def init_app_runtime():
init_runtime(logging_level=constants.LOGGING_LEVEL)
logger.info(ENV.to_json() if ENV.IS_DEV_ENV else ENV.to_str())
# Copyright (c) 2020 the original author or authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express # or implied. See the License for the specific language governing # permissions and limitations under the License. import logging from src.init_runtime import init_runtime init_runtime(logging.DEBUG)
def get_plausible_thresholds_iterator(arg_count):
one_arg_values = [
0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85,
0.9, 0.95, 0.99
]
all_arg_values = list(itertools.product(one_arg_values, repeat=arg_count))
random.shuffle(all_arg_values)
return all_arg_values
def random_thresholds_generator(arg_count):
while True:
yield [random.uniform(0, 1) for _ in range(arg_count)]
if __name__ == '__main__':
init_runtime(logging_level=LOGGING_LEVEL)
logger.info(ENV.to_json() if ENV_MAIN.IS_DEV_ENV else ENV.to_str())
task = Facenet2018DetectionThresholdOptimization()
threshold_iterators = [
get_plausible_thresholds_iterator(task.arg_count),
random_thresholds_generator(task.arg_count)
]
storage = ResultsStorage()
optimizer = Optimizer(task, storage, checkpoint_every_s=120)
for threshold_iterator in threshold_iterators:
optimizer.optimize(threshold_iterator)