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Incubation of Graphical Representation in TF2 of HParams #1

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34 changes: 19 additions & 15 deletions torch/utils/tensorboard/writer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,21 +297,25 @@ def add_hparams(
:scale: 50 %

"""
torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
if type(hparam_dict) is not dict or type(metric_dict) is not dict:
raise TypeError('hparam_dict and metric_dict should be dictionary.')
exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)

if not run_name:
run_name = str(time.time())
logdir = os.path.join(self._get_file_writer().get_logdir(), run_name)
with SummaryWriter(log_dir=logdir) as w_hp:
w_hp.file_writer.add_summary(exp)
w_hp.file_writer.add_summary(ssi)
w_hp.file_writer.add_summary(sei)
for k, v in metric_dict.items():
w_hp.add_scalar(k, v)

def add_hparams(self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None):
torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
self.file_writer.add_summary(exp)
self.file_writer.add_summary(ssi)
self.file_writer.add_summary(sei)
for k, v in metric_dict.items():
if v is not None:
self.add_scalar(k, v)
from torch.utils.tensorboard import SummaryWriter
for i in range(5):
save_metrics = {'train/acc': None, 'train/loss': None}
writer = SummaryWriter(f'runs/{i}')
for step in range(50):
writer.add_scalar('train/acc', 10*i+step, step)
writer.add_scalar('train/loss', 10*i - step, step)
writer.add_hparams({'lr': 0.1*i, 'bsize': i}, save_metrics)
writer.close()

def add_scalar(
self,
tag,
Expand Down