def __init__(
self,
task="cv",
input_width=32,
input_channel=3,
n_output_node=10,
algorithm_name="Bayesian",
optimize_mode="maximize",
path="model_path",
verbose=True,
beta=Constant.BETA,
t_min=Constant.T_MIN,
max_model_size=Constant.MAX_MODEL_SIZE,
default_model_len=Constant.MODEL_LEN,
default_model_width=Constant.MODEL_WIDTH,
):
"""
initilizer of the NetworkMorphismTuner.
"""
if not os.path.exists(path):
os.makedirs(path)
self.path = os.path.join(os.getcwd(), path)
if task == "cv":
self.generators = [CnnGenerator]
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError(
'{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
self.t_min = t_min
self.beta = beta
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode,
self.beta)
self.training_queue = []
self.descriptors = []
self.history = []
self.max_model_size = max_model_size
self.default_model_len = default_model_len
self.default_model_width = default_model_width
self.search_space = dict()
class NetworkMorphismTuner(Tuner):
"""
NetworkMorphismTuner is a tuner which using network morphism techniques.
Attributes
----------
n_classes : int
The class number or output node number (default: ``10``)
input_shape : tuple
A tuple including: (input_width, input_width, input_channel)
t_min : float
The minimum temperature for simulated annealing. (default: ``Constant.T_MIN``)
beta : float
The beta in acquisition function. (default: ``Constant.BETA``)
algorithm_name : str
algorithm name used in the network morphism (default: ``"Bayesian"``)
optimize_mode : str
optimize mode "minimize" or "maximize" (default: ``"minimize"``)
verbose : bool
verbose to print the log (default: ``True``)
bo : BayesianOptimizer
The optimizer used in networkmorphsim tuner.
max_model_size : int
max model size to the graph (default: ``Constant.MAX_MODEL_SIZE``)
default_model_len : int
default model length (default: ``Constant.MODEL_LEN``)
default_model_width : int
default model width (default: ``Constant.MODEL_WIDTH``)
search_space : dict
"""
def __init__(
self,
task="cv",
input_width=32,
input_channel=3,
n_output_node=10,
algorithm_name="Bayesian",
optimize_mode="maximize",
path="model_path",
verbose=True,
beta=Constant.BETA,
t_min=Constant.T_MIN,
max_model_size=Constant.MAX_MODEL_SIZE,
default_model_len=Constant.MODEL_LEN,
default_model_width=Constant.MODEL_WIDTH,
):
"""
initilizer of the NetworkMorphismTuner.
"""
if not os.path.exists(path):
os.makedirs(path)
self.path = os.path.join(os.getcwd(), path)
if task == "cv":
self.generators = [CnnGenerator]
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError(
'{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
self.t_min = t_min
self.beta = beta
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode,
self.beta)
self.training_queue = []
self.descriptors = []
self.history = []
self.max_model_size = max_model_size
self.default_model_len = default_model_len
self.default_model_width = default_model_width
self.search_space = dict()
def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in neural architecture.
"""
self.search_space = search_space
def generate_parameters(self, parameter_id, **kwargs):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
generated_graph = None
if not self.training_queue:
new_father_id, generated_graph = self.generate()
new_model_id = self.model_count
self.model_count += 1
self.training_queue.append(
(generated_graph, new_father_id, new_model_id))
self.descriptors.append(generated_graph.extract_descriptor())
graph, father_id, model_id = self.training_queue.pop(0)
# from graph to json
json_model_path = os.path.join(self.path, str(model_id) + ".json")
json_out = graph_to_json(graph, json_model_path)
self.total_data[parameter_id] = (json_out, father_id, model_id)
return json_out
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""
Record an observation of the objective function.
Parameters
----------
parameter_id : int
the id of a group of paramters that generated by nni manager.
parameters : dict
A group of parameters.
value : dict/float
if value is dict, it should have "default" key.
"""
reward = extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError("Received parameter_id not in total_data.")
(_, father_id, model_id) = self.total_data[parameter_id]
graph = self.bo.searcher.load_model_by_id(model_id)
# to use the value and graph
self.add_model(reward, model_id)
self.update(father_id, graph, reward, model_id)
def init_search(self):
"""
Call the generators to generate the initial architectures for the search.
"""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_model_width)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
if self.verbose:
logger.info("Initialization finished.")
def generate(self):
"""
Generate the next neural architecture.
Returns
-------
other_info : any object
Anything to be saved in the training queue together with the architecture.
generated_graph : Graph
An instance of Graph.
"""
generated_graph, new_father_id = self.bo.generate(self.descriptors)
if new_father_id is None:
new_father_id = 0
generated_graph = self.generators[0](self.n_classes,
self.input_shape).generate(
self.default_model_len,
self.default_model_width)
return new_father_id, generated_graph
def update(self, other_info, graph, metric_value, model_id):
"""
Update the controller with evaluation result of a neural architecture.
Parameters
----------
other_info: any object
In our case it is the father ID in the search tree.
graph: Graph
An instance of Graph. The trained neural architecture.
metric_value: float
The final evaluated metric value.
model_id: int
"""
father_id = other_info
self.bo.fit([graph.extract_descriptor()], [metric_value])
self.bo.add_child(father_id, model_id)
def add_model(self, metric_value, model_id):
"""
Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
if self.verbose:
logger.info("Saving model.")
# Update best_model text file
ret = {"model_id": model_id, "metric_value": metric_value}
self.history.append(ret)
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, "best_model.txt"), "w")
file.write("best model: " + str(model_id))
file.close()
return ret
def get_best_model_id(self):
"""
Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history,
key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["model_id"]
def load_model_by_id(self, model_id):
"""
Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
with open(os.path.join(self.path, str(model_id) + ".json")) as fin:
json_str = fin.read().replace("\n", "")
load_model = json_to_graph(json_str)
return load_model
def load_best_model(self):
"""
Get the best model by model id
Returns
-------
load_model : Graph
the model graph representation
"""
return self.load_model_by_id(self.get_best_model_id())
def get_metric_value_by_id(self, model_id):
"""
Get the model metric valud by its model_id
Parameters
----------
model_id : int
model index
Returns
-------
float
the model metric
"""
for item in self.history:
if item["model_id"] == model_id:
return item["metric_value"]
return None
def import_data(self, data):
pass
class NetworkMorphismTuner(Tuner):
"""NetworkMorphismTuner is a tuner which using network morphism techniques."""
def __init__(
self,
task="cv",
input_width=32,
input_channel=3,
n_output_node=10,
algorithm_name="Bayesian",
optimize_mode="maximize",
path="model_path",
verbose=True,
beta=Constant.BETA,
t_min=Constant.T_MIN,
max_model_size=Constant.MAX_MODEL_SIZE,
default_model_len=Constant.MODEL_LEN,
default_model_width=Constant.MODEL_WIDTH,
):
""" initilizer of the NetworkMorphismTuner.
Keyword Arguments:
task {str} -- [task mode, such as "cv","common" etc.] (default: {"cv"})
input_width {int} -- [input sample shape] (default: {32})
input_channel {int} -- [input sample shape] (default: {3})
n_output_node {int} -- [output node number] (default: {10})
algorithm_name {str} -- [algorithm name used in the network morphism] (default: {"Bayesian"})
optimize_mode {str} -- [optimize mode "minimize" or "maximize"] (default: {"minimize"})
path {str} -- [default mode path to save the model file] (default: {"model_path"})
verbose {bool} -- [verbose to print the log] (default: {True})
beta {float} -- [The beta in acquisition function. (refer to our paper)] (default: {Constant.BETA})
t_min {float} -- [The minimum temperature for simulated annealing.] (default: {Constant.T_MIN})
max_model_size {int} -- [max model size to the graph] (default: {Constant.MAX_MODEL_SIZE})
default_model_len {int} -- [default model length] (default: {Constant.MODEL_LEN})
default_model_width {int} -- [default model width] (default: {Constant.MODEL_WIDTH})
"""
if not os.path.exists(path):
os.makedirs(path)
self.path = os.path.join(os.getcwd(), path)
if task == "cv":
self.generators = [CnnGenerator]
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError(
'{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
self.t_min = t_min
self.beta = beta
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode,
self.beta)
self.training_queue = []
# self.x_queue = []
# self.y_queue = []
self.descriptors = []
self.history = []
self.max_model_size = max_model_size
self.default_model_len = default_model_len
self.default_model_width = default_model_width
self.search_space = dict()
def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in neural architecture.
"""
self.search_space = search_space
def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
generated_graph = None
if not self.training_queue:
new_father_id, generated_graph = self.generate()
new_model_id = self.model_count
self.model_count += 1
self.training_queue.append(
(generated_graph, new_father_id, new_model_id))
self.descriptors.append(generated_graph.extract_descriptor())
graph, father_id, model_id = self.training_queue.pop(0)
# from graph to json
json_model_path = os.path.join(self.path, str(model_id) + ".json")
json_out = graph_to_json(graph, json_model_path)
self.total_data[parameter_id] = (json_out, father_id, model_id)
return json_out
def receive_trial_result(self, parameter_id, parameters, value):
""" Record an observation of the objective function.
Arguments:
parameter_id : int
parameters : dict of parameters
value: final metrics of the trial, including reward
Raises:
RuntimeError -- Received parameter_id not in total_data.
"""
reward = self.extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError("Received parameter_id not in total_data.")
(_, father_id, model_id) = self.total_data[parameter_id]
graph = self.bo.searcher.load_model_by_id(model_id)
# to use the value and graph
self.add_model(reward, model_id)
self.update(father_id, graph, reward, model_id)
def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_model_width)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
if self.verbose:
logger.info("Initialization finished.")
def generate(self):
"""Generate the next neural architecture.
Returns:
other_info: Anything to be saved in the training queue together with the architecture.
generated_graph: An instance of Graph.
"""
generated_graph, new_father_id = self.bo.generate(self.descriptors)
if new_father_id is None:
new_father_id = 0
generated_graph = self.generators[0](self.n_classes,
self.input_shape).generate(
self.default_model_len,
self.default_model_width)
return new_father_id, generated_graph
def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture.
Args:
other_info: Anything. In our case it is the father ID in the search tree.
graph: An instance of Graph. The trained neural architecture.
metric_value: The final evaluated metric value.
model_id: An integer.
"""
father_id = other_info
self.bo.fit([graph.extract_descriptor()], [metric_value])
self.bo.add_child(father_id, model_id)
def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Arguments:
metric_value: int --metric_value
graph: dict -- graph
model_id: int -- model_id
Returns:
model dict
"""
if self.verbose:
logger.info("Saving model.")
# Update best_model text file
ret = {"model_id": model_id, "metric_value": metric_value}
self.history.append(ret)
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, "best_model.txt"), "w")
file.write("best model: " + str(model_id))
file.close()
# descriptor = graph.extract_descriptor()
# self.x_queue.append(descriptor)
# self.y_queue.append(metric_value)
return ret
def get_best_model_id(self):
""" Get the best model_id from history using the metric value
Returns:
int -- the best model_id
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history,
key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["model_id"]
def load_model_by_id(self, model_id):
"""Get the model by model_id
Arguments:
model_id {int} -- model index
Returns:
Graph -- the model graph representation
"""
with open(os.path.join(self.path, str(model_id) + ".json")) as fin:
json_str = fin.read().replace("\n", "")
load_model = json_to_graph(json_str)
return load_model
def load_best_model(self):
""" Get the best model by model id
Returns:
Graph -- the best model graph representation
"""
return self.load_model_by_id(self.get_best_model_id())
def get_metric_value_by_id(self, model_id):
""" Get the model metric valud by its model_id
Arguments:
model_id {int} -- model index
Returns:
float -- the model metric
"""
for item in self.history:
if item["model_id"] == model_id:
return item["metric_value"]
return None
def __init__(
self,
task="cv",
input_width=32,
input_channel=3,
n_output_node=10,
algorithm_name="Bayesian",
optimize_mode="maximize",
path="model_path",
verbose=True,
beta=Constant.BETA,
t_min=Constant.T_MIN,
max_model_size=Constant.MAX_MODEL_SIZE,
default_model_len=Constant.MODEL_LEN,
default_model_width=Constant.MODEL_WIDTH,
):
""" initilizer of the NetworkMorphismTuner.
Parameters
----------
task : str
task mode, such as "cv","common" etc. (default: {"cv"})
input_width : int
input sample shape (default: {32})
input_channel : int
input sample shape (default: {3})
n_output_node : int
output node number (default: {10})
algorithm_name : str
algorithm name used in the network morphism (default: {"Bayesian"})
optimize_mode : str
optimize mode "minimize" or "maximize" (default: {"minimize"})
path : str
default mode path to save the model file (default: {"model_path"})
verbose : bool
verbose to print the log (default: {True})
beta : float
The beta in acquisition function. (default: {Constant.BETA})
t_min : float
The minimum temperature for simulated annealing. (default: {Constant.T_MIN})
max_model_size : int
max model size to the graph (default: {Constant.MAX_MODEL_SIZE})
default_model_len : int
default model length (default: {Constant.MODEL_LEN})
default_model_width : int
default model width (default: {Constant.MODEL_WIDTH})
"""
if not os.path.exists(path):
os.makedirs(path)
self.path = os.path.join(os.getcwd(), path)
if task == "cv":
self.generators = [CnnGenerator]
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError('{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
self.t_min = t_min
self.beta = beta
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode, self.beta)
self.training_queue = []
self.descriptors = []
self.history = []
self.max_model_size = max_model_size
self.default_model_len = default_model_len
self.default_model_width = default_model_width
self.search_space = dict()
class NetworkMorphismTuner(Tuner):
"""NetworkMorphismTuner is a tuner which using network morphism techniques."""
def __init__(
self,
task="cv",
input_width=32,
input_channel=3,
n_output_node=10,
algorithm_name="Bayesian",
optimize_mode="maximize",
path="model_path",
verbose=True,
beta=Constant.BETA,
t_min=Constant.T_MIN,
max_model_size=Constant.MAX_MODEL_SIZE,
default_model_len=Constant.MODEL_LEN,
default_model_width=Constant.MODEL_WIDTH,
init_model_dir= Constant.FILE_DIR,
):
""" initilizer of the NetworkMorphismTuner.
Parameters
----------
task : str
task mode, such as "cv","common" etc. (default: {"cv"})
input_width : int
input sample shape (default: {32})
input_channel : int
input sample shape (default: {3})
n_output_node : int
output node number (default: {10})
algorithm_name : str
algorithm name used in the network morphism (default: {"Bayesian"})
optimize_mode : str
optimize mode "minimize" or "maximize" (default: {"minimize"})
path : str
default mode path to save the model file (default: {"model_path"})
verbose : bool
verbose to print the log (default: {True})
beta : float
The beta in acquisition function. (default: {Constant.BETA})
t_min : float
The minimum temperature for simulated annealing. (default: {Constant.T_MIN})
max_model_size : int
max model size to the graph (default: {Constant.MAX_MODEL_SIZE})
default_model_len : int
default model length (default: {Constant.MODEL_LEN})
default_model_width : int
default model width (default: {Constant.MODEL_WIDTH})
init_model_list : list
store the remorph models , every node initializes
"""
if not os.path.exists(path):
os.makedirs(path)
self.path = os.path.join(os.getcwd(), path)
if task == "cv":
self.generators = [CnnGenerator]
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError('{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
self.t_min = t_min
self.beta = beta
self.algorithm_name = algorithm_name
self.optimize_mode = OptimizeMode(optimize_mode)
self.json = None
self.total_data = {}
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode, self.beta)
self.training_queue = []
self.descriptors = []
self.history = []
self.max_model_size = max_model_size
self.default_model_len = default_model_len
self.default_model_width = default_model_width
self.init_model_dir = []
if os.path.isdir(init_model_dir):
for parent, dirnames,model_files in os.walk(init_model_dir):
if model_files is not None:
for model_file_name in model_files:
model_file=os.path.join(parent, model_file_name)
self.init_model_dir.append(model_file)
else:
self.init_model_dir.append(0)
elif os.path.isfile(init_model_dir):
self.init_model_dir.append(init_model_dir)
else:
self.init_model_dir.append(0)
self.search_space = dict()
def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in neural architecture.
"""
self.search_space = search_space
def set_descriptors(self, model_id, generated_graph):
self.descriptors[model_id] = generated_graph.extract_descriptor()
def fake_generate_parameters(self, parameter_id, **kwargs):
"""
Returns a initialized model.
"""
self.init_search()
new_father_id = None
generated_graph = None
graph, father_id, model_id = self.training_queue.pop(0)
# from graph to json
json_model_path = os.path.join(self.path, str(model_id) + ".json")
json_out = graph_to_json(graph, json_model_path)
self.total_data[parameter_id] = (json_out, father_id, model_id)
return json_out
def generate_parameters(self, parameter_id, **kwargs):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
#If there is no history, slave node will use the fake model.
if not self.history:
print("If there is no history, generate_parameters should not be called!")
exit(1)
total_start=time.time()
rate = 1
if (os.path.exists(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/generate_time") and os.path.exists(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/train_time")):
with open(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/generate_time", "r") as f:
generate_time = float(f.read())
with open(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/train_time", "r") as f:
train_time = float(f.read())
if (generate_time != 0) and (train_time != 0):
realrate = int(train_time / generate_time)
if (realrate < 5) and (realrate > 1):
rate = int(realrate)
if (realrate <= 1):
rate = 1
for i in range(rate):
start=time.time()
new_father_id = None
generated_graph = None
if not self.training_queue:
new_father_id, generated_graph = self.generate()
father_id,json_out,new_model_id = self.total_data[parameter_id]
self.training_queue.append((generated_graph, new_father_id, new_model_id))
#self.descriptors.append(generated_graph.extract_descriptor())
else:
print("training_queue should be an empty list.")
exit(1)
graph, father_id, model_id = self.training_queue.pop(0)
# from graph to json
json_model_path = os.path.join(self.path, str(model_id) + ".json")
json_out = graph_to_json(graph, json_model_path)
end=time.time()
#self.total_data[parameter_id] = (json_out, father_id, model_id)
json_and_id="json_out="+str(json_out)+"+father_id="+str(father_id)+"+parameter_id="+str(parameter_id)+"+history="+"True"
lock.acquire()
with open(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/trials/" + str(nni.get_trial_id()) + "/output.log","a+") as f:
f.write("single_generate=" + str(end - start)+"\n")
with open(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/graph.txt","a+") as f:
f.write(json_and_id+"\n")
lock.release()
total_end=time.time()
lock.acquire()
with open(os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/trials/" + str(nni.get_trial_id()) + "/output.log","a+") as f:
f.write("total_generate=" + str(total_end - total_start)+"\n")
lock.release()
totime = total_end - total_start
if totime<0:
totime = 0-totime
with open (os.environ["HOME"] + "/mountdir/nni/experiments/" + str(nni.get_experiment_id()) + "/generate_time","w+") as f:
gt = totime/rate
f.write(str(gt))
#return json_out, father_id
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
""" Record an observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
"""
reward = extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError("Received parameter_id not in total_data.")
(_, father_id, model_id) = self.total_data[parameter_id]
graph = self.bo.searcher.load_model_by_id(model_id)
# to use the value and graph
self.add_model(reward, model_id)
self.update(father_id, graph, reward, model_id)
def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
#if self.verbose:
# logger.info("Initializing search.")
import yaml
trial_concurrency = os.popen('cat /etc/slurm-llnl/slurm.conf|grep NodeName|wc -l')
trial_concurrency = int(trial_concurrency.read().strip())
if trial_concurrency > self.model_count: #判断当前训练的trial是否已超过第一轮trials
#若没有超过第一轮trial，则判断当前的trial是否超过预生成的模型序列，若未超过，则正常设置num=count
# if len(self.init_model_dir) > self.model_count:
# num= self.model_count
# else:
# #若预生成的序列元素少于节点数（第一轮trial个数），则trial重复读取modle，采用取余方式
num = self.model_count % len(self.init_model_dir)
else:
num=0
for generator in self.generators:
if os.path.isfile(self.init_model_dir[num]):
graph = generator(self.n_classes, self.input_shape).pre_generate(
self.init_model_dir[num]
)
else:
graph = generator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_model_width
)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
# add acc fake
if trial_concurrency > model_id:
self.bo.fit([graph.extract_descriptor()], [0.05+model_id*0.01])
# self.bo.add_child(-1, model_id)
self.history.append( {"model_id": model_id, "metric_value": 0.05+model_id*0.01})
if model_id == trial_concurrency-1 :
if len(self.init_model_dir) >= trial_concurrency:
for i in range(trial_concurrency):
self.bo.add_child(i-1, i)
else:
#按正常的model id构建search tree
for i in range(len(self.init_model_dir)):
self.bo.add_child(i-1, i)
#把尾部超出预生成模型路径的model id 取余后加至对应的father id上
for i in range(trial_concurrency-len(self.init_model_dir)):
j=(model_id-trial_concurrency+len(self.init_model_dir)+i+1) % len(self.init_model_dir)
self.bo.add_child(j ,i + len(self.init_model_dir))
ret_tree=self.bo.search_tree.get_dict(0)
def generate(self):
"""Generate the next neural architecture.
Returns
-------
other_info: any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
An instance of Graph.
"""
generated_graph, new_father_id = self.bo.generate(self.descriptors)
if new_father_id is None:
new_father_id = 0
generated_graph = self.generators[0](
self.n_classes, self.input_shape
).generate(self.default_model_len, self.default_model_width)
return new_father_id, generated_graph
def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture.
Parameters
----------
other_info: any object
In our case it is the father ID in the search tree.
graph: Graph
An instance of Graph. The trained neural architecture.
metric_value: float
The final evaluated metric value.
model_id: int
"""
father_id = other_info
t1 = time.time()
self.bo.fit([graph.extract_descriptor()], [metric_value])
trial_concurrency = os.popen('cat /etc/slurm-llnl/slurm.conf|grep NodeName|wc -l')
trial_concurrency = int(trial_concurrency.read().strip())
if model_id >= trial_concurrency :
self.bo.add_child(father_id, model_id)
ret_tree=self.bo.search_tree.get_dict(0)
t2 = time.time()
print("Update time = " + str(t2 - t1))
def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
if self.verbose:
logger.info("Saving model.")
# Update best_model text file
ret = {"model_id": model_id, "metric_value": metric_value}
trial_concurrency = os.popen('cat /etc/slurm-llnl/slurm.conf|grep NodeName|wc -l')
trial_concurrency = int(trial_concurrency.read().strip())
if model_id < trial_concurrency:
for i in range(len(self.history)):
if self.history[i]['model_id'] == model_id:
self.history[i]['metric_value'] = metric_value
break
else:
self.history.append(ret)
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, "best_model.txt"), "w")
file.write("best model: " + str(model_id))
file.close()
return ret
def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history, key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["model_id"]
def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
with open(os.path.join(self.path, str(model_id) + ".json")) as fin:
json_str = fin.read().replace("\n", "")
load_model = json_to_graph(json_str)
return load_model
def load_best_model(self):
""" Get the best model by model id
Returns
-------
load_model : Graph
the model graph representation
"""
return self.load_model_by_id(self.get_best_model_id())
def get_metric_value_by_id(self, model_id):
""" Get the model metric valud by its model_id
Parameters
----------
model_id : int
model index
Returns
-------
float
the model metric
"""
for item in self.history:
if item["model_id"] == model_id:
return item["metric_value"]
return None
def import_data(self, data):
pass
