# MIT License
# Copyright (c) 2021 MARL @ SJTU
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
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# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.
from typing import Dict, Union, Sequence
from torch.utils import tensorboard
[docs]def write_to_tensorboard(
writer: tensorboard.SummaryWriter,
info: Dict,
global_step: Union[int, Dict],
prefix: str,
):
"""Write learning info to tensorboard.
Args:
writer (tensorboard.SummaryWriter): The summary writer instance.
info (Dict): The information dict.
global_step (int): The global step indicator.
prefix (str): Prefix added to keys in the info dict.
"""
if writer is None:
return
dict_step = isinstance(global_step, Dict)
prefix = f"{prefix}/" if len(prefix) > 0 else ""
for k, v in info.items():
if isinstance(v, dict):
# add k to prefix
write_to_tensorboard(
writer,
v,
global_step if not dict_step else global_step[k],
f"{prefix}{k}",
)
elif isinstance(v, Sequence):
raise NotImplementedError(
f"Sequence value cannot be logged currently: {v}."
)
elif v is None:
continue
else:
writer.add_scalar(
f"{prefix}{k}",
v,
global_step=global_step
if not dict_step
else global_step["global_step"],
)