def make_hparam_info(hparam):
data_types = {
None: DataType.DATA_TYPE_UNSET,
str: DataType.DATA_TYPE_STRING,
list: DataType.DATA_TYPE_STRING,
tuple: DataType.DATA_TYPE_STRING,
bool: DataType.DATA_TYPE_BOOL,
int: DataType.DATA_TYPE_FLOAT64,
float: DataType.DATA_TYPE_FLOAT64,
}
return HParamInfo(
name=hparam["name"],
type=data_types[hparam.get("type")],
description=hparam.get("description"),
display_name=hparam.get("display_name"),
domain_discrete=hparam.get("domain_discrete"),
domain_interval=hparam.get("domain_interval"),
)
def hparams(hparam_dict=None, metric_dict=None, hparam_domain_discrete=None):
"""Outputs three `Summary` protocol buffers needed by hparams plugin.
`Experiment` keeps the metadata of an experiment, such as the name of the
hyperparameters and the name of the metrics.
`SessionStartInfo` keeps key-value pairs of the hyperparameters
`SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS
Args:
hparam_dict: A dictionary that contains names of the hyperparameters
and their values.
metric_dict: A dictionary that contains names of the metrics
and their values.
hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
contains names of the hyperparameters and all discrete values they can hold
Returns:
The `Summary` protobufs for Experiment, SessionStartInfo and
SessionEndInfo
"""
import torch
from six import string_types
from tensorboard.plugins.hparams.api_pb2 import (
Experiment,
HParamInfo,
MetricInfo,
MetricName,
Status,
DataType,
)
from tensorboard.plugins.hparams.metadata import (
PLUGIN_NAME,
PLUGIN_DATA_VERSION,
EXPERIMENT_TAG,
SESSION_START_INFO_TAG,
SESSION_END_INFO_TAG,
)
from tensorboard.plugins.hparams.plugin_data_pb2 import (
HParamsPluginData,
SessionEndInfo,
SessionStartInfo,
)
# 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))
# mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy',
# description='', dataset_type=DatasetType.DATASET_VALIDATION)
# exp = Experiment(name='123', description='456', time_created_secs=100.0,
# hparam_infos=[hp], metric_infos=[mt], user='******')
if not isinstance(hparam_dict, dict):
logger.warning(
"parameter: hparam_dict should be a dictionary, nothing logged.")
raise TypeError(
"parameter: hparam_dict should be a dictionary, nothing logged.")
if not isinstance(metric_dict, dict):
logger.warning(
"parameter: metric_dict should be a dictionary, nothing logged.")
raise TypeError(
"parameter: metric_dict should be a dictionary, nothing logged.")
hparam_domain_discrete = hparam_domain_discrete or {}
if not isinstance(hparam_domain_discrete, dict):
raise TypeError(
"parameter: hparam_domain_discrete should be a dictionary, nothing logged."
)
for k, v in hparam_domain_discrete.items():
if (k not in hparam_dict or not isinstance(v, list)
or not all(isinstance(d, type(hparam_dict[k])) for d in v)):
raise TypeError(
"parameter: hparam_domain_discrete[{}] should be a list of same type as "
"hparam_dict[{}].".format(k, k))
hps = []
ssi = SessionStartInfo()
for k, v in hparam_dict.items():
if v is None:
continue
if isinstance(v, int) or isinstance(v, float):
ssi.hparams[k].number_value = v
if k in hparam_domain_discrete:
domain_discrete: Optional[
struct_pb2.ListValue] = struct_pb2.ListValue(values=[
struct_pb2.Value(number_value=d)
for d in hparam_domain_discrete[k]
])
else:
domain_discrete = None
hps.append(
HParamInfo(
name=k,
type=DataType.Value("DATA_TYPE_FLOAT64"),
domain_discrete=domain_discrete,
))
continue
if isinstance(v, string_types):
ssi.hparams[k].string_value = v
if k in hparam_domain_discrete:
domain_discrete = struct_pb2.ListValue(values=[
struct_pb2.Value(string_value=d)
for d in hparam_domain_discrete[k]
])
else:
domain_discrete = None
hps.append(
HParamInfo(
name=k,
type=DataType.Value("DATA_TYPE_STRING"),
domain_discrete=domain_discrete,
))
continue
if isinstance(v, bool):
ssi.hparams[k].bool_value = v
if k in hparam_domain_discrete:
domain_discrete = struct_pb2.ListValue(values=[
struct_pb2.Value(bool_value=d)
for d in hparam_domain_discrete[k]
])
else:
domain_discrete = None
hps.append(
HParamInfo(
name=k,
type=DataType.Value("DATA_TYPE_BOOL"),
domain_discrete=domain_discrete,
))
continue
if isinstance(v, torch.Tensor):
v = make_np(v)[0]
ssi.hparams[k].number_value = v
hps.append(
HParamInfo(name=k, type=DataType.Value("DATA_TYPE_FLOAT64")))
continue
raise ValueError(
"value should be one of int, float, str, bool, or torch.Tensor")
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.Value("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 hparams(hparam_dict=None, metric_dict=None):
"""Outputs three `Summary` protocol buffers needed by hparams plugin.
`Experiment` keeps the metadata of an experiment, such as the name of the
hyperparameters and the name of the metrics.
`SessionStartInfo` keeps key-value pairs of the hyperparameters
`SessionEndInfo` describes status of the experiment e.g. STATUS_SUCCESS
Args:
hparam_dict: A dictionary that contains names of the hyperparameters
and their values.
metric_dict: A dictionary that contains names of the metrics
and their values.
Returns:
The `Summary` protobufs for Experiment, SessionStartInfo and
SessionEndInfo
"""
import torch
from six import string_types
from tensorboard.plugins.hparams.api_pb2 import (Experiment, HParamInfo,
MetricInfo, MetricName,
Status)
from tensorboard.plugins.hparams.metadata import (PLUGIN_NAME,
PLUGIN_DATA_VERSION,
EXPERIMENT_TAG,
SESSION_START_INFO_TAG,
SESSION_END_INFO_TAG)
from tensorboard.plugins.hparams.plugin_data_pb2 import (HParamsPluginData,
SessionEndInfo,
SessionStartInfo)
# 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))
# mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy',
# description='', dataset_type=DatasetType.DATASET_VALIDATION)
# exp = Experiment(name='123', description='456', time_created_secs=100.0,
# hparam_infos=[hp], metric_infos=[mt], user='******')
if not isinstance(hparam_dict, dict):
logging.warning(
'parameter: hparam_dict should be a dictionary, nothing logged.')
raise TypeError(
'parameter: hparam_dict should be a dictionary, nothing logged.')
if not isinstance(metric_dict, dict):
logging.warning(
'parameter: metric_dict should be a dictionary, nothing logged.')
raise TypeError(
'parameter: metric_dict should be a dictionary, nothing logged.')
hps = [HParamInfo(name=k) for k in hparam_dict.keys()]
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)])
ssi = SessionStartInfo()
for k, v in hparam_dict.items():
if v is None:
continue
if isinstance(v, int) or isinstance(v, float):
ssi.hparams[k].number_value = v
continue
if isinstance(v, string_types):
ssi.hparams[k].string_value = v
continue
if isinstance(v, bool):
ssi.hparams[k].bool_value = v
continue
if isinstance(v, torch.Tensor):
v = make_np(v)[0]
ssi.hparams[k].number_value = v
continue
raise ValueError(
'value should be one of int, float, str, bool, or torch.Tensor')
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)])
sei = SessionEndInfo(status=Status.Value('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