def get_default_config(cls):
d = super(IqrSearch, cls).get_default_config()
# Remove parent_app slot for later explicit specification.
del d['parent_app']
# fill in plugin configs
d['data_set'] = plugin.make_config(get_data_set_impls())
d['descr_generator'] = \
plugin.make_config(get_descriptor_generator_impls())
d['nn_index'] = plugin.make_config(get_nn_index_impls())
ri_config = plugin.make_config(get_relevancy_index_impls())
if d['rel_index_config']:
ri_config.update(d['rel_index_config'])
d['rel_index_config'] = ri_config
df_config = DescriptorElementFactory.get_default_config()
if d['descriptor_factory']:
df_config.update(d['descriptor_factory'].get_config())
d['descriptor_factory'] = df_config
return d
def from_config(cls, config, parent_app):
"""
Instantiate a new instance of this class given the configuration
JSON-compliant dictionary encapsulating initialization arguments.
:param config: JSON compliant dictionary encapsulating
a configuration.
:type config: dict
:param parent_app: Parent containing flask app instance
:type parent_app: smqtk.web.search_app.app.search_app
:return: Constructed instance from the provided config.
:rtype: IqrSearch
"""
merged = cls.get_default_config()
merged.update(config)
# construct nested objects via configurations
merged['data_set'] = \
plugin.from_plugin_config(merged['data_set'],
get_data_set_impls())
merged['descr_generator'] = \
plugin.from_plugin_config(merged['descr_generator'],
get_descriptor_generator_impls())
merged['nn_index'] = \
plugin.from_plugin_config(merged['nn_index'],
get_nn_index_impls())
merged['descriptor_factory'] = \
DescriptorElementFactory.from_config(merged['descriptor_factory'])
return cls(parent_app, **merged)
def from_config(cls, config, parent_app):
"""
Instantiate a new instance of this class given the configuration
JSON-compliant dictionary encapsulating initialization arguments.
:param config: JSON compliant dictionary encapsulating
a configuration.
:type config: dict
:param parent_app: Parent containing flask app instance
:type parent_app: smqtk.web.search_app.app.search_app
:return: Constructed instance from the provided config.
:rtype: IqrSearch
"""
merged = cls.get_default_config()
merged.update(config)
# construct nested objects via configurations
merged['data_set'] = \
plugin.from_plugin_config(merged['data_set'],
get_data_set_impls())
return cls(parent_app, **merged)
def get_default_config(cls):
d = super(IqrSearch, cls).get_default_config()
# Remove parent_app slot for later explicit specification.
del d['parent_app']
# fill in plugin configs
d['data_set'] = plugin.make_config(get_data_set_impls())
d['descr_generator'] = \
plugin.make_config(get_descriptor_generator_impls())
d['nn_index'] = plugin.make_config(get_nn_index_impls())
ri_config = plugin.make_config(get_relevancy_index_impls())
if d['rel_index_config']:
ri_config.update(d['rel_index_config'])
d['rel_index_config'] = ri_config
df_config = DescriptorElementFactory.get_default_config()
if d['descriptor_factory']:
df_config.update(d['descriptor_factory'].get_config())
d['descriptor_factory'] = df_config
return d
def default_config():
return {
"descriptor_generator":
plugin.make_config(get_descriptor_generator_impls()),
"descriptor_factory":
DescriptorElementFactory.get_default_config(),
"descriptor_index":
plugin.make_config(get_descriptor_index_impls()),
"optional_data_set":
plugin.make_config(get_data_set_impls())
}
def default_config():
return {
'tool': {
'girder_api_root': 'http://localhost:8080/api/v1',
'api_key': None,
'api_query_batch': 1000,
'dataset_insert_batch_size': None,
},
'plugins': {
'data_set': plugin.make_config(get_data_set_impls()),
}
}
def get_default_config(cls):
d = super(IqrSearch, cls).get_default_config()
# Remove parent_app slot for later explicit specification.
del d['parent_app']
d['iqr_service_url'] = None
# fill in plugin configs
d['data_set'] = plugin.make_config(get_data_set_impls())
return d
def get_default_config(cls):
d = super(IqrSearch, cls).get_default_config()
# Remove parent_app slot for later explicit specification.
del d['parent_app']
d['iqr_service_url'] = None
# fill in plugin configs
d['data_set'] = plugin.make_config(get_data_set_impls())
return d
def default_config():
return {
'tool': {
'girder_api_root': 'http://localhost:8080/api/v1',
'api_key': None,
'api_query_batch': 1000,
'dataset_insert_batch_size': None,
},
'plugins': {
'data_set': plugin.make_config(get_data_set_impls()),
}
}
def main():
args = cli_parser().parse_args()
config = bin_utils.utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
api_root = config['tool']['girder_api_root']
api_key = config['tool']['api_key']
api_query_batch = config['tool']['api_query_batch']
insert_batch_size = config['tool']['dataset_insert_batch_size']
# Collect N folder/item/file references on CL and any files referenced.
#: :type: list[str]
ids_folder = args.folder
#: :type: list[str]
ids_item = args.item
#: :type: list[str]
ids_file = args.file
if args.folder_list:
with open(args.folder_list) as f:
ids_folder.extend([fid.strip() for fid in f])
if args.item_list:
with open(args.item_list) as f:
ids_item.extend([iid.strip() for iid in f])
if args.file_list:
with open(args.file_list) as f:
ids_file.extend([fid.strip() for fid in f])
#: :type: smqtk.representation.DataSet
data_set = plugin.from_plugin_config(config['plugins']['data_set'],
get_data_set_impls())
batch = collections.deque()
rps = [0]*7
for e in find_girder_files(api_root, ids_folder, ids_item, ids_file,
api_key, api_query_batch):
batch.append(e)
if insert_batch_size and len(batch) >= insert_batch_size:
data_set.add_data(*batch)
batch.clear()
bin_utils.report_progress(log.info, rps, 1.0)
if batch:
data_set.add_data(*batch)
log.info('Done')
def main():
args = cli_parser().parse_args()
config = bin_utils.utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
api_root = config['tool']['girder_api_root']
api_key = config['tool']['api_key']
api_query_batch = config['tool']['api_query_batch']
insert_batch_size = config['tool']['dataset_insert_batch_size']
# Collect N folder/item/file references on CL and any files referenced.
#: :type: list[str]
ids_folder = args.folder
#: :type: list[str]
ids_item = args.item
#: :type: list[str]
ids_file = args.file
if args.folder_list:
with open(args.folder_list) as f:
ids_folder.extend([fid.strip() for fid in f])
if args.item_list:
with open(args.item_list) as f:
ids_item.extend([iid.strip() for iid in f])
if args.file_list:
with open(args.file_list) as f:
ids_file.extend([fid.strip() for fid in f])
#: :type: smqtk.representation.DataSet
data_set = plugin.from_plugin_config(config['plugins']['data_set'],
get_data_set_impls())
batch = collections.deque()
rps = [0] * 7
for e in find_girder_files(api_root, ids_folder, ids_item, ids_file,
api_key, api_query_batch):
batch.append(e)
if insert_batch_size and len(batch) >= insert_batch_size:
data_set.add_data(*batch)
batch.clear()
bin_utils.report_progress(log.info, rps, 1.0)
if batch:
data_set.add_data(*batch)
log.info('Done')
def main():
parser = cli_parser()
args = parser.parse_args()
bin_utils.initialize_logging(logging.getLogger(),
logging.INFO - (10 * args.verbose))
log = logging.getLogger("main")
# Merge loaded config with default
config_loaded = False
config = default_config()
if args.config:
if osp.isfile(args.config):
with open(args.config, 'r') as f:
config.update(json.load(f))
config_loaded = True
elif not osp.isfile(args.config):
log.error("Configuration file path not valid.")
exit(1)
# output configuration dictionary when asked for.
bin_utils.output_config(args.output_config, config, log, True)
if not config_loaded:
log.error("No configuration provided")
exit(1)
log.debug("Script arguments:\n%s" % args)
def iter_input_elements():
for f in args.input_files:
f = osp.expanduser(f)
if osp.isfile(f):
yield DataFileElement(f)
else:
log.debug("Expanding glob: %s" % f)
for g in glob.glob(f):
yield DataFileElement(g)
log.info("Adding elements to data set")
#: :type: smqtk.representation.DataSet
ds = plugin.from_plugin_config(config['data_set'], get_data_set_impls())
ds.add_data(*iter_input_elements())
def main():
parser = cli_parser()
args = parser.parse_args()
bin_utils.initialize_logging(logging.getLogger(),
logging.INFO - (10 * args.verbose))
log = logging.getLogger("main")
# Merge loaded config with default
config_loaded = False
config = default_config()
if args.config:
if osp.isfile(args.config):
with open(args.config, 'r') as f:
config.update(json.load(f))
config_loaded = True
elif not osp.isfile(args.config):
log.error("Configuration file path not valid.")
exit(1)
# output configuration dictionary when asked for.
bin_utils.output_config(args.output_config, config, log, True)
if not config_loaded:
log.error("No configuration provided")
exit(1)
log.debug("Script arguments:\n%s" % args)
def iter_input_elements():
for f in args.input_files:
f = osp.expanduser(f)
if osp.isfile(f):
yield DataFileElement(f)
else:
log.debug("Expanding glob: %s" % f)
for g in glob.glob(f):
yield DataFileElement(g)
log.info("Adding elements to data set")
#: :type: smqtk.representation.DataSet
ds = plugin.from_plugin_config(config['data_set'], get_data_set_impls())
ds.add_data(*iter_input_elements())
def main():
parser = cli_parser()
args = parser.parse_args()
config = bin_utils.utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
log.debug("Script arguments:\n%s" % args)
def iter_input_elements():
for f in args.input_files:
f = osp.expanduser(f)
if osp.isfile(f):
yield DataFileElement(f)
else:
log.debug("Expanding glob: %s" % f)
for g in glob.glob(f):
yield DataFileElement(g)
log.info("Adding elements to data set")
#: :type: smqtk.representation.DataSet
ds = plugin.from_plugin_config(config['data_set'], get_data_set_impls())
ds.add_data(*iter_input_elements())
def main():
parser = cli_parser()
args = parser.parse_args()
config = bin_utils.utility_main_helper(default_config, args)
log = logging.getLogger(__name__)
log.debug("Script arguments:\n%s" % args)
def iter_input_elements():
for f in args.input_files:
f = osp.expanduser(f)
if osp.isfile(f):
yield DataFileElement(f)
else:
log.debug("Expanding glob: %s" % f)
for g in glob.glob(f):
yield DataFileElement(g)
log.info("Adding elements to data set")
#: :type: smqtk.representation.DataSet
ds = plugin.from_plugin_config(config['data_set'], get_data_set_impls())
ds.add_data(*iter_input_elements())
def default_config():
return {
"data_set": plugin.make_config(get_data_set_impls())
}
def main():
parser = cli_parser()
args = parser.parse_args()
#
# Setup logging
#
if not logging.getLogger().handlers:
if args.verbose:
bin_utils.initialize_logging(logging.getLogger(), logging.DEBUG)
else:
bin_utils.initialize_logging(logging.getLogger(), logging.INFO)
log = logging.getLogger("smqtk.scripts.iqr_app_model_generation")
search_app_config = json.loads(jsmin.jsmin(open(args.config).read()))
#
# Input parameters
#
# The following dictionaries are JSON configurations that are used to
# configure the various data structures and algorithms needed for the IQR demo
# application. Values here can be changed to suit your specific data and
# algorithm needs.
#
# See algorithm implementation doc-strings for more information on configuration
# parameters (see implementation class ``__init__`` method).
#
# base actions on a specific IQR tab configuration (choose index here)
if args.tab < 0 or args.tab > (len(search_app_config["iqr_tabs"]) - 1):
log.error("Invalid tab number provided.")
exit(1)
search_app_iqr_config = search_app_config["iqr_tabs"][args.tab]
# Configure DataSet implementation and parameters
data_set_config = search_app_iqr_config['data_set']
# Configure DescriptorGenerator algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
descriptor_generator_config = search_app_iqr_config['descr_generator']
# Configure NearestNeighborIndex algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
nn_index_config = search_app_iqr_config['nn_index']
# Configure RelevancyIndex algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
#
# The LibSvmHikRelevancyIndex implementation doesn't actually build a persistant
# model (or doesn't have to that is), but we're leaving this block here in
# anticipation of other potential implementations in the future.
#
rel_index_config = search_app_iqr_config['rel_index_config']
# Configure DescriptorElementFactory instance, which defines what implementation
# of DescriptorElement to use for storing generated descriptor vectors below.
descriptor_elem_factory_config = search_app_iqr_config['descriptor_factory']
#
# Initialize data/algorithms
#
# Constructing appropriate data structures and algorithms, needed for the IQR
# demo application, in preparation for model training.
#
descriptor_elem_factory = \
representation.DescriptorElementFactory \
.from_config(descriptor_elem_factory_config)
#: :type: representation.DataSet
data_set = \
plugin.from_plugin_config(data_set_config,
representation.get_data_set_impls())
#: :type: algorithms.DescriptorGenerator
descriptor_generator = \
plugin.from_plugin_config(descriptor_generator_config,
algorithms.get_descriptor_generator_impls())
#: :type: algorithms.NearestNeighborsIndex
nn_index = \
plugin.from_plugin_config(nn_index_config,
algorithms.get_nn_index_impls())
#: :type: algorithms.RelevancyIndex
rel_index = \
plugin.from_plugin_config(rel_index_config,
algorithms.get_relevancy_index_impls())
#
# Build models
#
# Perform the actual building of the models.
#
# Add data files to DataSet
DataFileElement = representation.get_data_element_impls()["DataFileElement"]
for fp in args.input_files:
fp = osp.expanduser(fp)
if osp.isfile(fp):
data_set.add_data(DataFileElement(fp))
else:
log.debug("Expanding glob: %s" % fp)
for g in glob.iglob(fp):
data_set.add_data(DataFileElement(g))
# Generate a mode if the generator defines a known generation method.
if hasattr(descriptor_generator, "generate_model"):
descriptor_generator.generate_model(data_set)
# Add other if-else cases for other known implementation-specific generation
# methods stubs
# Generate descriptors of data for building NN index.
data2descriptor = descriptor_generator.compute_descriptor_async(
data_set, descriptor_elem_factory
)
try:
nn_index.build_index(data2descriptor.itervalues())
except RuntimeError:
# Already built model, so skipping this step
pass
rel_index.build_index(data2descriptor.itervalues())
def default_config():
return {"data_set": plugin.make_config(get_data_set_impls())}
def run_file_list(c,
filelist_filepath,
checkpoint_filepath,
batch_size=None,
check_image=False):
"""
Top level function handling configuration and inputs/outputs.
:param c: Configuration dictionary (JSON)
:type c: dict
:param filelist_filepath: Path to a text file that lists paths to data
files, separated by new lines.
:type filelist_filepath: str
:param checkpoint_filepath: Output file to which we write input filepath to
SHA1 (UUID) relationships.
:type checkpoint_filepath:
:param batch_size: Optional batch size (None default) of data elements to
process / descriptors to compute at a time. This causes files and
stores to be written to incrementally during processing instead of
one single batch transaction at a time.
:type batch_size:
:param check_image: Enable checking image loading from file before queueing
that file for processing. If the check fails, the file is skipped
instead of a halting exception being raised.
:type check_image: bool
"""
log = logging.getLogger(__name__)
file_paths = [l.strip() for l in open(filelist_filepath)]
log.info("Making descriptor factory")
factory = DescriptorElementFactory.from_config(c['descriptor_factory'])
log.info("Making descriptor index")
#: :type: smqtk.representation.DescriptorIndex
descriptor_index = plugin.from_plugin_config(c['descriptor_index'],
get_descriptor_index_impls())
data_set = None
if c['optional_data_set']['type'] is None:
log.info("Not saving loaded data elements to data set")
else:
log.info("Initializing data set to append to")
#: :type: smqtk.representation.DataSet
data_set = plugin.from_plugin_config(c['optional_data_set'],
get_data_set_impls())
log.info("Making descriptor generator '%s'",
c['descriptor_generator']['type'])
#: :type: smqtk.algorithms.DescriptorGenerator
generator = plugin.from_plugin_config(c['descriptor_generator'],
get_descriptor_generator_impls())
def iter_valid_elements():
def is_valid(file_path):
dfe = DataFileElement(file_path)
if is_valid_element(
dfe,
valid_content_types=generator.valid_content_types(),
check_image=check_image):
return dfe
else:
return False
data_elements = collections.deque()
valid_files_filter = parallel.parallel_map(is_valid,
file_paths,
name="check-file-type",
use_multiprocessing=True)
for dfe in valid_files_filter:
if dfe:
yield dfe
if data_set is not None:
data_elements.append(dfe)
if batch_size and len(data_elements) == batch_size:
log.debug(
"Adding data element batch to set (size: %d)",
len(data_elements))
data_set.add_data(*data_elements)
data_elements.clear()
# elements only collected if we have a data-set configured, so add any
# still in the deque to the set
if data_elements:
log.debug("Adding data elements to set (size: %d",
len(data_elements))
data_set.add_data(*data_elements)
log.info("Computing descriptors")
m = compute_many_descriptors(
iter_valid_elements(),
generator,
factory,
descriptor_index,
batch_size=batch_size,
)
# Recording computed file paths and associated file UUIDs (SHA1)
cf = open(checkpoint_filepath, 'w')
cf_writer = csv.writer(cf)
try:
rps = [0] * 7
for fp, descr in m:
cf_writer.writerow([fp, descr.uuid()])
report_progress(log.debug, rps, 1.)
finally:
del cf_writer
cf.close()
log.info("Done")
def main():
args = cli_parser().parse_args()
ui_config_filepath, iqr_config_filepath = args.config
llevel = logging.DEBUG if args.verbose else logging.INFO
tab = args.tab
input_files_globs = args.input_files
# Not using `bin_utils.utility_main_helper`` due to deviating from single-
# config-with-default usage.
bin_utils.initialize_logging(logging.getLogger('smqtk'), llevel)
bin_utils.initialize_logging(logging.getLogger('__main__'), llevel)
log = logging.getLogger(__name__)
log.info("Loading UI config: '{}'".format(ui_config_filepath))
ui_config, ui_config_loaded = bin_utils.load_config(ui_config_filepath)
log.info("Loading IQR config: '{}'".format(iqr_config_filepath))
iqr_config, iqr_config_loaded = bin_utils.load_config(iqr_config_filepath)
if not (ui_config_loaded and iqr_config_loaded):
raise RuntimeError("One or both configuration files failed to load.")
# Ensure the given "tab" exists in UI configuration.
if tab is None:
log.error("No configuration tab provided to drive model generation.")
exit(1)
if tab not in ui_config["iqr_tabs"]:
log.error("Invalid tab provided: '{}'. Available tags: {}"
.format(tab, list(ui_config["iqr_tabs"])))
exit(1)
#
# Gather Configurations
#
log.info("Extracting plugin configurations")
ui_tab_config = ui_config["iqr_tabs"][tab]
iqr_plugins_config = iqr_config['iqr_service']['plugins']
# Configure DataSet implementation and parameters
data_set_config = ui_tab_config['data_set']
# Configure DescriptorElementFactory instance, which defines what
# implementation of DescriptorElement to use for storing generated
# descriptor vectors below.
descriptor_elem_factory_config = iqr_plugins_config['descriptor_factory']
# Configure DescriptorGenerator algorithm implementation, parameters and
# persistent model component locations (if implementation has any).
descriptor_generator_config = iqr_plugins_config['descriptor_generator']
# Configure NearestNeighborIndex algorithm implementation, parameters and
# persistent model component locations (if implementation has any).
nn_index_config = iqr_plugins_config['neighbor_index']
#
# Initialize data/algorithms
#
# Constructing appropriate data structures and algorithms, needed for the
# IQR demo application, in preparation for model training.
#
log.info("Instantiating plugins")
#: :type: representation.DataSet
data_set = \
plugin.from_plugin_config(data_set_config,
representation.get_data_set_impls())
descriptor_elem_factory = \
representation.DescriptorElementFactory \
.from_config(descriptor_elem_factory_config)
#: :type: algorithms.DescriptorGenerator
descriptor_generator = \
plugin.from_plugin_config(descriptor_generator_config,
algorithms.get_descriptor_generator_impls())
#: :type: algorithms.NearestNeighborsIndex
nn_index = \
plugin.from_plugin_config(nn_index_config,
algorithms.get_nn_index_impls())
#
# Build models
#
log.info("Adding files to dataset '{}'".format(data_set))
for g in input_files_globs:
g = osp.expanduser(g)
if osp.isfile(g):
data_set.add_data(DataFileElement(g, readonly=True))
else:
log.debug("Expanding glob: %s" % g)
for fp in glob.iglob(g):
data_set.add_data(DataFileElement(fp, readonly=True))
# Generate a model if the generator defines a known generation method.
try:
log.debug("descriptor generator as model to generate?")
descriptor_generator.generate_model(data_set)
except AttributeError as ex:
log.debug("descriptor generator as model to generate - Nope: {}"
.format(str(ex)))
# Generate descriptors of data for building NN index.
log.info("Computing descriptors for data set with {}"
.format(descriptor_generator))
data2descriptor = descriptor_generator.compute_descriptor_async(
data_set, descriptor_elem_factory
)
# Possible additional support steps before building NNIndex
try:
# Fit the LSH index functor
log.debug("Has LSH Functor to fit?")
nn_index.lsh_functor.fit(six.itervalues(data2descriptor))
except AttributeError as ex:
log.debug("Has LSH Functor to fit - Nope: {}".format(str(ex)))
log.info("Building nearest neighbors index {}".format(nn_index))
nn_index.build_index(six.itervalues(data2descriptor))
def main():
parser = cli_parser()
args = parser.parse_args()
#
# Setup logging
#
if not logging.getLogger().handlers:
if args.verbose:
bin_utils.initialize_logging(logging.getLogger(), logging.DEBUG)
else:
bin_utils.initialize_logging(logging.getLogger(), logging.INFO)
log = logging.getLogger("smqtk.scripts.iqr_app_model_generation")
search_app_config = json.loads(jsmin.jsmin(open(args.config).read()))
#
# Input parameters
#
# The following dictionaries are JSON configurations that are used to
# configure the various data structures and algorithms needed for the IQR demo
# application. Values here can be changed to suit your specific data and
# algorithm needs.
#
# See algorithm implementation doc-strings for more information on configuration
# parameters (see implementation class ``__init__`` method).
#
# base actions on a specific IQR tab configuration (choose index here)
if args.tab < 0 or args.tab > (len(search_app_config["iqr_tabs"]) - 1):
log.error("Invalid tab number provided.")
exit(1)
search_app_iqr_config = search_app_config["iqr_tabs"][args.tab]
# Configure DataSet implementation and parameters
data_set_config = search_app_iqr_config['data_set']
# Configure DescriptorGenerator algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
descriptor_generator_config = search_app_iqr_config['descr_generator']
# Configure NearestNeighborIndex algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
nn_index_config = search_app_iqr_config['nn_index']
# Configure RelevancyIndex algorithm implementation, parameters and
# persistant model component locations (if implementation has any).
#
# The LibSvmHikRelevancyIndex implementation doesn't actually build a persistant
# model (or doesn't have to that is), but we're leaving this block here in
# anticipation of other potential implementations in the future.
#
rel_index_config = search_app_iqr_config['rel_index_config']
# Configure DescriptorElementFactory instance, which defines what implementation
# of DescriptorElement to use for storing generated descriptor vectors below.
descriptor_elem_factory_config = search_app_iqr_config[
'descriptor_factory']
#
# Initialize data/algorithms
#
# Constructing appropriate data structures and algorithms, needed for the IQR
# demo application, in preparation for model training.
#
descriptor_elem_factory = \
representation.DescriptorElementFactory \
.from_config(descriptor_elem_factory_config)
#: :type: representation.DataSet
data_set = \
plugin.from_plugin_config(data_set_config,
representation.get_data_set_impls())
#: :type: algorithms.DescriptorGenerator
descriptor_generator = \
plugin.from_plugin_config(descriptor_generator_config,
algorithms.get_descriptor_generator_impls())
#: :type: algorithms.NearestNeighborsIndex
nn_index = \
plugin.from_plugin_config(nn_index_config,
algorithms.get_nn_index_impls())
#: :type: algorithms.RelevancyIndex
rel_index = \
plugin.from_plugin_config(rel_index_config,
algorithms.get_relevancy_index_impls())
#
# Build models
#
# Perform the actual building of the models.
#
# Add data files to DataSet
DataFileElement = representation.get_data_element_impls(
)["DataFileElement"]
for fp in args.input_files:
fp = osp.expanduser(fp)
if osp.isfile(fp):
data_set.add_data(DataFileElement(fp))
else:
log.debug("Expanding glob: %s" % fp)
for g in glob.iglob(fp):
data_set.add_data(DataFileElement(g))
# Generate a mode if the generator defines a known generation method.
if hasattr(descriptor_generator, "generate_model"):
descriptor_generator.generate_model(data_set)
# Add other if-else cases for other known implementation-specific generation
# methods stubs
# Generate descriptors of data for building NN index.
data2descriptor = descriptor_generator.compute_descriptor_async(
data_set, descriptor_elem_factory)
try:
nn_index.build_index(six.itervalues(data2descriptor))
except RuntimeError:
# Already built model, so skipping this step
pass
rel_index.build_index(six.itervalues(data2descriptor))
