def hparams(hparam_dict=None, metric_dict=None):
from tensorboardX.proto.plugin_hparams_pb2 import HParamsPluginData, SessionEndInfo, SessionStartInfo
from tensorboardX.proto.api_pb2 import Experiment, HParamInfo, MetricInfo, MetricName, Status, DataType
from six import string_types
PLUGIN_NAME = 'hparams'
PLUGIN_DATA_VERSION = 0
EXPERIMENT_TAG = '_hparams_/experiment'
SESSION_START_INFO_TAG = '_hparams_/session_start_info'
SESSION_END_INFO_TAG = '_hparams_/session_end_info'
# TODO: expose other parameters in the future.
# hp = HParamInfo(name='lr',display_name='learning rate', type=DataType.DATA_TYPE_FLOAT64, domain_interval=Interval(min_value=10, max_value=100)) # noqa E501
# mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy', description='', dataset_type=DatasetType.DATASET_VALIDATION) # noqa E501
# exp = Experiment(name='123', description='456', time_created_secs=100.0, hparam_infos=[hp], metric_infos=[mt], user='******') # noqa E501
hps = []
ssi = SessionStartInfo()
for k, v in hparam_dict.items():
if isinstance(v, string_types):
ssi.hparams[k].string_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_STRING))
continue
if isinstance(v, bool):
ssi.hparams[k].bool_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_BOOL))
continue
if not isinstance(v, int) or not isinstance(v, float):
v = make_np(v)[0]
ssi.hparams[k].number_value = v
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_FLOAT64))
continue
hps.append(HParamInfo(name=k, type=DataType.DATA_TYPE_UNSET))
content = HParamsPluginData(session_start_info=ssi, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
ssi = Summary(value=[Summary.Value(tag=SESSION_START_INFO_TAG, metadata=smd)])
mts = [MetricInfo(name=MetricName(tag=k)) for k in metric_dict.keys()]
exp = Experiment(hparam_infos=hps, metric_infos=mts)
content = HParamsPluginData(experiment=exp, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
exp = Summary(value=[Summary.Value(tag=EXPERIMENT_TAG, metadata=smd)])
sei = SessionEndInfo(status=Status.STATUS_SUCCESS)
content = HParamsPluginData(session_end_info=sei, version=PLUGIN_DATA_VERSION)
smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
sei = Summary(value=[Summary.Value(tag=SESSION_END_INFO_TAG, metadata=smd)])
return exp, ssi, sei
def make_metric_info(metric):
return MetricInfo(
name=MetricName(tag=metric['tag']),
dataset_type=DatasetType.Value(
f'DATASET_{metric.get("dataset_type", "UNKNOWN").upper()}'),
description=metric.get('description'),
display_name=metric.get('display_name'))