vars_local == vars_root).all()
return synced
def configure_output_file(self, filename=None):
"""
Set output directory to d, or to /tmp/somerandomnumber if d is None
"""
self.first_row = True
self.log_headers = []
self.log_current_row = {}
output_path = filename or "output/log_%i.txt"%int(time.time())
out_dir = os.path.dirname(output_path)
if not os.path.exists(out_dir) and MPIUtil.is_root_proc():
os.makedirs(out_dir)
if (MPIUtil.is_root_proc()):
self.output_file = open(output_path, 'w')
assert osp.exists(output_path)
atexit.register(self.output_file.close)
Logger.print("Logging data to " + self.output_file.name)
return
def print_tabular(self):
"""
Print all of the diagnostics from the current iteration
"""
if (MPIUtil.is_root_proc()):
vals = []
Logger.print("-"*37)
for key in self.log_headers:
val = self.log_current_row.get(key, "")
if isinstance(val, float):
valstr = "%8.3g"%val
elif isinstance(val, int):
valstr = str(val)
else:
valstr = val
Logger.print("| %15s | %15s |"%(key, valstr))
vals.append(val)
Logger.print("-" * 37)
return
def save_records(self, filename):
def numpy_1d_array_to_string(arr):
line = ''
arr = list(arr)
for i in range(0, len(arr)):
if i < len(arr) - 1:
line += '{:.10}'.format(arr[i]) + ' '
else:
line += '{:.10}'.format(arr[i])
return line
trajectories_list = MPIUtilExtend.gather(self._trajectories)
if MPIUtil.is_root_proc():
trajectories = trajectories_list[0]
for i in range(1, len(trajectories_list)):
trajectories += trajectories_list[i]
with open(filename, 'w') as fout:
for records in trajectories:
for record in records:
line = ''
for entry in record:
line += numpy_1d_array_to_string(entry) + '\n'
fout.write(line)
fout.write('\n')
self._record_smaple_count += len(records)
_root_sample_count = np.array(self._record_smaple_count, dtype=np.float32)
MPIUtil.bcast(_root_sample_count)
else:
_root_sample_count = np.array([0], dtype=np.float32)
MPIUtil.bcast(_root_sample_count)
self._record_smaple_count = int(_root_sample_count[0])
Logger.print('Record Sample Count: ' + str(self._record_smaple_count))
self._trajectories.clear()
return
def print(str):
# rank == ROOT_PROC_RANK (0) is true
if (MPIUtil.is_root_proc()):
print(str)
return
def _enable_int_output(self):
return MPIUtil.is_root_proc() and self.int_output_dir != ""
def print(str):
if (MPIUtil.is_root_proc()):
print(str)
return
def _is_root(self):
return MPIUtil.is_root_proc()
