def load(cls, path):
# type: (Text) -> KerasPolicy
from tensorflow.keras.models import load_model
if os.path.exists(path):
featurizer = TrackerFeaturizer.load(path)
meta_path = os.path.join(path, "keras_policy.json")
if os.path.isfile(meta_path):
with io.open(meta_path) as f:
meta = json.loads(f.read())
model_file = os.path.join(path, meta["model"])
graph = tf.Graph()
with graph.as_default():
session = tf.Session()
with session.as_default():
model = load_model(model_file)
return cls(featurizer=featurizer,
model=model,
graph=graph,
session=session,
current_epoch=meta["epochs"])
else:
return cls(featurizer=featurizer)
else:
raise Exception("Failed to load dialogue model. Path {} "
"doesn't exist".format(os.path.abspath(path)))
def load(cls, path):
# type: (Text) -> MemoizationPolicy
featurizer = TrackerFeaturizer.load(path)
memorized_file = os.path.join(path, 'memorized_turns.json')
if os.path.isfile(memorized_file):
with io.open(memorized_file) as f:
data = json.loads(f.read())
return cls(featurizer=featurizer, lookup=data["lookup"])
else:
logger.info("Couldn't load memoization for policy. "
"File '{}' doesn't exist. Falling back to empty "
"turn memory.".format(memorized_file))
return cls()
def load(cls, path):
# type: (Text) -> MemoizationPolicy
featurizer = TrackerFeaturizer.load(path)
memorized_file = os.path.join(path, 'memorized_turns.json')
if os.path.isfile(memorized_file):
with io.open(memorized_file) as f:
data = json.loads(f.read())
return cls(featurizer=featurizer, lookup=data["lookup"])
else:
logger.info("Couldn't load memoization for policy. "
"File '{}' doesn't exist. Falling back to empty "
"turn memory.".format(memorized_file))
return cls()
def load(cls, path: Text) -> 'MemoizationPolicy':
featurizer = TrackerFeaturizer.load(path)
memorized_file = os.path.join(path, 'memorized_turns.json')
if os.path.isfile(memorized_file):
data = json.loads(utils.read_file(memorized_file))
return cls(featurizer=featurizer,
priority=data["priority"],
lookup=data["lookup"])
else:
logger.info("Couldn't load memoization for policy. "
"File '{}' doesn't exist. Falling back to empty "
"turn memory.".format(memorized_file))
return cls()
def load(cls, path):
# type: (Text) -> TorchPolicy
if os.path.exists(path):
featurizer = TrackerFeaturizer.load(path)
meta_path = os.path.join(path, "torch_policy.json")
if os.path.isfile(meta_path):
with io.open(meta_path) as f:
meta = json.loads(f.read())
model_file = os.path.join(path, meta["model"])
model = torch.load(model_file)
return cls(featurizer=featurizer, model=model)
else:
return cls(featurizer=featurizer)
else:
raise Exception("path {} does not exist".format(path))
def load(cls, path):
# type: (Text) -> KerasPolicy
if os.path.exists(path):
featurizer = TrackerFeaturizer.load(path)
meta_path = os.path.join(path, "keras_policy.json")
if os.path.isfile(meta_path):
with io.open(meta_path) as f:
meta = json.loads(f.read())
model_arch = cls._load_model_arch(path, meta)
return cls(featurizer=featurizer,
model=cls._load_weights_for_model(
path, model_arch, meta),
current_epoch=meta["epochs"])
else:
return cls(featurizer=featurizer)
else:
raise Exception("Failed to load dialogue model. Path {} "
"doesn't exist".format(os.path.abspath(path)))
def load(cls, path: Text) -> Policy:
filename = os.path.join(path, 'sklearn_model.pkl')
if not os.path.exists(path):
raise OSError("Failed to load dialogue model. Path {} "
"doesn't exist".format(os.path.abspath(filename)))
featurizer = TrackerFeaturizer.load(path)
assert isinstance(featurizer, MaxHistoryTrackerFeaturizer), \
("Loaded featurizer of type {}, should be "
"MaxHistoryTrackerFeaturizer.".format(type(featurizer).__name__))
policy = cls(featurizer=featurizer)
with open(filename, 'rb') as f:
state = pickle.load(f)
vars(policy).update(state)
logger.info("Loaded sklearn model")
return policy
def load(cls, path):
# type: (Text) -> KerasPolicy
if os.path.exists(path):
featurizer = TrackerFeaturizer.load(path)
meta_path = os.path.join(path, "keras_policy.json")
if os.path.isfile(meta_path):
with io.open(meta_path) as f:
meta = json.loads(f.read())
model_arch = cls._load_model_arch(path, meta)
return cls(
featurizer=featurizer,
model=cls._load_weights_for_model(path,
model_arch,
meta),
current_epoch=meta["epochs"])
else:
return cls(featurizer=featurizer)
else:
raise Exception("Failed to load dialogue model. Path {} "
"doesn't exist".format(os.path.abspath(path)))
def load(cls, path):
# type: (Text) -> Policy
filename = os.path.join(path, 'sklearn_model.pkl')
if not os.path.exists(path):
raise OSError("Failed to load dialogue model. Path {} "
"doesn't exist".format(os.path.abspath(filename)))
featurizer = TrackerFeaturizer.load(path)
assert isinstance(featurizer, MaxHistoryTrackerFeaturizer), \
("Loaded featurizer of type {}, should be "
"MaxHistoryTrackerFeaturizer.".format(type(featurizer).__name__))
policy = cls(featurizer=featurizer)
with open(filename, 'rb') as f:
state = pickle.load(f)
vars(policy).update(state)
logger.info("Loaded sklearn model")
return policy
def test_fail_to_load_non_existent_featurizer():
assert TrackerFeaturizer.load("non_existent_class") is None
def test_fail_to_load_non_existent_featurizer():
assert TrackerFeaturizer.load("non_existent_class") is None
