class Metrics(object):
"""Metrics class of all metrics defined in cfg.
:param metric_cfg: metric part of config
:type metric_cfg: dict or Config
"""
__supported_call__ = [
'accuracy', 'DetMetric', 'IoUMetric', 'SRMetric',
'JDDTrainerPSNRMetric'
]
def __init__(self, metric_cfg):
"""Init Metrics."""
metric_config = deepcopy(metric_cfg)
self.mdict = {}
if not isinstance(metric_config, list):
metric_config = [metric_config]
for metric_item in metric_config:
metric_name = metric_item.pop('type')
if ClassFactory.is_exists(ClassType.METRIC, metric_name):
metric_class = ClassFactory.get_cls(ClassType.METRIC,
metric_name)
else:
metric_class = getattr(
importlib.import_module('vega.core.metrics'), metric_name)
if isfunction(metric_class):
metric_class = partial(metric_class, **metric_item)
else:
metric_class = metric_class(**metric_item)
self.mdict[metric_name] = metric_class
self.mdict = Config(self.mdict)
def __call__(self, output=None, target=None, *args, **kwargs):
"""Calculate all supported metrics by using output and target.
:param output: predicted output by networks
:type output: torch tensor
:param target: target label data
:type target: torch tensor
:return: performance of metrics
:rtype: list
"""
pfms = []
for key in self.mdict:
metric = self.mdict[key]
if key in self.__supported_call__:
pfms.append(metric(output, target, *args, **kwargs))
return pfms
# if len(pfms) == 1:
# return pfms[0]
# else:
# return pfms
def reset(self):
"""Reset states for new evaluation after each epoch."""
for val in self.mdict.values():
val.reset()
@property
def names(self):
"""Return metrics names."""
names = [
name for name in self.mdict if name in self.__supported_call__
]
return names
# if len(names) == 1:
# return names[0]
# else:
# return names
@property
def results(self):
"""Return metrics results."""
results = [
self.mdict[name].summary() for name in self.mdict
if name in self.__supported_call__
]
return deepcopy(results)
@property
def results_dict(self):
"""Return metrics results dict."""
rdict = {}
for key in self.mdict:
rdict[key] = self.mdict[key].summary()
return deepcopy(rdict)
def __getattr__(self, key):
"""Get a metric by key name.
:param key: metric name
:type key: str
"""
return self.mdict[key]
class Metrics(object):
"""Metrics class of all metrics defined in cfg.
:param metric_cfg: metric part of config
:type metric_cfg: dict or Config
"""
config = MetricsConfig()
def __init__(self, metric_cfg=None):
"""Init Metrics."""
self.mdict = {}
metric_config = obj2config(
self.config) if not metric_cfg else deepcopy(metric_cfg)
if not isinstance(metric_config, list):
metric_config = [metric_config]
for metric_item in metric_config:
ClassFactory.get_cls(ClassType.METRIC, self.config.type)
metric_name = metric_item.pop('type')
metric_class = ClassFactory.get_cls(ClassType.METRIC, metric_name)
if isfunction(metric_class):
metric_class = partial(metric_class,
**metric_item.get("params", {}))
else:
metric_class = metric_class(**metric_item.get("params", {}))
self.mdict[metric_name] = metric_class
self.mdict = Config(self.mdict)
def __call__(self, output=None, target=None, *args, **kwargs):
"""Calculate all supported metrics by using output and target.
:param output: predicted output by networks
:type output: torch tensor
:param target: target label data
:type target: torch tensor
:return: performance of metrics
:rtype: list
"""
pfms = []
for key in self.mdict:
metric = self.mdict[key]
pfms.append(metric(output, target, *args, **kwargs))
return pfms
def reset(self):
"""Reset states for new evaluation after each epoch."""
for val in self.mdict.values():
val.reset()
@property
def results(self):
"""Return metrics results."""
res = {}
for name, metric in self.mdict.items():
res.update(metric.result)
return res
@property
def objectives(self):
"""Return objectives results."""
return {name: self.mdict.get(name).objective for name in self.mdict}
def __getattr__(self, key):
"""Get a metric by key name.
:param key: metric name
:type key: str
"""
return self.mdict[key]
