def __init__(self, args, kg, pn, fn_kg, fn, fn_secondary_kg=None):
super(RewardShapingPolicyGradient, self).__init__(args, kg, pn)
# super(RewardShapingPolicyGradient, self).__init__(args, kg, pn)
self.reward_shaping_threshold = args.reward_shaping_threshold
# Fact network modules
self.fn_kg = fn_kg
self.fn = fn
self.fn_secondary_kg = fn_secondary_kg
self.mu = args.mu
fn_model = self.fn_model
if fn_model in ['conve']:
fn_state_dict = torch.load(args.conve_state_dict_path)
# fn_state_dict = torch.load(args.conve_state_dict_path, map_location=LOAD_FLAG)
# print("CHANGE MAP_LOCATION:", LOAD_FLAG)
fn_nn_state_dict = get_conve_nn_state_dict(fn_state_dict)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
self.fn.load_state_dict(fn_nn_state_dict)
elif fn_model == 'distmult':
fn_state_dict = torch.load(args.distmult_state_dict_path)
fn_kg_state_dict = get_distmult_kg_state_dict(fn_state_dict)
elif fn_model == 'complex':
fn_state_dict = torch.load(args.complex_state_dict_path)
fn_kg_state_dict = get_complex_kg_state_dict(fn_state_dict)
elif fn_model == 'hypere':
fn_state_dict = torch.load(args.conve_state_dict_path)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
else:
raise NotImplementedError
# try:
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in state_dict.items():
# name = 'module.' + k # add `module.`
# new_state_dict[name] = v
# # load params
# # model.load_state_dict(new_state_dict)
# self.net.load_state_dict(new_state_dict)
# except Exception as e:
# print(e)
self.fn_kg.load_state_dict(fn_kg_state_dict)
if fn_model == 'hypere':
complex_state_dict = torch.load(args.complex_state_dict_path)
complex_kg_state_dict = get_complex_kg_state_dict(complex_state_dict)
self.fn_secondary_kg.load_state_dict(complex_kg_state_dict)
self.fn.eval()
self.fn_kg.eval()
ops.detach_module(self.fn)
ops.detach_module(self.fn_kg)
if fn_model == 'hypere':
self.fn_secondary_kg.eval()
ops.detach_module(self.fn_secondary_kg)
def __init__(self, args, kg, pn, fn_kg, fn, fn_secondary_kg=None):
super(RewardShapingPolicyGradient, self).__init__(args, kg, pn)
self.reward_shaping_threshold = args.reward_shaping_threshold
# Fact network modules
self.fn_kg = fn_kg
self.fn = fn
self.fn_secondary_kg = fn_secondary_kg
self.mu = args.mu
fn_model = self.fn_model
if fn_model in ["conve"]:
fn_state_dict = torch.load(
args.conve_state_dict_path, map_location=("cuda:" + str(args.gpu))
)
fn_nn_state_dict = get_conve_nn_state_dict(fn_state_dict)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
self.fn.load_state_dict(fn_nn_state_dict)
elif fn_model == "distmult":
fn_state_dict = torch.load(
args.distmult_state_dict_path, map_location=("cuda:" + str(args.gpu))
)
fn_kg_state_dict = get_distmult_kg_state_dict(fn_state_dict)
elif fn_model == "complex":
fn_state_dict = torch.load(
args.complex_state_dict_path, map_location=("cuda:" + str(args.gpu))
)
fn_kg_state_dict = get_complex_kg_state_dict(fn_state_dict)
elif fn_model == "hypere":
fn_state_dict = torch.load(
args.conve_state_dict_path, map_location=("cuda:" + str(args.gpu))
)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
else:
raise NotImplementedError
# ================= newly added ===================
# added to make original embeddings work
fn_kg_state_dict["AGG_W"] = torch.nn.init.xavier_uniform_(torch.nn.Parameter(torch.zeros(400, 200)))
# ================= newly added ===================
self.fn_kg.load_state_dict(fn_kg_state_dict)
if fn_model == "hypere":
complex_state_dict = torch.load(args.complex_state_dict_path)
complex_kg_state_dict = get_complex_kg_state_dict(complex_state_dict)
self.fn_secondary_kg.load_state_dict(complex_kg_state_dict)
self.fn.eval()
self.fn_kg.eval()
ops.detach_module(self.fn)
ops.detach_module(self.fn_kg)
if fn_model == "hypere":
self.fn_secondary_kg.eval()
ops.detach_module(self.fn_secondary_kg)
def __init__(self, args, kg, pn, fn_kg, fn, fn_secondary_kg=None):
super(RewardMinerGradient, self).__init__(args, kg, pn)
self.reward_shaping_threshold = args.reward_shaping_threshold
# Fact network modules
self.fn_kg = fn_kg
self.fn = fn
self.fn_secondary_kg = fn_secondary_kg
self.mu = args.mu
print(self.fn_kg.relation_embeddings.weight.size(),
self.fn_kg.entity_embeddings.weight.size())
fn_model = self.fn_model
if fn_model in ['conve']:
fn_state_dict = torch.load(args.conve_state_dict_path)
fn_nn_state_dict = get_conve_nn_state_dict(fn_state_dict)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
self.fn.load_state_dict(fn_nn_state_dict)
elif fn_model == 'distmult':
fn_state_dict = torch.load(args.distmult_state_dict_path)
fn_kg_state_dict = get_distmult_kg_state_dict(fn_state_dict)
elif fn_model == 'complex':
fn_state_dict = torch.load(args.complex_state_dict_path)
fn_kg_state_dict = get_complex_kg_state_dict(fn_state_dict)
elif fn_model == 'hypere':
fn_state_dict = torch.load(args.conve_state_dict_path)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
else:
raise NotImplementedError
self.fn_kg.load_state_dict(fn_kg_state_dict)
if fn_model == 'hypere':
complex_state_dict = torch.load(args.complex_state_dict_path)
complex_kg_state_dict = get_complex_kg_state_dict(
complex_state_dict)
self.fn_secondary_kg.load_state_dict(complex_kg_state_dict)
print(self.fn_kg.relation_embeddings.weight.size(),
self.fn_kg.entity_embeddings.weight.size())
self.calc_dummy_end_embedding()
print(self.fn_kg.relation_embeddings.weight.size(),
self.fn_kg.entity_embeddings.weight.size())
self.fn.eval()
self.fn_kg.eval()
ops.detach_module(self.fn)
ops.detach_module(self.fn_kg)
if fn_model == 'hypere':
self.fn_secondary_kg.eval()
ops.detach_module(self.fn_secondary_kg)
def __init__(self, args, kg, pn, fn_kg, fn, fn_secondary_kg=None):
super(RewardShapingPolicyGradient, self).__init__(args, kg, pn)
self.reward_shaping_threshold = args.reward_shaping_threshold
# Fact network modules
self.fn_kg = fn_kg
self.fn = fn
self.fn_secondary_kg = fn_secondary_kg
self.mu = args.mu
fn_model = self.fn_model
if fn_model in ['conve']:
fn_state_dict = torch.load(args.conve_state_dict_path,
map_location='cuda:' + str(args.gpu))
fn_nn_state_dict = get_conve_nn_state_dict(fn_state_dict)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
self.fn.load_state_dict(fn_nn_state_dict)
elif fn_model == 'distmult':
fn_state_dict = torch.load(args.distmult_state_dict_path,
map_location='cuda:' + str(args.gpu))
fn_kg_state_dict = get_distmult_kg_state_dict(fn_state_dict)
elif fn_model == 'complex':
fn_state_dict = torch.load(args.complex_state_dict_path,
map_location='cuda:' + str(args.gpu))
fn_kg_state_dict = get_complex_kg_state_dict(fn_state_dict)
elif fn_model == 'hypere':
fn_state_dict = torch.load(args.conve_state_dict_path,
map_location='cuda:' + str(args.gpu))
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
else:
raise NotImplementedError
self.fn_kg.load_state_dict(fn_kg_state_dict)
if fn_model == 'hypere':
complex_state_dict = torch.load(args.complex_state_dict_path,
map_location='cuda:' +
str(args.gpu))
complex_kg_state_dict = get_complex_kg_state_dict(
complex_state_dict)
self.fn_secondary_kg.load_state_dict(complex_kg_state_dict)
self.fn.eval()
self.fn_kg.eval()
ops.detach_module(self.fn)
ops.detach_module(self.fn_kg)
if fn_model == 'hypere':
self.fn_secondary_kg.eval()
ops.detach_module(self.fn_secondary_kg)
def __init__(self, args, kg, agent, fn_kg, fn, fn_secondary_kg=None):
super(RewardShapingPolicyGradient, self).__init__(args, kg, agent)
self.reward_shaping_threshold = args.reward_shaping_threshold
# Fact network modules
self.fn_kg = fn_kg
self.fn = fn
self.fn_secondary_kg = fn_secondary_kg
self.mu = args.mu
fn_model = self.fn_model
if fn_model in ["conve"]:
fn_state_dict = torch.load(args.conve_state_dict_path)
fn_nn_state_dict = get_conve_nn_state_dict(fn_state_dict)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
self.fn.load_state_dict(fn_nn_state_dict)
elif fn_model == "distmult":
fn_state_dict = torch.load(args.distmult_state_dict_path)
fn_kg_state_dict = get_distmult_kg_state_dict(fn_state_dict)
elif fn_model == "complex":
fn_state_dict = torch.load(args.complex_state_dict_path)
fn_kg_state_dict = get_complex_kg_state_dict(fn_state_dict)
elif fn_model == "hypere":
fn_state_dict = torch.load(args.conve_state_dict_path)
fn_kg_state_dict = get_conve_kg_state_dict(fn_state_dict)
else:
raise NotImplementedError
self.fn_kg.load_state_dict(fn_kg_state_dict)
if fn_model == "hypere":
complex_state_dict = torch.load(args.complex_state_dict_path)
complex_kg_state_dict = get_complex_kg_state_dict(
complex_state_dict)
self.fn_secondary_kg.load_state_dict(complex_kg_state_dict)
self.fn.eval()
self.fn_kg.eval()
ops.detach_module(self.fn)
ops.detach_module(self.fn_kg)
if fn_model == "hypere":
self.fn_secondary_kg.eval()
ops.detach_module(self.fn_secondary_kg)