def run(self):
exporter = ModelExporter(self.gen_pred_config())
rm_n_mkdir(self.model_export_dir)
exporter.export_compact(
filename="{}/compact.pb".format(self.model_export_dir))
exporter.export_serving(
os.path.join(self.model_export_dir, "serving"),
signature_name="serving_default",
)
print(f"Saved model to {self.model_export_dir}.")
def run(self, save_only):
if self.inf_auto_find_chkpt:
self.inf_model_path = os.path.join(self.save_dir, str(max([int(x) for x in [name for name in os.listdir(self.save_dir) if os.path.isdir(os.path.join(self.save_dir, name))]])))
print(f"Inference model path: <{self.inf_model_path}>")
print('-----Auto Selecting Checkpoint Basing On "%s" Through "%s" Comparison' % \
(self.inf_auto_metric, self.inf_auto_comparator))
model_path, stat = get_best_chkpts(self.inf_model_path, self.inf_auto_metric, self.inf_auto_comparator)
print('Selecting: %s' % model_path)
print('Having Following Statistics:')
for key, value in stat.items():
print('\t%s: %s' % (key, value))
else:
model_path = self.inf_model_path
model_constructor = self.get_model()
pred_config = PredictConfig(
model = model_constructor(),
session_init = get_model_loader(model_path),
input_names = self.eval_inf_input_tensor_names,
output_names = self.eval_inf_output_tensor_names)
predictor = OfflinePredictor(pred_config)
if save_only:
exporter = ModelExporter(pred_config)
rm_n_mkdir(self.model_export_dir)
print ('{}/compact.pb'.format(self.model_export_dir))
exporter.export_compact(filename='{}/compact.pb'.format(self.model_export_dir))
exporter.export_serving(os.path.join(self.model_export_dir, 'serving'), signature_name='serving_default')
return
for num, data_dir in enumerate(self.inf_data_list):
save_dir = os.path.join(self.inf_output_dir, str(num))
file_list = glob.glob(os.path.join(data_dir, '*{}'.format(self.inf_imgs_ext)))
file_list.sort() # ensure same order
rm_n_mkdir(save_dir)
for filename in file_list:
filename = os.path.basename(filename)
basename = filename.split('.')[0]
print(data_dir, basename, end=' ', flush=True)
##
img = cv2.imread(os.path.join(data_dir, filename))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
##
pred_map = self.__gen_prediction(img, predictor)
sio.savemat(os.path.join(save_dir,'{}.mat'.format(basename)), {'result':[pred_map]})
print(f"Finished. {datetime.now().strftime('%H:%M:%S.%f')}")
def save_model(model_paths, model, target="", compact=False):
"""Save a model to given dir"""
from os import path
from os import makedirs
import tensorpack as tp
from tensorpack.tfutils.varmanip import get_checkpoint_path
from tensorpack.tfutils.export import ModelExporter
import misc.logger as logger
_L = logger.getLogger("Saver")
save_to_modeldir = target is ""
for model_path in model_paths:
# get model path
real_path = get_checkpoint_path(model_path)
abs_p = path.realpath(model_path)
if (not path.isfile(abs_p)):
_L.error("{} is not a model file".format(model_path))
continue
# save to same folder as model
if (save_to_modeldir):
target = path.dirname(abs_p)
# make sure the folder exists
if not path.exists(target):
makedirs(target)
conf = tp.PredictConfig(session_init=tp.get_model_loader(model_path),
model=model,
input_names=["input"],
output_names=["emb"])
exporter = ModelExporter(conf)
if (compact):
out = path.join(target, "{}.pb".format(path.basename(real_path)))
_L.info("saving {} to {}".format(path.basename(real_path), out))
exporter.export_compact(out)
else:
_L.info("compact saving {} to {}".format(path.basename(real_path),
target))
exporter.export_serving(target)
