def get_model(cls, network_desc=None, pretrained=False):
"""Get model from model zoo.
:param network_name: the name of network, eg. ResNetVariant.
:type network_name: str or None.
:param network_desc: the description of network.
:type network_desc: str or None.
:param pretrained: pre-trained model or not.
:type pretrained: bool.
:return: model.
:rtype: model.
"""
try:
network = NetworkDesc(network_desc)
model = network.to_model()
except Exception as e:
logging.error(
"Failed to get model, network_desc={}, msg={}".format(
network_desc, str(e)))
raise e
logging.info("Model was created, model_desc={}".format(network_desc))
if pretrained is True:
logging.info("Load pretrained model.")
model = cls._load_pretrained_model(network, model)
logging.info("Pretrained model was loaded.")
return model
def _init_model(self, model=None):
"""Load model desc from save path and parse to model."""
if model is not None:
return model
model_cfg = ClassFactory.__configs__.get('model')
if 'model_desc_file' in model_cfg and model_cfg.model_desc_file is not None:
desc_file = model_cfg.model_desc_file.replace(
"{model_zoo}", self.model_zoo_path)
desc_file = desc_file.replace("{local_base_path}",
self.local_base_path)
if ":" not in desc_file:
desc_file = os.path.abspath(desc_file)
if ":" in desc_file:
local_desc_file = FileOps.join_path(
self.local_output_path, os.path.basename(desc_file))
FileOps.copy_file(desc_file, local_desc_file)
desc_file = local_desc_file
if self.horovod:
hvd.join()
model_desc = Config(desc_file)
logging.info("net_desc:{}".format(model_desc))
elif 'model_desc' in model_cfg and model_cfg.model_desc is not None:
model_desc = model_cfg.model_desc
else:
return None
if model_desc is not None:
self.model_desc = model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
return model
else:
return None
def get_model(cls, model_desc=None, model_checkpoint=None):
"""Get model from model zoo.
:param network_name: the name of network, eg. ResNetVariant.
:type network_name: str or None.
:param network_desc: the description of network.
:type network_desc: str or None.
:param model_checkpoint: path of model.
:type model_checkpoint: str.
:return: model.
:rtype: model.
"""
try:
network = NetworkDesc(model_desc)
model = network.to_model()
except Exception as e:
logging.error("Failed to get model, model_desc={}, msg={}".format(
model_desc, str(e)))
raise e
logging.info("Model was created.")
logging.debug("model_desc={}".format(model_desc))
if model_checkpoint is not None:
logging.info("Load model with weight.")
model = cls._load_pretrained_model(network, model, model_checkpoint)
logging.info("Model was loaded.")
return model
def new_model(self):
"""Build new model."""
net_desc = NetworkDesc(self.search_space)
model_new = net_desc.to_model().cuda()
for x, y in zip(model_new.arch_parameters(),
self.model.arch_parameters()):
x.detach().copy_(y.detach())
return model_new
def _init_model(self):
"""Initialize the model architecture for full train step.
:return: train model
:rtype: class
"""
search_space = Config({"search_space": self.model_desc})
self.codec = Codec(self.cfg.codec, search_space)
self.get_selected_arch()
indiv_cfg = self.codec.decode(self.elitism)
self.trainer.model_desc = self.elitism.active_net_list()
# self.output_model_desc()
net_desc = NetworkDesc(indiv_cfg)
model = net_desc.to_model()
return model
def _init_model(self):
"""Initialize the model architecture for full train step.
:return: train model
:rtype: class
"""
logging.info('Initializing model')
if 'model_desc' in self.cfg and self.cfg.model_desc is not None:
if self.horovod:
hvd.join()
model_desc = self.cfg.model_desc
self.model_desc = self.cfg.model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
return model
else:
return None
def _init_model(self):
"""Initialize the model architecture for full train step.
:return: train model
:rtype: class
"""
model_cfg = ClassFactory.__configs__.get('model')
if 'model_desc' in model_cfg and model_cfg.model_desc is not None:
model_desc = model_cfg.model_desc
else:
raise ValueError('Model_desc is None for evaluator')
search_space = Config({"search_space": model_desc})
self.codec = Codec(self.cfg.codec, search_space)
self._get_selected_arch()
indiv_cfg = self.codec.decode(self.elitism)
logger.info('Model arch:{}'.format(self.elitism.active_net_list()))
self.model_desc = self.elitism.active_net_list()
net_desc = NetworkDesc(indiv_cfg)
model = net_desc.to_model()
return model
def _init_model(self):
"""Initialize the model architecture for full train step.
:return: train model
:rtype: class
"""
logging.info('Initializing model')
if self.cfg.model_desc:
logging.debug("model_desc: {}".format(self.cfg.model_desc))
_file = FileOps.join_path(
self.worker_path, "model_desc_{}.json".format(self._worker_id))
with open(_file, "w") as f:
json.dump(self.cfg.model_desc, f)
if self.cfg.distributed:
hvd.join()
model_desc = self.cfg.model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
return model
else:
return None
def _init_model(self, model=None):
"""Load model desc from save path and parse to model."""
if model is not None:
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
model_cfg = Config(ClassFactory.__configs__.get('model'))
if "model_desc_file" in model_cfg and model_cfg.model_desc_file is not None:
desc_file = model_cfg.model_desc_file
desc_file = desc_file.replace("{local_base_path}",
self.local_base_path)
if ":" not in desc_file:
desc_file = os.path.abspath(desc_file)
if ":" in desc_file:
local_desc_file = FileOps.join_path(
self.local_output_path, os.path.basename(desc_file))
FileOps.copy_file(desc_file, local_desc_file)
desc_file = local_desc_file
model_desc = Config(desc_file)
logging.info("net_desc:{}".format(model_desc))
elif "model_desc" in model_cfg and model_cfg.model_desc is not None:
model_desc = model_cfg.model_desc
elif "models_folder" in model_cfg and model_cfg.models_folder is not None:
folder = model_cfg.models_folder.replace("{local_base_path}",
self.local_base_path)
pattern = FileOps.join_path(folder, "desc_*.json")
desc_file = glob.glob(pattern)[0]
model_desc = Config(desc_file)
else:
return None
if model_desc is not None:
self.model_desc = model_desc
net_desc = NetworkDesc(model_desc)
model = net_desc.to_model()
if vega.is_torch_backend() and self.use_cuda:
model = model.cuda()
return model
else:
return None