def _get_fextractor_instance(self, fextractor_type):
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
fextractor = fextractor_class(assets=self.assets,
logger=self.logger,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
result_store=self.result_store,
optional_dict=self.optional_dict)
return fextractor
def _get_atom_features(self, fextractor_type):
if self.feature_dict[fextractor_type] == 'all':
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
atom_features = fextractor_class.ATOM_FEATURES + \
(fextractor_class.DERIVED_ATOM_FEATURES
if hasattr(fextractor_class, 'DERIVED_ATOM_FEATURES')
else [])
else:
atom_features = self.feature_dict[fextractor_type]
return atom_features
def _get_atom_features(self, fextractor_type):
if self.feature_dict[fextractor_type] == 'all':
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
atom_features = fextractor_class.ATOM_FEATURES + \
(fextractor_class.DERIVED_ATOM_FEATURES
if hasattr(fextractor_class, 'DERIVED_ATOM_FEATURES')
else [])
else:
atom_features = self.feature_dict[fextractor_type]
return atom_features
def _get_fextractor_instance(self, fextractor_type):
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
fextractor = fextractor_class(assets=self.assets,
logger=self.logger,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
result_store=self.result_store,
optional_dict=self.optional_dict
)
return fextractor
def run(self):
"""
Do all the calculation here.
:return:
"""
# for each FeatureExtractor_type key in feature_dict, find the subclass
# of FeatureExtractor, run, and put results in a dict
for fextractor_type in self.feature_dict:
# fextractor = self._get_fextractor_instance(fextractor_type)
# fextractor.run()
# results = fextractor.results
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
_, results = run_executors_in_parallel(
fextractor_class,
assets=self.assets,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
parallelize=self.parallelize,
result_store=self.result_store,
optional_dict=self.optional_dict,
optional_dict2=self.optional_dict2,
)
self.type2results_dict[fextractor_type] = results
# assemble an output dict with demanded atom features
# atom_features_dict = self.fextractor_atom_features_dict
result_dicts = [dict() for _ in self.assets]
for fextractor_type in self.feature_dict:
assert fextractor_type in self.type2results_dict
for atom_feature in self._get_atom_features(fextractor_type):
scores_key = self._get_scores_key(fextractor_type, atom_feature)
for result_index, result in enumerate(self.type2results_dict[
fextractor_type]):
result_dicts[result_index][scores_key] = result[scores_key]
self.results = map(
lambda (asset, result_dict): BasicResult(asset, result_dict),
zip(self.assets, result_dicts)
)
def run(self):
"""
Do all the calculation here.
:return:
"""
# for each FeatureExtractor_type key in feature_dict, find the subclass
# of FeatureExtractor, run, and put results in a dict
for fextractor_type in self.feature_dict:
# fextractor = self._get_fextractor_instance(fextractor_type)
# fextractor.run()
# results = fextractor.results
fextractor_class = FeatureExtractor.find_subclass(fextractor_type)
_, results = run_executors_in_parallel(
fextractor_class,
assets=self.assets,
fifo_mode=self.fifo_mode,
delete_workdir=self.delete_workdir,
parallelize=self.parallelize,
result_store=self.result_store,
optional_dict=self.optional_dict,
optional_dict2=self.optional_dict2,
)
self.type2results_dict[fextractor_type] = results
# assemble an output dict with demanded atom features
# atom_features_dict = self.fextractor_atom_features_dict
result_dicts = [dict() for _ in self.assets]
for fextractor_type in self.feature_dict:
assert fextractor_type in self.type2results_dict
for atom_feature in self._get_atom_features(fextractor_type):
scores_key = self._get_scores_key(fextractor_type,
atom_feature)
for result_index, result in enumerate(
self.type2results_dict[fextractor_type]):
result_dicts[result_index][scores_key] = result[scores_key]
self.results = map(
lambda (asset, result_dict): BasicResult(asset, result_dict),
zip(self.assets, result_dicts))
def _get_scores_key(self, fextractor_type, atom_feature):
fextractor_subclass = FeatureExtractor.find_subclass(fextractor_type)
scores_key = fextractor_subclass.get_scores_key(atom_feature)
return scores_key
def _get_scores_key(self, fextractor_type, atom_feature):
fextractor_subclass = FeatureExtractor.find_subclass(fextractor_type)
scores_key = fextractor_subclass.get_scores_key(atom_feature)
return scores_key
