def from_module(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 transform_architecture(model, pretrained_model_file=None):
"""Transform architecture."""
if not hasattr(model, "_arch_params") or not model._arch_params or \
PipeStepConfig.pipe_step.get("type") == "TrainPipeStep":
return model
model._apply_names()
logging.info(
"Start to transform architecture, model arch params type: {}".format(
model._arch_params_type))
ConnectionsArchParamsCombiner().combine(model)
if vega.is_ms_backend():
from mindspore.train.serialization import load_checkpoint
changed_name_list = []
mask_weight_list = []
for name, module in model.named_modules():
if not ClassFactory.is_exists(model._arch_params_type,
module.model_name):
continue
changed_name_list, mask_weight_list = decode_fn_ms(
module, changed_name_list, mask_weight_list)
assert len(changed_name_list) == len(mask_weight_list)
# change model and rebuild
model_desc = model.desc
root_name = [
name for name in list(model_desc.keys())
if name not in ('type', '_arch_params')
]
for changed_name, mask in zip(changed_name_list, mask_weight_list):
name = changed_name.split('.')
name[0] = root_name[int(name[0])]
assert len(name) <= 6
if len(name) == 6:
model_desc[name[0]][name[1]][name[2]][name[3]][name[4]][
name[5]] = sum(mask)
if len(name) == 5:
model_desc[name[0]][name[1]][name[2]][name[3]][name[4]] = sum(
mask)
if len(name) == 4:
model_desc[name[0]][name[1]][name[2]][name[3]] = sum(mask)
if len(name) == 3:
model_desc[name[0]][name[1]][name[2]] = sum(mask)
if len(name) == 2:
model_desc[name[0]][name[1]] = sum(mask)
network = NetworkDesc(model_desc)
model = network.to_model()
model_desc.pop(
'_arch_params') if '_arch_params' in model_desc else model_desc
model.desc = model_desc
# change weight
if hasattr(model, "pretrained"):
pretrained_weight = model.pretrained(pretrained_model_file)
load_checkpoint(pretrained_weight, net=model)
os.remove(pretrained_weight)
else:
for name, module in model.named_modules():
if not ClassFactory.is_exists(model._arch_params_type,
module.model_name):
continue
arch_cls = ClassFactory.get_cls(model._arch_params_type,
module.model_name)
decode_fn(module, arch_cls)
module.register_forward_pre_hook(arch_cls.fit_weights)
module.register_forward_hook(module.clear_module_arch_params)
return model