def write_scalars_to_text(summary, prefix, step, textlogger,
log_axe, log_axe_sec, log_together=False):
prefix_abb = get_prefix_abb(prefix=prefix)
summary = {prefix_abb + '.' + k: v for k, v in summary.items()}
textlogger.log(step, **summary)
time_str = prefix + '-' + '-'.join(summary.keys())
if log_axe:
now = time.time()
last_time = getattr(Trainer, time_str, 0)
if now - last_time > log_axe_sec:
if log_together:
textlogger.log_ax(**summary)
else:
textlogger.log_axes(**summary)
setattr(Trainer, time_str, now)
def print_distribution(self,
iteration,
log_prob=True,
print_interval=float('inf')):
# remove formats
org_formatters = []
for handler in self.logger.handlers:
org_formatters.append(handler.formatter)
handler.setFormatter(logging.Formatter("%(message)s"))
default_dict = collections.defaultdict(dict)
self.logger.info("####### distribution #######")
searched_arc = []
for layer_id, op_dist in enumerate(self.op_dist):
prob = op_dist.probs
max_op_id = prob.argmax().item()
searched_arc.append(max_op_id)
for op_id, op_name in enumerate(self.cfg_ops.keys()):
op_prob = prob[0][op_id]
default_dict[f'L{layer_id}'][get_prefix_abb(
op_name)] = op_prob.item()
if log_prob:
if layer_id % print_interval == 0:
self.logger.info(layer_id // print_interval)
self.logger.info(prob)
searched_arc = np.array(searched_arc)
self.logger.info('\nsearched arcs: \n%s' % searched_arc)
self.myargs.textlogger.logstr(
iteration,
searched_arc='\n' +
np.array2string(searched_arc, threshold=np.inf))
summary_defaultdict2txtfig(default_dict=default_dict,
prefix='',
step=iteration,
textlogger=self.myargs.textlogger)
self.logger.info("#####################")
# restore formats
for handler, formatter in zip(self.logger.handlers, org_formatters):
handler.setFormatter(formatter)
def print_distribution(self, iteration, print_interval=float('inf')):
# remove formats
org_formatters = []
for handler in self.logger.handlers:
org_formatters.append(handler.formatter)
handler.setFormatter(logging.Formatter("%(message)s"))
self.logger.info("####### distribution #######")
class_arcs = []
for class_idx in range(self.n_classes):
default_dict = collections.defaultdict(dict)
searched_arc = []
for layer_id, op_dist in enumerate(self.op_dist):
prob = op_dist.probs
max_op_id = prob[class_idx].argmax().item()
searched_arc.append(max_op_id)
for op_id, op_name in enumerate(self.cfg_ops.keys()):
op_prob = prob[class_idx][op_id]
default_dict[f'C{class_idx}L{layer_id}'][get_prefix_abb(
op_name)] = op_prob.item()
class_arcs.append(searched_arc)
searched_arc = np.array(searched_arc)
self.logger.info(
f'Class {class_idx} searched arcs: {searched_arc}')
# self.myargs.textlogger.logstr(iteration,
# searched_arc='\n' + np.array2string(searched_arc, threshold=np.inf))
summary_defaultdict2txtfig(default_dict=default_dict,
prefix='',
step=iteration,
textlogger=self.myargs.textlogger)
class_arcs = np.array(class_arcs)
self.myargs.textlogger.logstr(
iteration,
searched_class_arc='\n' +
np.array2string(class_arcs, threshold=np.inf))
self.logger.info("#####################")
# restore formats
for handler, formatter in zip(self.logger.handlers, org_formatters):
handler.setFormatter(formatter)
def summary_defaultdict2txtfig(default_dict, prefix, step,
textlogger=None, in_one_figure=True,
log_txt=True, log_fig=True, save_fig_sec=300):
if not comm.is_main_process():
return
if textlogger is not None:
prefix_abb = get_prefix_abb(prefix=prefix)
default_dict_copy = defaultdict(dict)
# add prefix_abb and key to subkey
for k, v in default_dict.items():
default_dict_copy[k] = {prefix_abb + '.' + k + '.' + subk: subv for subk, subv in v.items()}
default_dict = default_dict_copy
if log_txt:
for k, v in default_dict.items():
textlogger.log(step, **v)
if log_fig:
textlogger.log_defaultdict2figure(default_dict, in_one_figure=in_one_figure, save_fig_sec=save_fig_sec)
else:
print('textlogger are None!')