def get_predictions(self, model, weights_path):
""" Runs the prediction. """
self.model = model
self.weights_path = Path(weights_path)
weights_conf = self._load_weights()
out_folder = self.output_dir / self._make_basename() / get_timestamp()
self.logger.info('Results in:\n > {}'.format(out_folder))
if out_folder.exists():
self.logger.warning('Directory exists, might overwrite files')
else:
out_folder.mkdir(parents=True)
if weights_conf:
yaml.dump(weights_conf,
(out_folder / 'weights_config.yml').open('w'))
# Predict and store
for img_path in self.img_ls:
output_path = out_folder / img_path.name
self.logger.info('Processing file\n > {}'.format(img_path))
start = time()
sr_img = self._forward_pass(img_path)
end = time()
self.logger.info('Elapsed time: {}s'.format(end - start))
self.logger.info('Result in: {}'.format(output_path))
imageio.imwrite(output_path, sr_img)
def get_session_id(self, basename):
""" Returns unique session identifier. """
time_stamp = get_timestamp()
if basename:
session_id = '{b}_{ts}'.format(b=basename, ts=time_stamp)
else:
session_id = time_stamp
return session_id