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Unverified Commit 0168ff1c authored by xuehui's avatar xuehui Committed by GitHub
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update docstring and pylint (#1662) 

* update docstring of batchtuner

* update docstring of batch tuner

* update docstring of evolution tuner

* update docstring and pylint of metis_tuner

* fix pylint related to logger in metis_tuner

* fix pylint

* update

* fix pylint in metis_tuner

* update in networkmorphsim_tuner

* update

* update

* update docstring in hyperopt_tuner

* update batch_tuner

* delete unused space

* update in metis

* update sdk_reference.rst

* update netowrkmorhism

* update networkmorphsim

* update batch_tuner

* update batch_tuner

* update

* update metis

* roll back to print

* update Returns

* update

* delete white space
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docs/en_US/sdk_reference.rst
View file @ 0168ff1c
......@@ -36,6 +36,9 @@ Tuner
.. autoclass:: nni.metis_tuner.metis_tuner.MetisTuner
:members:
.. autoclass:: nni.batch_tuner.batch_tuner.BatchTuner
:members:
Assessor
------------------------
.. autoclass:: nni.assessor.Assessor
......
src/sdk/pynni/nni/batch_tuner/batch_tuner.py
View file @ 0168ff1c
......@@ -31,22 +31,27 @@ TYPE = '_type'
CHOICE = 'choice'
VALUE = '_value'
logger = logging.getLogger('batch_tuner_AutoML')
LOGGER = logging.getLogger('batch_tuner_AutoML')
class BatchTuner(Tuner):
"""
BatchTuner is tuner will running all the configure that user want to run batchly.
Examples
--------
The search space only be accepted like:
```
{
'combine_params': { '_type': 'choice',
'_value': '[{...}, {...}, {...}]',
}
}
```
"""
def __init__(self):
self.count = -1
self.values = []
self._count = -1
self._values = []
def is_valid(self, search_space):
"""
......@@ -55,6 +60,11 @@ class BatchTuner(Tuner):
Parameters
----------
search_space : dict
Returns
-------
None or list
If valid, return candidate values; else return None.
"""
if not len(search_space) == 1:
raise RuntimeError('BatchTuner only supprt one combined-paramreters key.')
......@@ -62,11 +72,14 @@ class BatchTuner(Tuner):
for param in search_space:
param_type = search_space[param][TYPE]
if not param_type == CHOICE:
raise RuntimeError('BatchTuner only supprt one combined-paramreters type is choice.')
else:
raise RuntimeError('BatchTuner only supprt \
one combined-paramreters type is choice.')
if isinstance(search_space[param][VALUE], list):
return search_space[param][VALUE]
raise RuntimeError('The combined-paramreters value in BatchTuner is not a list.')
raise RuntimeError('The combined-paramreters \
value in BatchTuner is not a list.')
return None
def update_search_space(self, search_space):
......@@ -76,7 +89,7 @@ class BatchTuner(Tuner):
----------
search_space : dict
"""
self.values = self.is_valid(search_space)
self._values = self.is_valid(search_space)
def generate_parameters(self, parameter_id, **kwargs):
"""Returns a dict of trial (hyper-)parameters, as a serializable object.
......@@ -84,41 +97,49 @@ class BatchTuner(Tuner):
Parameters
----------
parameter_id : int
Returns
-------
dict
A candidate parameter group.
"""
self.count += 1
if self.count > len(self.values) - 1:
self._count += 1
if self._count > len(self._values) - 1:
raise nni.NoMoreTrialError('no more parameters now.')
return self.values[self.count]
return self._values[self._count]
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
pass
def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
if not self.values:
logger.info("Search space has not been initialized, skip this data import")
if not self._values:
LOGGER.info("Search space has not been initialized, skip this data import")
return
self.values = self.values[(self.count+1):]
self.count = -1
self._values = self._values[(self._count+1):]
self._count = -1
_completed_num = 0
for trial_info in data:
logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
LOGGER .info("Importing data, current processing \
progress %s / %s", _completed_num, len(data))
# simply validate data format
assert "parameter" in trial_info
_params = trial_info["parameter"]
assert "value" in trial_info
_value = trial_info['value']
if not _value:
logger.info("Useless trial data, value is %s, skip this trial data.", _value)
LOGGER.info("Useless trial data, value is %s, skip this trial data.", _value)
continue
_completed_num += 1
if _params in self.values:
self.values.remove(_params)
logger.info("Successfully import data to batch tuner, total data: %d, imported data: %d.", len(data), _completed_num)
if _params in self._values:
self._values.remove(_params)
LOGGER .info("Successfully import data to batch tuner, \
total data: %d, imported data: %d.", len(data), _completed_num)
src/sdk/pynni/nni/evolution_tuner/evolution_tuner.py
View file @ 0168ff1c
......@@ -32,7 +32,9 @@ import nni.parameter_expressions as parameter_expressions
def json2space(x, oldy=None, name=NodeType.ROOT):
"""Change search space from json format to hyperopt format
"""
Change search space from json format to hyperopt format
"""
y = list()
if isinstance(x, dict):
......@@ -59,7 +61,9 @@ def json2space(x, oldy=None, name=NodeType.ROOT):
return y
def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
"""Json to pramaters.
"""
Json to pramaters.
"""
if isinstance(x, dict):
if NodeType.TYPE in x.keys():
......@@ -117,6 +121,17 @@ def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeTyp
class Individual:
"""
Indicidual class to store the indv info.
Attributes
----------
config : str
Search space.
info : str
The str to save information of individual.
result : float
The final metric of a individual.
store_dir : str
save_dir : str
"""
def __init__(self, config=None, info=None, result=None, save_dir=None):
......@@ -124,6 +139,7 @@ class Individual:
Parameters
----------
config : str
A config to represent a group of parameters.
info : str
result : float
save_dir : str
......@@ -140,6 +156,8 @@ class Individual:
def mutation(self, config=None, info=None, save_dir=None):
"""
Mutation by reset state information.
Parameters
----------
config : str
......@@ -177,8 +195,11 @@ class EvolutionTuner(Tuner):
self.population = None
self.space = None
def update_search_space(self, search_space):
"""Update search space.
"""
Update search space.
Search_space contains the information that user pre-defined.
Parameters
......@@ -191,15 +212,19 @@ class EvolutionTuner(Tuner):
self.random_state = np.random.RandomState()
self.population = []
is_rand = dict()
for item in self.space:
is_rand[item] = True
for _ in range(self.population_size):
config = json2parameter(
self.searchspace_json, is_rand, self.random_state)
self.population.append(Individual(config=config))
def generate_parameters(self, parameter_id, **kwargs):
"""Returns a dict of trial (hyper-)parameters, as a serializable object.
"""
This function will returns a dict of trial (hyper-)parameters, as a serializable object.
Parameters
----------
......@@ -207,15 +232,19 @@ class EvolutionTuner(Tuner):
Returns
-------
config : dict
dict
A group of candaidte parameters that evolution tuner generated.
"""
if not self.population:
raise RuntimeError('The population is empty')
pos = -1
for i in range(len(self.population)):
if self.population[i].result is None:
pos = i
break
if pos != -1:
indiv = copy.deepcopy(self.population[pos])
self.population.pop(pos)
......@@ -230,6 +259,7 @@ class EvolutionTuner(Tuner):
self.population[0].config)
is_rand = dict()
mutation_pos = space[random.randint(0, len(space)-1)]
for i in range(len(self.space)):
is_rand[self.space[i]] = (self.space[i] == mutation_pos)
config = json2parameter(
......@@ -238,21 +268,27 @@ class EvolutionTuner(Tuner):
# remove "_index" from config and save params-id
total_config = config
self.total_data[parameter_id] = total_config
config = split_index(total_config)
return config
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
'''Record the result from a trial
"""
Record the result from a trial
Parameters
----------
parameters: dict
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
value is final metrics of the trial.
'''
"""
reward = extract_scalar_reward(value)
if parameter_id not in self.total_data:
raise RuntimeError('Received parameter_id not in total_data.')
# restore the paramsters contains "_index"
......
src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py
View file @ 0168ff1c
......@@ -422,7 +422,8 @@ class HyperoptTuner(Tuner):
misc_by_id[tid]['vals'][key] = [val]
def get_suggestion(self, random_search=False):
"""get suggestion from hyperopt
"""
get suggestion from hyperopt
Parameters
----------
......@@ -473,7 +474,8 @@ class HyperoptTuner(Tuner):
return total_params
def import_data(self, data):
"""Import additional data for tuning
"""
Import additional data for tuning
Parameters
----------
......
src/sdk/pynni/nni/metis_tuner/Regression_GMM/CreateModel.py
View file @ 0168ff1c
......@@ -16,7 +16,8 @@
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
import os
import sys
......@@ -31,7 +32,8 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
'''
Create the Gaussian Mixture Model
'''
samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
samples = [samples_x[i] + [samples_y_aggregation[i]]
for i in range(0, len(samples_x))]
# Sorts so that we can get the top samples
samples = sorted(samples, key=itemgetter(-1))
......@@ -39,13 +41,16 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
samples_goodbatch = samples[0:samples_goodbatch_size]
samples_badbatch = samples[samples_goodbatch_size:]
samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in samples_goodbatch]
samples_x_goodbatch = [sample_goodbatch[0:-1]
for sample_goodbatch in samples_goodbatch]
#samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in samples_badbatch]
samples_x_badbatch = [sample_badbatch[0:-1]
for sample_badbatch in samples_badbatch]
# === Trains GMM clustering models === #
#sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, samples_goodbatch_size - 1))
bgmm_goodbatch = mm.BayesianGaussianMixture(
n_components=max(1, samples_goodbatch_size - 1))
bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
bgmm_goodbatch.fit(samples_x_goodbatch)
......@@ -55,4 +60,3 @@ def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
model['clusteringmodel_good'] = bgmm_goodbatch
model['clusteringmodel_bad'] = bgmm_badbatch
return model
\ No newline at end of file
src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py
View file @ 0168ff1c
......@@ -16,7 +16,8 @@
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
import os
import random
......@@ -33,14 +34,17 @@ CONSTRAINT_UPPERBOUND = None
CONSTRAINT_PARAMS_IDX = []
def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
def _ratio_scores(parameters_value, clusteringmodel_gmm_good,
clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
ratio = clusteringmodel_gmm_good.score(
[parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma
def selection_r(x_bounds,
x_types,
clusteringmodel_gmm_good,
......@@ -60,6 +64,7 @@ def selection_r(x_bounds,
return outputs
def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
......@@ -69,13 +74,14 @@ def selection(x_bounds,
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lowest_mu(\
_ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],\
x_bounds, x_types, minimize_starting_points, \
results = lib_acquisition_function.next_hyperparameter_lowest_mu(
_ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],
x_bounds, x_types, minimize_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
return results
def _rand_with_constraints(x_bounds, x_types):
'''
Random generate the variable with constraints
......@@ -96,6 +102,7 @@ def _rand_with_constraints(x_bounds, x_types):
outputs[i] = random.randint(x_bounds[i][0], x_bounds[i][1])
return outputs
def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
......
src/sdk/pynni/nni/metis_tuner/Regression_GP/OutlierDetection.py
View file @ 0168ff1c
......@@ -17,7 +17,9 @@
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
OutlierDectection.py
"""
import os
import sys
......@@ -30,19 +32,21 @@ sys.path.insert(1, os.path.join(sys.path[0], '..'))
def _outlierDetection_threaded(inputs):
'''
"""
Detect the outlier
'''
"""
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
# Create a diagnostic regression model which removes the sample that we want to evaluate
diagnostic_regressor_gp = gp_create_model.create_model(\
samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
# Create a diagnostic regression model which removes the sample that we
# want to evaluate
diagnostic_regressor_gp = gp_create_model.create_model(
samples_x[0:samples_idx] + samples_x[samples_idx + 1:],
samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
mu, sigma = gp_prediction.predict(samples_x[samples_idx], diagnostic_regressor_gp['model'])
mu, sigma = gp_prediction.predict(
samples_x[samples_idx], diagnostic_regressor_gp['model'])
# 2.33 is the z-score for 98% confidence level
if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
......@@ -52,16 +56,18 @@ def _outlierDetection_threaded(inputs):
"difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)}
return outlier
def outlierDetection_threaded(samples_x, samples_y_aggregation):
'''
"""
Use Multi-thread to detect the outlier
'''
"""
outliers = []
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\
threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]
for samples_idx in range(0, len(samples_x))]
threads_pool = ThreadPool(min(4, len(threads_inputs)))
threads_results = threads_pool.map(_outlierDetection_threaded, threads_inputs)
threads_results = threads_pool.map(
_outlierDetection_threaded, threads_inputs)
threads_pool.close()
threads_pool.join()
......@@ -69,15 +75,13 @@ def outlierDetection_threaded(samples_x, samples_y_aggregation):
if threads_result is not None:
outliers.append(threads_result)
else:
print("error here.")
print("Error: threads_result is None.")
outliers = outliers if outliers else None
return outliers
def outlierDetection(samples_x, samples_y_aggregation):
'''
TODO
'''
outliers = []
for samples_idx, _ in enumerate(samples_x):
#sys.stderr.write("[%s] DEBUG: Evaluating %d of %d samples\n"
......@@ -92,7 +96,8 @@ def outlierDetection(samples_x, samples_y_aggregation):
outliers.append({"samples_idx": samples_idx,
"expected_mu": mu,
"expected_sigma": sigma,
"difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)})
"difference": \
abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)})
outliers = outliers if outliers else None
return outliers
src/sdk/pynni/nni/metis_tuner/lib_acquisition_function.py
View file @ 0168ff1c
......@@ -16,7 +16,11 @@
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
"""
lib_acquisition_function.py
"""
import sys
import numpy
......@@ -33,9 +37,9 @@ def next_hyperparameter_expected_improvement(fun_prediction,
samples_y_aggregation,
minimize_starting_points,
minimize_constraints_fun=None):
'''
"""
"Expected Improvement" acquisition function
'''
"""
best_x = None
best_acquisition_value = None
x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
......@@ -70,6 +74,7 @@ def next_hyperparameter_expected_improvement(fun_prediction,
return outputs
def _expected_improvement(x, fun_prediction, fun_prediction_args,
x_bounds, x_types, samples_y_aggregation,
minimize_constraints_fun):
......@@ -77,7 +82,8 @@ def _expected_improvement(x, fun_prediction, fun_prediction_args,
x = lib_data.match_val_type(x, x_bounds, x_types)
expected_improvement = sys.maxsize
if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
if (minimize_constraints_fun is None) or (
minimize_constraints_fun(x) is True):
mu, sigma = fun_prediction(x, *fun_prediction_args)
loss_optimum = min(samples_y_aggregation)
......@@ -101,9 +107,9 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
x_bounds, x_types,
minimize_starting_points,
minimize_constraints_fun=None):
'''
"""
"Lowest Confidence" acquisition function
'''
"""
best_x = None
best_acquisition_value = None
x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
......@@ -120,10 +126,12 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
x_types,
minimize_constraints_fun))
if (best_acquisition_value) is None or (res.fun < best_acquisition_value):
if (best_acquisition_value) is None or (
res.fun < best_acquisition_value):
res.x = numpy.ndarray.tolist(res.x)
res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
if (minimize_constraints_fun is None) or (
minimize_constraints_fun(res.x) is True):
best_acquisition_value = res.fun
best_x = res.x
......@@ -134,13 +142,15 @@ def next_hyperparameter_lowest_confidence(fun_prediction,
'expected_sigma': sigma, 'acquisition_func': "lc"}
return outputs
def _lowest_confidence(x, fun_prediction, fun_prediction_args,
x_bounds, x_types, minimize_constraints_fun):
# This is only for step-wise optimization
x = lib_data.match_val_type(x, x_bounds, x_types)
ci = sys.maxsize
if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
if (minimize_constraints_fun is None) or (
minimize_constraints_fun(x) is True):
mu, sigma = fun_prediction(x, *fun_prediction_args)
ci = (sigma * 1.96 * 2) / mu
# We want ci to be as large as possible
......@@ -156,9 +166,9 @@ def next_hyperparameter_lowest_mu(fun_prediction,
x_bounds, x_types,
minimize_starting_points,
minimize_constraints_fun=None):
'''
"""
"Lowest Mu" acquisition function
'''
"""
best_x = None
best_acquisition_value = None
x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
......@@ -169,13 +179,15 @@ def next_hyperparameter_lowest_mu(fun_prediction,
x0=starting_point.reshape(1, -1),
bounds=x_bounds_minmax,
method="L-BFGS-B",
args=(fun_prediction, fun_prediction_args, \
args=(fun_prediction, fun_prediction_args,
x_bounds, x_types, minimize_constraints_fun))
if (best_acquisition_value is None) or (res.fun < best_acquisition_value):
if (best_acquisition_value is None) or (
res.fun < best_acquisition_value):
res.x = numpy.ndarray.tolist(res.x)
res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
if (minimize_constraints_fun is None) or (
minimize_constraints_fun(res.x) is True):
best_acquisition_value = res.fun
best_x = res.x
......@@ -189,14 +201,14 @@ def next_hyperparameter_lowest_mu(fun_prediction,
def _lowest_mu(x, fun_prediction, fun_prediction_args,
x_bounds, x_types, minimize_constraints_fun):
'''
"""
Calculate the lowest mu
'''
"""
# This is only for step-wise optimization
x = lib_data.match_val_type(x, x_bounds, x_types)
mu = sys.maxsize
if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
if (minimize_constraints_fun is None) or (
minimize_constraints_fun(x) is True):
mu, _ = fun_prediction(x, *fun_prediction_args)
return mu
\ No newline at end of file
src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py
View file @ 0168ff1c
......@@ -16,7 +16,11 @@
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
"""
lib_constraint_summation.py
"""
import math
import random
......@@ -39,6 +43,7 @@ def check_feasibility(x_bounds, lowerbound, upperbound):
return (x_bounds_lowerbound <= lowerbound <= x_bounds_upperbound) or \
(x_bounds_lowerbound <= upperbound <= x_bounds_upperbound)
def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
'''
Key idea is that we try to move towards upperbound, by randomly choose one
......@@ -55,7 +60,8 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
if x_types[i] == "discrete_int":
x_idx_sorted.append([i, len(x_bounds[i])])
elif (x_types[i] == "range_int") or (x_types[i] == "range_continuous"):
x_idx_sorted.append([i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
x_idx_sorted.append(
[i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
x_idx_sorted = sorted(x_idx_sorted, key=itemgetter(1))
for _ in range(max_retries):
......@@ -77,12 +83,13 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
temp.append(j)
# Randomly pick a number from the integer array
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
outputs[x_idx] = temp[random.randint(
0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(x_bounds[x_idx][0],
min(x_bounds[x_idx][-1], budget_max))
outputs[x_idx] = random.randint(
x_bounds[x_idx][0], min(x_bounds[x_idx][-1], budget_max))
else:
# The last x that we need to assign a random number
......@@ -91,26 +98,28 @@ def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
# This check:
# is our smallest possible value going to overflow the available budget space,
# and is our largest possible value going to underflow the lower bound
# and is our largest possible value going to underflow the
# lower bound
if (x_bounds[x_idx][0] <= budget_max) and \
(x_bounds[x_idx][-1] >= randint_lowerbound):
if x_types[x_idx] == "discrete_int":
temp = []
for j in x_bounds[x_idx]:
# if (j <= budget_max) and (j >= randint_lowerbound):
# if (j <= budget_max) and (j >=
# randint_lowerbound):
if randint_lowerbound <= j <= budget_max:
temp.append(j)
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
outputs[x_idx] = temp[random.randint(
0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(randint_lowerbound,
min(x_bounds[x_idx][1], budget_max))
outputs[x_idx] = random.randint(
randint_lowerbound, min(
x_bounds[x_idx][1], budget_max))
if outputs[x_idx] is None:
break
else:
budget_allocated += outputs[x_idx]
if None not in outputs:
break
return outputs
\ No newline at end of file
src/sdk/pynni/nni/metis_tuner/lib_data.py
View file @ 0168ff1c
......@@ -16,7 +16,8 @@
# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
import math
import random
......@@ -56,7 +57,7 @@ def rand(x_bounds, x_types):
temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
outputs.append(temp)
elif x_types[i] == "range_int":
temp = random.randint(x_bounds[i][0], x_bounds[i][1] -1)
temp = random.randint(x_bounds[i][0], x_bounds[i][1] - 1)
outputs.append(temp)
elif x_types[i] == "range_continuous":
temp = random.uniform(x_bounds[i][0], x_bounds[i][1])
......
src/sdk/pynni/nni/networkmorphism_tuner/bayesian.py
View file @ 0168ff1c
......@@ -38,7 +38,7 @@ from nni.networkmorphism_tuner.layers import is_layer
def layer_distance(a, b):
"""The distance between two layers."""
# pylint: disable=unidiomatic-typecheck
if type(a) != type(b):
if not isinstance(a, type(b)):
return 1.0
if is_layer(a, "Conv"):
att_diff = [
......@@ -96,7 +96,8 @@ def skip_connection_distance(a, b):
return 1.0
len_a = abs(a[1] - a[0])
len_b = abs(b[1] - b[0])
return (abs(a[0] - b[0]) + abs(len_a - len_b)) / (max(a[0], b[0]) + max(len_a, len_b))
return (abs(a[0] - b[0]) + abs(len_a - len_b)) / \
(max(a[0], b[0]) + max(len_a, len_b))
def skip_connections_distance(list_a, list_b):
......@@ -161,7 +162,8 @@ class IncrementalGaussianProcess:
def incremental_fit(self, train_x, train_y):
""" Incrementally fit the regressor. """
if not self._first_fitted:
raise ValueError("The first_fit function needs to be called first.")
raise ValueError(
"The first_fit function needs to be called first.")
train_x, train_y = np.array(train_x), np.array(train_y)
......@@ -174,7 +176,7 @@ class IncrementalGaussianProcess:
temp_distance_matrix = np.concatenate((up_k, down_k), axis=0)
k_matrix = bourgain_embedding_matrix(temp_distance_matrix)
diagonal = np.diag_indices_from(k_matrix)
diagonal = (diagonal[0][-len(train_x) :], diagonal[1][-len(train_x) :])
diagonal = (diagonal[0][-len(train_x):], diagonal[1][-len(train_x):])
k_matrix[diagonal] += self.alpha
try:
......@@ -186,7 +188,8 @@ class IncrementalGaussianProcess:
self._y = np.concatenate((self._y, train_y), axis=0)
self._distance_matrix = temp_distance_matrix
self._alpha_vector = cho_solve((self._l_matrix, True), self._y) # Line 3
self._alpha_vector = cho_solve(
(self._l_matrix, True), self._y) # Line 3
return self
......@@ -209,7 +212,8 @@ class IncrementalGaussianProcess:
self._l_matrix = cholesky(k_matrix, lower=True) # Line 2
self._alpha_vector = cho_solve((self._l_matrix, True), self._y) # Line 3
self._alpha_vector = cho_solve(
(self._l_matrix, True), self._y) # Line 3
self._first_fitted = True
return self
......@@ -227,7 +231,9 @@ class IncrementalGaussianProcess:
# compute inverse K_inv of K based on its Cholesky
# decomposition L and its inverse L_inv
l_inv = solve_triangular(self._l_matrix.T, np.eye(self._l_matrix.shape[0]))
l_inv = solve_triangular(
self._l_matrix.T, np.eye(
self._l_matrix.shape[0]))
k_inv = l_inv.dot(l_inv.T)
# Compute variance of predictive distribution
y_var = np.ones(len(train_x), dtype=np.float)
......@@ -378,7 +384,11 @@ class BayesianOptimizer:
continue
temp_acq_value = self.acq(temp_graph)
pq.put(elem_class(temp_acq_value, elem.father_id, temp_graph))
pq.put(
elem_class(
temp_acq_value,
elem.father_id,
temp_graph))
descriptors.append(temp_graph.extract_descriptor())
if self._accept_new_acq_value(opt_acq, temp_acq_value):
opt_acq = temp_acq_value
......
src/sdk/pynni/nni/networkmorphism_tuner/graph.py
View file @ 0168ff1c
......@@ -249,7 +249,8 @@ class Graph:
self.reverse_adj_list[v_id].remove(edge_tuple)
break
self.reverse_adj_list[new_v_id].append((u_id, layer_id))
for index, value in enumerate(self.layer_id_to_output_node_ids[layer_id]):
for index, value in enumerate(
self.layer_id_to_output_node_ids[layer_id]):
if value == v_id:
self.layer_id_to_output_node_ids[layer_id][index] = new_v_id
break
......@@ -350,7 +351,8 @@ class Graph:
self._replace_layer(layer_id, new_layer)
elif is_layer(layer, "BatchNormalization"):
new_layer = wider_bn(layer, start_dim, total_dim, n_add, self.weighted)
new_layer = wider_bn(
layer, start_dim, total_dim, n_add, self.weighted)
self._replace_layer(layer_id, new_layer)
self._search(v, start_dim, total_dim, n_add)
......@@ -405,7 +407,8 @@ class Graph:
target_id: A convolutional layer ID. The new block should be inserted after the block.
new_layer: An instance of StubLayer subclasses.
"""
self.operation_history.append(("to_deeper_model", target_id, new_layer))
self.operation_history.append(
("to_deeper_model", target_id, new_layer))
input_id = self.layer_id_to_input_node_ids[target_id][0]
output_id = self.layer_id_to_output_node_ids[target_id][0]
if self.weighted:
......@@ -478,14 +481,20 @@ class Graph:
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
skip_output_id = self._insert_pooling_layer_chain(
start_node_id, end_node_id)
# Add the conv layer
new_conv_layer = get_conv_class(self.n_dim)(filters_start, filters_end, 1)
new_conv_layer = get_conv_class(
self.n_dim)(
filters_start,
filters_end,
1)
skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
# Add the add layer.
add_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
add_input_node_id = self._add_node(
deepcopy(self.node_list[end_node_id]))
add_layer = StubAdd()
self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
......@@ -504,7 +513,8 @@ class Graph:
weights = np.zeros((filters_end, filters_start) + filter_shape)
bias = np.zeros(filters_end)
new_conv_layer.set_weights(
(add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weights, np.array([0, 1])), add_noise(
bias, np.array([0, 1])))
)
def to_concat_skip_model(self, start_id, end_id):
......@@ -513,7 +523,8 @@ class Graph:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection.
"""
self.operation_history.append(("to_concat_skip_model", start_id, end_id))
self.operation_history.append(
("to_concat_skip_model", start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
......@@ -521,9 +532,11 @@ class Graph:
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
skip_output_id = self._insert_pooling_layer_chain(
start_node_id, end_node_id)
concat_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
concat_input_node_id = self._add_node(
deepcopy(self.node_list[end_node_id]))
self._redirect_edge(pre_end_node_id, end_node_id, concat_input_node_id)
concat_layer = StubConcatenate()
......@@ -532,7 +545,10 @@ class Graph:
self.node_list[skip_output_id],
]
concat_output_node_id = self._add_node(Node(concat_layer.output_shape))
self._add_edge(concat_layer, concat_input_node_id, concat_output_node_id)
self._add_edge(
concat_layer,
concat_input_node_id,
concat_output_node_id)
self._add_edge(concat_layer, skip_output_id, concat_output_node_id)
concat_layer.output = self.node_list[concat_output_node_id]
self.node_list[concat_output_node_id].shape = concat_layer.output_shape
......@@ -559,7 +575,8 @@ class Graph:
)
bias = np.zeros(filters_end)
new_conv_layer.set_weights(
(add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weights, np.array([0, 1])), add_noise(
bias, np.array([0, 1])))
)
def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
......@@ -568,7 +585,8 @@ class Graph:
new_layer = deepcopy(layer)
if is_layer(new_layer, "Conv"):
filters = self.node_list[start_node_id].shape[-1]
new_layer = get_conv_class(self.n_dim)(filters, filters, 1, layer.stride)
new_layer = get_conv_class(self.n_dim)(
filters, filters, 1, layer.stride)
if self.weighted:
init_conv_weight(new_layer)
else:
......@@ -601,8 +619,10 @@ class Graph:
temp_v = v
temp_layer_id = layer_id
skip_type = None
while not (temp_v in index_in_main_chain and temp_u in index_in_main_chain):
if is_layer(self.layer_list[temp_layer_id], "Concatenate"):
while not (
temp_v in index_in_main_chain and temp_u in index_in_main_chain):
if is_layer(
self.layer_list[temp_layer_id], "Concatenate"):
skip_type = NetworkDescriptor.CONCAT_CONNECT
if is_layer(self.layer_list[temp_layer_id], "Add"):
skip_type = NetworkDescriptor.ADD_CONNECT
......@@ -711,7 +731,8 @@ class Graph:
def wide_layer_ids(self):
return (
self._conv_layer_ids_in_order()[:-1] + self._dense_layer_ids_in_order()[:-1]
self._conv_layer_ids_in_order(
)[:-1] + self._dense_layer_ids_in_order()[:-1]
)
def skip_connection_layer_ids(self):
......@@ -810,7 +831,8 @@ class KerasModel:
topo_node_list = self.graph.topological_order
output_id = topo_node_list[-1]
input_id = topo_node_list[0]
input_tensor = keras.layers.Input(shape=graph.node_list[input_id].shape)
input_tensor = keras.layers.Input(
shape=graph.node_list[input_id].shape)
node_list = deepcopy(self.graph.node_list)
node_list[input_id] = input_tensor
......@@ -838,7 +860,8 @@ class KerasModel:
output_tensor = keras.layers.Activation("softmax", name="activation_add")(
output_tensor
)
self.model = keras.models.Model(inputs=input_tensor, outputs=output_tensor)
self.model = keras.models.Model(
inputs=input_tensor, outputs=output_tensor)
if graph.weighted:
for index, layer in enumerate(self.layers):
......@@ -892,7 +915,8 @@ class JSONModel:
for layer_id, item in enumerate(graph.layer_list):
layer = graph.layer_list[layer_id]
layer_information = layer_description_extractor(layer, graph.node_to_id)
layer_information = layer_description_extractor(
layer, graph.node_to_id)
layer_list.append((layer_id, layer_information))
data["node_list"] = node_list
......@@ -938,7 +962,8 @@ def json_to_graph(json_model: str):
graph.input_shape = input_shape
vis = json_model["vis"]
graph.vis = {tuple(item): True for item in vis} if vis is not None else None
graph.vis = {
tuple(item): True for item in vis} if vis is not None else None
graph.weighted = json_model["weighted"]
layer_id_to_input_node_ids = json_model["layer_id_to_input_node_ids"]
graph.layer_id_to_input_node_ids = {
......
src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py
View file @ 0168ff1c
......@@ -40,7 +40,8 @@ def to_wider_graph(graph):
'''
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
filter(
lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
)
wider_layers = sample(weighted_layer_ids, 1)
......@@ -58,12 +59,14 @@ def to_wider_graph(graph):
def to_skip_connection_graph(graph):
''' skip connection graph
'''
# The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
# The last conv layer cannot be widen since wider operator cannot be done
# over the two sides of flatten.
weighted_layer_ids = graph.skip_connection_layer_ids()
valid_connection = []
for skip_type in sorted([NetworkDescriptor.ADD_CONNECT, NetworkDescriptor.CONCAT_CONNECT]):
for skip_type in sorted(
[NetworkDescriptor.ADD_CONNECT, NetworkDescriptor.CONCAT_CONNECT]):
for index_a in range(len(weighted_layer_ids)):
for index_b in range(len(weighted_layer_ids))[index_a + 1 :]:
for index_b in range(len(weighted_layer_ids))[index_a + 1:]:
valid_connection.append((index_a, index_b, skip_type))
if not valid_connection:
......@@ -84,9 +87,14 @@ def create_new_layer(layer, n_dim):
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim), StubReLU]
conv_deeper_classes = [
get_conv_class(n_dim),
get_batch_norm_class(n_dim),
StubReLU]
if is_layer(layer, "ReLU"):
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim)]
conv_deeper_classes = [
get_conv_class(n_dim),
get_batch_norm_class(n_dim)]
dense_deeper_classes = [StubDense, get_dropout_class(n_dim)]
elif is_layer(layer, "Dropout"):
dense_deeper_classes = [StubDense, StubReLU]
......
src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py
View file @ 0168ff1c
......@@ -52,7 +52,8 @@ def deeper_conv_block(conv_layer, kernel_size, weighted=True):
if weighted:
new_conv_layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weight, np.array([0, 1])),
add_noise(bias, np.array([0, 1])))
)
new_weights = [
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
......@@ -74,7 +75,8 @@ def dense_to_deeper_block(dense_layer, weighted=True):
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weight, np.array([0, 1])),
add_noise(bias, np.array([0, 1])))
)
return [StubReLU(), new_dense_layer]
......@@ -97,8 +99,11 @@ def wider_pre_dense(layer, n_add, weighted=True):
teacher_index = rand[i]
new_weight = teacher_w[teacher_index, :]
new_weight = new_weight[np.newaxis, :]
student_w = np.concatenate((student_w, add_noise(new_weight, student_w)), axis=0)
student_b = np.append(student_b, add_noise(teacher_b[teacher_index], student_b))
student_w = np.concatenate(
(student_w, add_noise(new_weight, student_w)), axis=0)
student_b = np.append(
student_b, add_noise(
teacher_b[teacher_index], student_b))
new_pre_layer = StubDense(layer.input_units, n_units2 + n_add)
new_pre_layer.set_weights((student_w, student_b))
......@@ -209,7 +214,7 @@ def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True):
student_w[:, : start_dim * n_units_each_channel],
add_noise(new_weight, student_w),
student_w[
:, start_dim * n_units_each_channel : total_dim * n_units_each_channel
:, start_dim * n_units_each_channel: total_dim * n_units_each_channel
],
),
axis=1,
......@@ -225,7 +230,8 @@ def add_noise(weights, other_weights):
'''
w_range = np.ptp(other_weights.flatten())
noise_range = NOISE_RATIO * w_range
noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape)
noise = np.random.uniform(-noise_range / 2.0,
noise_range / 2.0, weights.shape)
return np.add(noise, weights)
......@@ -236,7 +242,8 @@ def init_dense_weight(layer):
weight = np.eye(units)
bias = np.zeros(units)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weight, np.array([0, 1])),
add_noise(bias, np.array([0, 1])))
)
......@@ -256,7 +263,8 @@ def init_conv_weight(layer):
bias = np.zeros(n_filters)
layer.set_weights(
(add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
(add_noise(weight, np.array([0, 1])),
add_noise(bias, np.array([0, 1])))
)
......
src/sdk/pynni/nni/networkmorphism_tuner/layers.py
View file @ 0168ff1c
......@@ -28,8 +28,10 @@ from nni.networkmorphism_tuner.utils import Constant
class AvgPool(nn.Module):
'''AvgPool Module.
'''
"""
AvgPool Module.
"""
def __init__(self):
super().__init__()
......@@ -39,8 +41,10 @@ class AvgPool(nn.Module):
class GlobalAvgPool1d(AvgPool):
'''GlobalAvgPool1d Module.
'''
"""
GlobalAvgPool1d Module.
"""
def forward(self, input_tensor):
return functional.avg_pool1d(input_tensor, input_tensor.size()[2:]).view(
input_tensor.size()[:2]
......@@ -48,8 +52,10 @@ class GlobalAvgPool1d(AvgPool):
class GlobalAvgPool2d(AvgPool):
'''GlobalAvgPool2d Module.
'''
"""
GlobalAvgPool2d Module.
"""
def forward(self, input_tensor):
return functional.avg_pool2d(input_tensor, input_tensor.size()[2:]).view(
input_tensor.size()[:2]
......@@ -57,8 +63,10 @@ class GlobalAvgPool2d(AvgPool):
class GlobalAvgPool3d(AvgPool):
'''GlobalAvgPool3d Module.
'''
"""
GlobalAvgPool3d Module.
"""
def forward(self, input_tensor):
return functional.avg_pool3d(input_tensor, input_tensor.size()[2:]).view(
input_tensor.size()[:2]
......@@ -66,70 +74,86 @@ class GlobalAvgPool3d(AvgPool):
class StubLayer:
'''StubLayer Module. Base Module.
'''
"""
StubLayer Module. Base Module.
"""
def __init__(self, input_node=None, output_node=None):
self.input = input_node
self.output = output_node
self.weights = None
def build(self, shape):
'''build shape.
'''
"""
build shape.
"""
def set_weights(self, weights):
'''set weights.
'''
"""
set weights.
"""
self.weights = weights
def import_weights(self, torch_layer):
'''import weights.
'''
"""
import weights.
"""
def import_weights_keras(self, keras_layer):
'''import weights from keras layer.
'''
"""
import weights from keras layer.
"""
def export_weights(self, torch_layer):
'''export weights.
'''
"""
export weights.
"""
def export_weights_keras(self, keras_layer):
'''export weights to keras layer.
'''
"""
export weights to keras layer.
"""
def get_weights(self):
'''get weights.
'''
"""
get weights.
"""
return self.weights
def size(self):
'''size().
'''
"""
size().
"""
return 0
@property
def output_shape(self):
'''output shape.
'''
"""
output shape.
"""
return self.input.shape
def to_real_layer(self):
'''to real layer.
'''
"""
to real layer.
"""
def __str__(self):
'''str() function to print.
'''
"""
str() function to print.
"""
return type(self).__name__[4:]
class StubWeightBiasLayer(StubLayer):
'''StubWeightBiasLayer Module to set the bias.
'''
"""
StubWeightBiasLayer Module to set the bias.
"""
def import_weights(self, torch_layer):
self.set_weights(
(torch_layer.weight.data.cpu().numpy(), torch_layer.bias.data.cpu().numpy())
(torch_layer.weight.data.cpu().numpy(),
torch_layer.bias.data.cpu().numpy())
)
def import_weights_keras(self, keras_layer):
......@@ -144,8 +168,10 @@ class StubWeightBiasLayer(StubLayer):
class StubBatchNormalization(StubWeightBiasLayer):
'''StubBatchNormalization Module. Batch Norm.
'''
"""
StubBatchNormalization Module. Batch Norm.
"""
def __init__(self, num_features, input_node=None, output_node=None):
super().__init__(input_node, output_node)
self.num_features = num_features
......@@ -175,29 +201,37 @@ class StubBatchNormalization(StubWeightBiasLayer):
class StubBatchNormalization1d(StubBatchNormalization):
'''StubBatchNormalization1d Module.
'''
"""
StubBatchNormalization1d Module.
"""
def to_real_layer(self):
return torch.nn.BatchNorm1d(self.num_features)
class StubBatchNormalization2d(StubBatchNormalization):
'''StubBatchNormalization2d Module.
'''
"""
StubBatchNormalization2d Module.
"""
def to_real_layer(self):
return torch.nn.BatchNorm2d(self.num_features)
class StubBatchNormalization3d(StubBatchNormalization):
'''StubBatchNormalization3d Module.
'''
"""
StubBatchNormalization3d Module.
"""
def to_real_layer(self):
return torch.nn.BatchNorm3d(self.num_features)
class StubDense(StubWeightBiasLayer):
'''StubDense Module. Linear.
'''
"""
StubDense Module. Linear.
"""
def __init__(self, input_units, units, input_node=None, output_node=None):
super().__init__(input_node, output_node)
self.input_units = input_units
......@@ -208,7 +242,9 @@ class StubDense(StubWeightBiasLayer):
return (self.units,)
def import_weights_keras(self, keras_layer):
self.set_weights((keras_layer.get_weights()[0].T, keras_layer.get_weights()[1]))
self.set_weights(
(keras_layer.get_weights()[0].T,
keras_layer.get_weights()[1]))
def export_weights_keras(self, keras_layer):
keras_layer.set_weights((self.weights[0].T, self.weights[1]))
......@@ -221,9 +257,12 @@ class StubDense(StubWeightBiasLayer):
class StubConv(StubWeightBiasLayer):
'''StubConv Module. Conv.
'''
def __init__(self, input_channel, filters, kernel_size, stride=1, input_node=None, output_node=None):
"""
StubConv Module. Conv.
"""
def __init__(self, input_channel, filters, kernel_size,
stride=1, input_node=None, output_node=None):
super().__init__(input_node, output_node)
self.input_channel = input_channel
self.filters = filters
......@@ -242,13 +281,16 @@ class StubConv(StubWeightBiasLayer):
return tuple(ret)
def import_weights_keras(self, keras_layer):
self.set_weights((keras_layer.get_weights()[0].T, keras_layer.get_weights()[1]))
self.set_weights(
(keras_layer.get_weights()[0].T,
keras_layer.get_weights()[1]))
def export_weights_keras(self, keras_layer):
keras_layer.set_weights((self.weights[0].T, self.weights[1]))
def size(self):
return (self.input_channel * self.kernel_size * self.kernel_size + 1) * self.filters
return (self.input_channel * self.kernel_size *
self.kernel_size + 1) * self.filters
@abstractmethod
def to_real_layer(self):
......@@ -272,8 +314,10 @@ class StubConv(StubWeightBiasLayer):
class StubConv1d(StubConv):
'''StubConv1d Module.
'''
"""
StubConv1d Module.
"""
def to_real_layer(self):
return torch.nn.Conv1d(
self.input_channel,
......@@ -285,8 +329,10 @@ class StubConv1d(StubConv):
class StubConv2d(StubConv):
'''StubConv2d Module.
'''
"""
StubConv2d Module.
"""
def to_real_layer(self):
return torch.nn.Conv2d(
self.input_channel,
......@@ -298,8 +344,10 @@ class StubConv2d(StubConv):
class StubConv3d(StubConv):
'''StubConv3d Module.
'''
"""
StubConv3d Module.
"""
def to_real_layer(self):
return torch.nn.Conv3d(
self.input_channel,
......@@ -311,8 +359,10 @@ class StubConv3d(StubConv):
class StubAggregateLayer(StubLayer):
'''StubAggregateLayer Module.
'''
"""
StubAggregateLayer Module.
"""
def __init__(self, input_nodes=None, output_node=None):
if input_nodes is None:
input_nodes = []
......@@ -320,8 +370,8 @@ class StubAggregateLayer(StubLayer):
class StubConcatenate(StubAggregateLayer):
'''StubConcatenate Module.
'''
"""StubConcatenate Module.
"""
@property
def output_shape(self):
ret = 0
......@@ -335,8 +385,9 @@ class StubConcatenate(StubAggregateLayer):
class StubAdd(StubAggregateLayer):
'''StubAdd Module.
'''
"""
StubAdd Module.
"""
@property
def output_shape(self):
return self.input[0].shape
......@@ -346,8 +397,9 @@ class StubAdd(StubAggregateLayer):
class StubFlatten(StubLayer):
'''StubFlatten Module.
'''
"""
StubFlatten Module.
"""
@property
def output_shape(self):
ret = 1
......@@ -360,22 +412,28 @@ class StubFlatten(StubLayer):
class StubReLU(StubLayer):
'''StubReLU Module.
'''
"""
StubReLU Module.
"""
def to_real_layer(self):
return torch.nn.ReLU()
class StubSoftmax(StubLayer):
'''StubSoftmax Module.
'''
"""
StubSoftmax Module.
"""
def to_real_layer(self):
return torch.nn.LogSoftmax(dim=1)
class StubDropout(StubLayer):
'''StubDropout Module.
'''
"""
StubDropout Module.
"""
def __init__(self, rate, input_node=None, output_node=None):
super().__init__(input_node, output_node)
self.rate = rate
......@@ -386,36 +444,45 @@ class StubDropout(StubLayer):
class StubDropout1d(StubDropout):
'''StubDropout1d Module.
'''
"""
StubDropout1d Module.
"""
def to_real_layer(self):
return torch.nn.Dropout(self.rate)
class StubDropout2d(StubDropout):
'''StubDropout2d Module.
'''
"""
StubDropout2d Module.
"""
def to_real_layer(self):
return torch.nn.Dropout2d(self.rate)
class StubDropout3d(StubDropout):
'''StubDropout3d Module.
'''
"""
StubDropout3d Module.
"""
def to_real_layer(self):
return torch.nn.Dropout3d(self.rate)
class StubInput(StubLayer):
'''StubInput Module.
'''
"""
StubInput Module.
"""
def __init__(self, input_node=None, output_node=None):
super().__init__(input_node, output_node)
class StubPooling(StubLayer):
'''StubPooling Module.
'''
"""
StubPooling Module.
"""
def __init__(self,
kernel_size=None,
......@@ -444,30 +511,37 @@ class StubPooling(StubLayer):
class StubPooling1d(StubPooling):
'''StubPooling1d Module.
'''
"""
StubPooling1d Module.
"""
def to_real_layer(self):
return torch.nn.MaxPool1d(self.kernel_size, stride=self.stride)
class StubPooling2d(StubPooling):
'''StubPooling2d Module.
'''
"""
StubPooling2d Module.
"""
def to_real_layer(self):
return torch.nn.MaxPool2d(self.kernel_size, stride=self.stride)
class StubPooling3d(StubPooling):
'''StubPooling3d Module.
'''
"""
StubPooling3d Module.
"""
def to_real_layer(self):
return torch.nn.MaxPool3d(self.kernel_size, stride=self.stride)
class StubGlobalPooling(StubLayer):
'''StubGlobalPooling Module.
'''
"""
StubGlobalPooling Module.
"""
def __init__(self, input_node=None, output_node=None):
super().__init__(input_node, output_node)
......@@ -481,49 +555,63 @@ class StubGlobalPooling(StubLayer):
class StubGlobalPooling1d(StubGlobalPooling):
'''StubGlobalPooling1d Module.
'''
"""
StubGlobalPooling1d Module.
"""
def to_real_layer(self):
return GlobalAvgPool1d()
class StubGlobalPooling2d(StubGlobalPooling):
'''StubGlobalPooling2d Module.
'''
"""
StubGlobalPooling2d Module.
"""
def to_real_layer(self):
return GlobalAvgPool2d()
class StubGlobalPooling3d(StubGlobalPooling):
'''StubGlobalPooling3d Module.
'''
"""
StubGlobalPooling3d Module.
"""
def to_real_layer(self):
return GlobalAvgPool3d()
class TorchConcatenate(nn.Module):
'''TorchConcatenate Module.
'''
"""
TorchConcatenate Module.
"""
def forward(self, input_list):
return torch.cat(input_list, dim=1)
class TorchAdd(nn.Module):
'''TorchAdd Module.
'''
"""
TorchAdd Module.
"""
def forward(self, input_list):
return input_list[0] + input_list[1]
class TorchFlatten(nn.Module):
'''TorchFlatten Module.
'''
"""
TorchFlatten Module.
"""
def forward(self, input_tensor):
return input_tensor.view(input_tensor.size(0), -1)
def keras_dropout(layer, rate):
'''keras dropout layer.
'''
"""
Keras dropout layer.
"""
from keras import layers
......@@ -539,8 +627,9 @@ def keras_dropout(layer, rate):
def to_real_keras_layer(layer):
''' real keras layer.
'''
"""
Real keras layer.
"""
from keras import layers
if is_layer(layer, "Dense"):
......@@ -574,10 +663,14 @@ def to_real_keras_layer(layer):
def is_layer(layer, layer_type):
'''judge the layer type.
Returns:
"""
Judge the layer type.
Returns
-------
bool
boolean -- True or False
'''
"""
if layer_type == "Input":
return isinstance(layer, StubInput)
......@@ -607,8 +700,9 @@ def is_layer(layer, layer_type):
def layer_description_extractor(layer, node_to_id):
'''get layer description.
'''
"""
Get layer description.
"""
layer_input = layer.input
layer_output = layer.output
......@@ -641,7 +735,8 @@ def layer_description_extractor(layer, node_to_id):
layer.units,
]
elif isinstance(layer, (StubBatchNormalization,)):
return (type(layer).__name__, layer_input, layer_output, layer.num_features)
return (type(layer).__name__, layer_input,
layer_output, layer.num_features)
elif isinstance(layer, (StubDropout,)):
return (type(layer).__name__, layer_input, layer_output, layer.rate)
elif isinstance(layer, StubPooling):
......@@ -658,8 +753,8 @@ def layer_description_extractor(layer, node_to_id):
def layer_description_builder(layer_information, id_to_node):
'''build layer from description.
'''
"""build layer from description.
"""
layer_type = layer_information[0]
layer_input_ids = layer_information[1]
......@@ -696,8 +791,9 @@ def layer_description_builder(layer_information, id_to_node):
def layer_width(layer):
'''get layer width.
'''
"""
Get layer width.
"""
if is_layer(layer, "Dense"):
return layer.units
......
src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py
View file @ 0168ff1c
......@@ -17,11 +17,13 @@
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ==================================================================================================
"""
networkmorphsim_tuner.py
"""
import logging
import os
from nni.tuner import Tuner
from nni.utils import OptimizeMode, extract_scalar_reward
from nni.networkmorphism_tuner.bayesian import BayesianOptimizer
......@@ -34,7 +36,35 @@ logger = logging.getLogger("NetworkMorphism_AutoML")
class NetworkMorphismTuner(Tuner):
"""NetworkMorphismTuner is a tuner which using network morphism techniques."""
"""
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,
......@@ -52,36 +82,8 @@ class NetworkMorphismTuner(Tuner):
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})
"""
initilizer of the NetworkMorphismTuner.
"""
if not os.path.exists(path):
......@@ -92,7 +94,8 @@ class NetworkMorphismTuner(Tuner):
elif task == "common":
self.generators = [MlpGenerator]
else:
raise NotImplementedError('{} task not supported in List ["cv","common"]')
raise NotImplementedError(
'{} task not supported in List ["cv","common"]')
self.n_classes = n_output_node
self.input_shape = (input_width, input_width, input_channel)
......@@ -106,7 +109,8 @@ class NetworkMorphismTuner(Tuner):
self.verbose = verbose
self.model_count = 0
self.bo = BayesianOptimizer(self, self.t_min, self.optimize_mode, self.beta)
self.bo = BayesianOptimizer(
self, self.t_min, self.optimize_mode, self.beta)
self.training_queue = []
self.descriptors = []
self.history = []
......@@ -117,6 +121,7 @@ class NetworkMorphismTuner(Tuner):
self.search_space = dict()
def update_search_space(self, search_space):
"""
Update search space definition in tuner by search_space in neural architecture.
......@@ -140,7 +145,8 @@ class NetworkMorphismTuner(Tuner):
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.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)
......@@ -153,12 +159,15 @@ class NetworkMorphismTuner(Tuner):
return json_out
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
""" Record an observation of the objective function.
"""
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.
"""
......@@ -175,8 +184,11 @@ class NetworkMorphismTuner(Tuner):
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."""
"""
Call the generators to generate the initial architectures for the search.
"""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
......@@ -191,14 +203,16 @@ class NetworkMorphismTuner(Tuner):
if self.verbose:
logger.info("Initialization finished.")
def generate(self):
"""Generate the next neural architecture.
"""
Generate the next neural architecture.
Returns
-------
other_info: any object
other_info : any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
generated_graph : Graph
An instance of Graph.
"""
generated_graph, new_father_id = self.bo.generate(self.descriptors)
......@@ -211,7 +225,8 @@ class NetworkMorphismTuner(Tuner):
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.
"""
Update the controller with evaluation result of a neural architecture.
Parameters
----------
......@@ -228,7 +243,8 @@ class NetworkMorphismTuner(Tuner):
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
"""
Add model to the history, x_queue and y_queue
Parameters
----------
......@@ -252,16 +268,21 @@ class NetworkMorphismTuner(Tuner):
file.close()
return ret
def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
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 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
"""
Get the model by model_id
Parameters
----------
......@@ -281,7 +302,8 @@ class NetworkMorphismTuner(Tuner):
return load_model
def load_best_model(self):
""" Get the best model by model id
"""
Get the best model by model id
Returns
-------
......@@ -291,7 +313,8 @@ class NetworkMorphismTuner(Tuner):
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
"""
Get the model metric valud by its model_id
Parameters
----------
......
src/sdk/pynni/nni/networkmorphism_tuner/nn.py
View file @ 0168ff1c
......@@ -92,17 +92,25 @@ class CnnGenerator(NetworkGenerator):
for i in range(model_len):
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id
self.batch_norm(
graph.node_list[output_node_id].shape[-1]), output_node_id
)
output_node_id = graph.add_layer(
self.conv(temp_input_channel, model_width, kernel_size=3, stride=stride),
self.conv(
temp_input_channel,
model_width,
kernel_size=3,
stride=stride),
output_node_id,
)
temp_input_channel = model_width
if pooling_len == 0 or ((i + 1) % pooling_len == 0 and i != model_len - 1):
output_node_id = graph.add_layer(self.pooling(), output_node_id)
if pooling_len == 0 or (
(i + 1) % pooling_len == 0 and i != model_len - 1):
output_node_id = graph.add_layer(
self.pooling(), output_node_id)
output_node_id = graph.add_layer(self.global_avg_pooling(), output_node_id)
output_node_id = graph.add_layer(
self.global_avg_pooling(), output_node_id)
output_node_id = graph.add_layer(
self.dropout(Constant.CONV_DROPOUT_RATE), output_node_id
)
......@@ -111,7 +119,11 @@ class CnnGenerator(NetworkGenerator):
output_node_id,
)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
graph.add_layer(StubDense(model_width, self.n_output_node), output_node_id)
graph.add_layer(
StubDense(
model_width,
self.n_output_node),
output_node_id)
return graph
......@@ -145,7 +157,8 @@ class MlpGenerator(NetworkGenerator):
if model_width is None:
model_width = Constant.MODEL_WIDTH
if isinstance(model_width, list) and not len(model_width) == model_len:
raise ValueError("The length of 'model_width' does not match 'model_len'")
raise ValueError(
"The length of 'model_width' does not match 'model_len'")
elif isinstance(model_width, int):
model_width = [model_width] * model_len
......@@ -162,5 +175,9 @@ class MlpGenerator(NetworkGenerator):
output_node_id = graph.add_layer(StubReLU(), output_node_id)
n_nodes_prev_layer = width
graph.add_layer(StubDense(n_nodes_prev_layer, self.n_output_node), output_node_id)
graph.add_layer(
StubDense(
n_nodes_prev_layer,
self.n_output_node),
output_node_id)
return graph
src/sdk/pynni/nni/networkmorphism_tuner/test_networkmorphism_tuner.py
View file @ 0168ff1c
......@@ -59,9 +59,12 @@ class NetworkMorphismTestCase(TestCase):
graph_recover.layer_id_to_input_node_ids,
)
self.assertEqual(graph_init.adj_list, graph_recover.adj_list)
self.assertEqual(graph_init.reverse_adj_list, graph_recover.reverse_adj_list)
self.assertEqual(
len(graph_init.operation_history), len(graph_recover.operation_history)
graph_init.reverse_adj_list,
graph_recover.reverse_adj_list)
self.assertEqual(
len(graph_init.operation_history), len(
graph_recover.operation_history)
)
self.assertEqual(graph_init.n_dim, graph_recover.n_dim)
self.assertEqual(graph_init.conv, graph_recover.conv)
......@@ -71,7 +74,8 @@ class NetworkMorphismTestCase(TestCase):
node_list_init = [node.shape for node in graph_init.node_list]
node_list_recover = [node.shape for node in graph_recover.node_list]
self.assertEqual(node_list_init, node_list_recover)
self.assertEqual(len(graph_init.node_to_id), len(graph_recover.node_to_id))
self.assertEqual(len(graph_init.node_to_id),
len(graph_recover.node_to_id))
layer_list_init = [
layer_description_extractor(item, graph_init.node_to_id)
for item in graph_init.layer_list
......@@ -82,7 +86,8 @@ class NetworkMorphismTestCase(TestCase):
]
self.assertEqual(layer_list_init, layer_list_recover)
node_to_id_init = [graph_init.node_to_id[node] for node in graph_init.node_list]
node_to_id_init = [graph_init.node_to_id[node]
for node in graph_init.node_list]
node_to_id_recover = [
graph_recover.node_to_id[node] for node in graph_recover.node_list
]
......
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