def from_module(cls, module):
"""From Model."""
name = module.__class__.__name__
if ClassFactory.is_exists(ClassType.NETWORK, name):
module_cls = ClassFactory.get_cls(ClassType.NETWORK, name)
if hasattr(module_cls, "from_module"):
return module_cls.from_module(module)
return module
def __init__(self, aux_weight, loss_base):
"""Init MixAuxiliaryLoss."""
self.aux_weight = aux_weight
loss_base_cp = loss_base.copy()
loss_base_name = loss_base_cp.pop('type')
if ClassFactory.is_exists('trainer.loss', loss_base_name):
loss_class = ClassFactory.get_cls('trainer.loss', loss_base_name)
else:
loss_class = getattr(importlib.import_module('tensorflow.losses'), loss_base_name)
self.loss_fn = loss_class(**loss_base_cp['params'])
def from_desc(cls, desc):
"""Create Model from desc."""
desc = deepcopy(desc)
module_groups = desc.get('modules', [])
module_type = desc.get('type', 'Sequential')
loss = desc.get('loss')
if '_arch_params' in desc:
arch_params = desc.pop('_arch_params')
arch_type = list(arch_params.keys())[0]
ArchParams._arch_type = arch_type
ArchParams.update(arch_params.get(arch_type))
modules = OrderedDict()
for group_name in module_groups:
module_desc = deepcopy(desc.get(group_name))
if not module_desc:
continue
if 'modules' in module_desc:
module = cls.from_desc(module_desc)
else:
cls_name = module_desc.get('type')
if not ClassFactory.is_exists(ClassType.NETWORK, cls_name):
raise ValueError("Network {} not exists.".format(cls_name))
module = ClassFactory.get_instance(ClassType.NETWORK,
module_desc)
modules[group_name] = module
module.name = str(group_name)
if not module_groups and module_type:
model = ClassFactory.get_instance(ClassType.NETWORK, desc)
else:
if ClassFactory.is_exists(SearchSpaceType.CONNECTIONS,
module_type):
connections = ClassFactory.get_cls(SearchSpaceType.CONNECTIONS,
module_type)
else:
connections = ClassFactory.get_cls(SearchSpaceType.CONNECTIONS,
'Sequential')
model = list(modules.values())[0] if len(
modules) == 1 else connections(modules)
if loss:
model.add_loss(ClassFactory.get_cls(ClassType.LOSS, loss))
return model
def _register_models_from_current_module_scope(module):
for _name in dir(module):
if _name.startswith("_"):
continue
_cls = getattr(module, _name)
if isinstance(_cls, ModuleType):
continue
if ClassFactory.is_exists(ClassType.NETWORK,
'torchvision_' + _cls.__name__):
continue
ClassFactory.register_cls(_cls,
ClassType.NETWORK,
alias='torchvision_' + _cls.__name__)
def _init_loss(self):
"""Init loss."""
if vega.is_torch_backend():
loss_config = self.criterion.copy()
loss_name = loss_config.pop('type')
loss_class = getattr(importlib.import_module('torch.nn'),
loss_name)
return loss_class(**loss_config)
elif vega.is_tf_backend():
from inspect import isclass
loss_config = self.config.tf_criterion.copy()
loss_name = loss_config.pop('type')
if ClassFactory.is_exists('trainer.loss', loss_name):
loss_class = ClassFactory.get_cls('trainer.loss', loss_name)
if isclass(loss_class):
return loss_class(**loss_config)
else:
return partial(loss_class, **loss_config)
else:
loss_class = getattr(
importlib.import_module('tensorflow.losses'), loss_name)
return partial(loss_class, **loss_config)
def __call__(self, img):
"""Call function of AutoContrast.
:param img: input image
:type img: numpy or tensor
:return: the image after transform
:rtype: numpy or tensor
"""
transforms = []
if self.prob < 1.0 and random.random() > self.prob:
return img
for name in self._RAND_TRANSFORMS.keys():
if ClassFactory.is_exists(ClassType.TRANSFORM,
self._RAND_TRANSFORMS[name]):
transforms.append(
ClassFactory.get_cls(ClassType.TRANSFORM,
self._RAND_TRANSFORMS[name]))
ops = np.random.choice(transforms, self.num)
for op in ops:
if self.magnitude_std and self.magnitude_std > 0:
magnitude = random.gauss(self.magnitude, self.magnitude_std)
magnitude = min(10, max(0, magnitude))
img = op(magnitude)(img)
return img
