def log_verbose(folder_path, name_prefix=""):
verbose_filename = name_prefix + 'verbose'
gitdiff_filename = name_prefix + 'gitdiff'
f = tf_open_file_in_path(folder_path, verbose_filename, "w")
f.write(" ".join(sys.argv) + '\n')
f.write(get_flags() + '\n')
write_git_revision(f)
f.close()
f = tf_open_file_in_path(folder_path, gitdiff_filename, "wb")
write_gitdiff(f)
f.close()
def print_parse(unused_args):
model_path = FLAGS.search_model_json_path
parse_search_dir = FLAGS.parse_search_dir
model_args, _ = get_model_args_and_gparams(model_path, None)
tf.logging.info(model_args)
stages_args = parse_netarch.parse_stages_args(parse_search_dir,
base_model_args=model_args)
model_args.stages_args = stages_args
parse_dir = FLAGS.parse_search_dir
f = tf_open_file_in_path(parse_dir, FLAGS.parse_json_name + '.json', 'w')
json.dump(model_args, f, indent=4, ensure_ascii=False)
tf.logging.info(model_args)
def main(unused_argv):
save_json_name = FLAGS.save_json_name
orig_img_size = FLAGS.input_image_size
model_args = io_utils.load_json_as_attrdict(FLAGS.model_json_path)
img_size = math_utils.round_to_multiple_of(
orig_img_size * FLAGS.resol_coefficient, FLAGS.img_divisor)
params_dict = {key: getattr(FLAGS, key) for key in ['filters_divisor']}
global_params = GlobalParams(**params_dict)
model_args = compound_scale(model_args, global_params,
FLAGS.width_coefficient,
FLAGS.depth_coefficient, orig_img_size,
FLAGS.depth_list)
model_args.img_size = img_size
f = io_utils.tf_open_file_in_path(FLAGS.save_dir, save_json_name, 'w')
json.dump(model_args, f, indent=4, ensure_ascii=False)
def get_model_args_and_gparams(model_json_path, override_params):
"""
Gets model_args from json file.
Supports both tensorflow-style stages_args and more human-readable style.
"""
model_json = json.load(tf_open_file_in_path("", model_json_path, "r"),
object_pairs_hook=AttrDict)
model_args = AttrDict(model_json)
decoder = BlockArgsDecoder()
model_args.stages_args = decoder.decode_to_stages_args(
model_args.stages_args)
gparams_dict = parse_gparams_from_model_args(model_args)
global_params = GlobalParams(**gparams_dict)
if override_params:
global_params = global_params._replace(**override_params)
tf.logging.info('global_params= %s', global_params)
tf.logging.info('stages_args= %s', model_args.stages_args)
return model_args, global_params
def save_model_args(model_args, model_dir, filename='scaled_model_args.json'):
f = tf_open_file_in_path(model_dir, filename, 'w')
json.dump(model_args, f, indent=4, ensure_ascii=False)