def default_config():
return {
"descriptor_generator":
make_default_config(DescriptorGenerator.get_impls()),
"descriptor_factory":
DescriptorElementFactory.get_default_config(),
"descriptor_set":
make_default_config(DescriptorSet.get_impls()),
"optional_data_set":
make_default_config(DataSet.get_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': make_default_config(DataSet.get_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'] = make_default_config(DataSet.get_impls())
return d
def main():
args = cli_parser().parse_args()
config = cli.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 = from_config_dict(config['plugins']['data_set'],
DataSet.get_impls())
batch = collections.deque()
pr = cli.ProgressReporter(log.info, 1.0).start()
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()
pr.increment_report()
pr.report()
if batch:
data_set.add_data(*batch)
log.info('Done')
def main():
parser = cli_parser()
args = parser.parse_args()
config = cli.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")
ds = from_config_dict(config['data_set'], DataSet.get_impls())
ds.add_data(*iter_input_elements())
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'] = \
from_config_dict(merged['data_set'], DataSet.get_impls())
return cls(parent_app, **merged)
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 = [line.strip() for line in open(filelist_filepath)]
log.info("Making descriptor factory")
factory = DescriptorElementFactory.from_config(c['descriptor_factory'])
log.info("Making descriptor index")
descriptor_set = cast(
DescriptorSet,
from_config_dict(c['descriptor_set'], DescriptorSet.get_impls()))
# ``data_set`` added to within the ``iter_valid_elements`` function.
data_set: Optional[DataSet] = 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")
data_set = cast(
DataSet,
from_config_dict(c['optional_data_set'], DataSet.get_impls()))
log.info("Making descriptor generator '%s'",
c['descriptor_generator']['type'])
generator = cast(
DescriptorGenerator,
from_config_dict(c['descriptor_generator'],
DescriptorGenerator.get_impls()))
def iter_valid_elements():
def is_valid(file_path):
e = DataFileElement(file_path)
if is_valid_element(
e,
valid_content_types=generator.valid_content_types(),
check_image=check_image):
return e
else:
return False
data_elements: Deque[DataFileElement] = 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_set is not None and 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_set,
batch_size=batch_size,
)
# Recording computed file paths and associated file UUIDs (SHA1)
cf = open(checkpoint_filepath, 'w')
cf_writer = csv.writer(cf)
try:
pr = ProgressReporter(log.debug, 1.0).start()
for de, descr in m:
# We know that we are using DataFileElements going into the
# compute_many_descriptors, so we can assume that's what comes out
# of it as well.
# noinspection PyProtectedMember
cf_writer.writerow([de._filepath, descr.uuid()])
pr.increment_report()
pr.report()
finally:
del cf_writer
cf.close()
log.info("Done")
def default_config():
return {
"data_set": make_default_config(DataSet.get_impls())
}