def __init__(self, **desc):
desc = Config(**desc)
self.num_class = desc.num_class
self.fp16 = desc.get('fp16', False)
self.blocks = desc.blocks
self.channels = desc.channels
self.conv = tf.layers.conv2d
self.pool = tf.layers.max_pooling2d
self.flat = tf.reshape
self.bn = tf.layers.batch_normalization
self.fc = tf.layers.dense
self.dropout = tf.layers.dropout
self.relu = tf.nn.relu
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 = self.config.to_dict()
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)
self.metric_results = dict()
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
"""
for key in self.mdict:
metric = self.mdict[key]
res = metric(output, target, *args, **kwargs)
return res
def reset(self):
"""Reset states for new evaluation after each epoch."""
self.metric_results = dict()
@property
def results(self):
"""Return metrics results."""
return deepcopy(self.metric_results)
@property
def objectives(self):
"""Return objectives results."""
return {name: self.mdict.get(name).objective for name in self.mdict}
def update(self, metrics):
"""Update the metrics results.
:param metrics: outside metrics
:type metrics: dict
"""
for key in metrics:
self.metric_results[key] = metrics[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 = self.config.to_dict() 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]
