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model_builder.py
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model_builder.py
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import torch
from pprint import pprint
import config
from utils.manager import PathManager
from model import *
def buildModel(path_manager: PathManager,
task_config=None,
model_params: config.ParamsConfig = None,
loss_func=None,
data_source=None,):
if model_params is None:
model_params = config.params
if task_config is None:
task_config = config.task
try:
model = ModelSwitch[model_params.ModelName](path_manager, model_params, task_config, loss_func, data_source)\
.cuda()
except KeyError:
raise ValueError("[ModelBuilder] No matched model implementation for '%s'"
% model_params.ModelName)
# 组装预训练的参数
if len(task_config.PreloadStateDictVersions) > 0:
remained_model_keys = [n for n,_ in model.named_parameters()]
unexpected_keys = []
for version in task_config.PreloadStateDictVersions:
pm = PathManager(dataset=task_config.Dataset,
version=version,
model_name=model_params.ModelName)
state_dict = torch.load(pm.model())
load_result = model.load_state_dict(state_dict, strict=False)
for k in state_dict.keys():
if k not in load_result.unexpected_keys and k in remained_model_keys:
remained_model_keys.remove(k)
unexpected_keys.extend(load_result.unexpected_keys)
if len(remained_model_keys) > 0:
print(f'[buildModel] Preloading, unloaded keys:')
pprint(remained_model_keys)
if len(unexpected_keys) > 0:
print(f'[buildModel] Preloading, unexpected keys:')
pprint(unexpected_keys)
return model
def _ProtoNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return ProtoNet(model_params, path_manager, loss_func, data_source)
def _NnNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return NnNet(model_params, path_manager, loss_func, data_source)
def _HAPNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return HAPNet(model_params, path_manager, loss_func, data_source, task_params.Episode.k)
def _SIMPLE(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return SIMPLE(model_params, path_manager, loss_func, data_source)
def _IMP(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return IMP(model_params, path_manager, loss_func, data_source)
def _PostProtoNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return PostProtoNet(model_params, path_manager, loss_func, data_source)
def _MLossProtoNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return MLossProtoNet(model_params, path_manager, loss_func, data_source)
def _MLossSIMPLE(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return MLossSIMPLE(model_params, path_manager, loss_func, data_source)
def _MLossIMP(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return MLossIMP(model_params, path_manager, loss_func, data_source)
def _FEAT(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return FEAT(model_params, path_manager, loss_func, data_source)
def _ConvProtoNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return ConvProtoNet(model_params, path_manager, loss_func, data_source, task_params.Episode.k)
def _InductionNet(path_manager: PathManager,
model_params: config.ParamsConfig,
task_params: config.TaskConfig,
loss_func,
data_source):
return InductionNet(model_params, path_manager, loss_func, data_source)
ModelSwitch = {
'ProtoNet': _ProtoNet,
'NnNet': _NnNet,
'HAPNet': _HAPNet,
'SIMPLE': _SIMPLE,
'IMP': _IMP,
'FEAT': _FEAT,
'ConvProtoNet': _ConvProtoNet,
'InductionNet': _InductionNet,
'PostProtoNet': _PostProtoNet,
'MLossProtoNet': _MLossProtoNet,
"MLossSIMPLE": _MLossSIMPLE,
'MLossIMP': _MLossIMP,
}