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import abc
import typing as t
from .interface.summary_record import SummaryItem, SummaryRecord
def _get_dict(d):
if isinstance(d, dict):
return d
# assume argparse Namespace
return vars(d)
class SummaryDict(metaclass=abc.ABCMeta):
"""dict-like wrapper for the nested dictionaries in a SummarySubDict.
Triggers self._root._callback on property changes.
"""
@abc.abstractmethod
def _as_dict(self):
raise NotImplementedError
@abc.abstractmethod
def _update(self, record: SummaryRecord):
raise NotImplementedError
def keys(self):
return [k for k in self._as_dict().keys() if k != "_wandb"]
def get(self, key, default=None):
return self._as_dict().get(key, default)
def __getitem__(self, key):
item = self._as_dict()[key]
if isinstance(item, dict):
# this nested dict needs to be wrapped:
wrapped_item = SummarySubDict()
object.__setattr__(wrapped_item, "_items", item)
object.__setattr__(wrapped_item, "_parent", self)
object.__setattr__(wrapped_item, "_parent_key", key)
return wrapped_item
# this item isn't a nested dict
return item
__getattr__ = __getitem__
def __setitem__(self, key, val):
self.update({key: val})
__setattr__ = __setitem__
def __delattr__(self, key):
record = SummaryRecord()
item = SummaryItem()
item.key = (key,)
record.remove = (item,)
self._update(record)
__delitem__ = __delattr__
def update(self, d: t.Dict):
# import ipdb; ipdb.set_trace()
record = SummaryRecord()
for key, value in d.items():
item = SummaryItem()
item.key = (key,)
item.value = value
record.update.append(item)
self._update(record)
class Summary(SummaryDict):
"""Track single values for each metric for each run.
By default, a metric's summary is the last value of its History.
For example, `wandb.log({'accuracy': 0.9})` will add a new step to History and
update Summary to the latest value. In some cases, it's more useful to have
the maximum or minimum of a metric instead of the final value. You can set
history manually `(wandb.summary['accuracy'] = best_acc)`.
In the UI, summary metrics appear in the table to compare across runs.
Summary metrics are also used in visualizations like the scatter plot and
parallel coordinates chart.
After training has completed, you may want to save evaluation metrics to a
run. Summary can handle numpy arrays and PyTorch/TensorFlow tensors. When
you save one of these types to Summary, we persist the entire tensor in a
binary file and store high level metrics in the summary object, such as min,
mean, variance, and 95th percentile.
Examples:
```python
wandb.init(config=args)
best_accuracy = 0
for epoch in range(1, args.epochs + 1):
test_loss, test_accuracy = test()
if test_accuracy > best_accuracy:
wandb.run.summary["best_accuracy"] = test_accuracy
best_accuracy = test_accuracy
```
"""
_update_callback: t.Callable
_get_current_summary_callback: t.Callable
def __init__(self, get_current_summary_callback: t.Callable):
super().__init__()
object.__setattr__(self, "_update_callback", None)
object.__setattr__(
self, "_get_current_summary_callback", get_current_summary_callback
)
def _set_update_callback(self, update_callback: t.Callable):
object.__setattr__(self, "_update_callback", update_callback)
def _as_dict(self):
return self._get_current_summary_callback()
def _update(self, record: SummaryRecord):
if self._update_callback: # type: ignore
self._update_callback(record)
class SummarySubDict(SummaryDict):
"""Non-root node of the summary data structure.
Contains a path to itself from the root.
"""
_items: t.Dict
_parent: SummaryDict
_parent_key: str
def __init__(self):
object.__setattr__(self, "_items", dict())
object.__setattr__(self, "_parent", None)
object.__setattr__(self, "_parent_key", None)
def _as_dict(self):
return self._items
def _update(self, record: SummaryRecord):
return self._parent._update(record._add_next_parent(self._parent_key))