def _make_tf_train_config(self):
self.train_iters = self.tf_data_dicts['train'][
'sample_cnt'] // self.config['batch_size']['train']
total_steps = self.config['epochs'] * self.train_iters
src_size = self.config['input_size']
input_size = (src_size['height'], src_size['width'])
base_config_path, remake_config_fn = determine_tf_config(
self.model_configuration)
tf_config = load_sample_config(base_config_path)
weights_dir = osp.join(sly.TaskPaths.MODEL_DIR, "model_weights")
if (not sly.fs.dir_exists(weights_dir)
) or sly.fs.dir_empty(weights_dir):
checkpoint = None
logger.info('Weights will not be inited.')
else:
checkpoint = osp.join(sly.TaskPaths.MODEL_DIR, 'model_weights',
'model.ckpt')
logger.info('Weights will be loaded from previous train.')
self.tf_config = remake_config_fn(
tf_config, 'SUPERVISELY_FORMAT', total_steps,
max(self.class_title_to_idx.values()), input_size,
self.config['batch_size']['train'], self.config['lr'], checkpoint)
logger.info(self.tf_config)
logger.info('Model config created.')
def _make_tf_train_config(self):
self.train_iters = self.tf_data_dicts['train'][
'sample_cnt'] // self.config['batch_size']['train']
total_steps = self.config['epochs'] * self.train_iters
src_size = self.config['input_size']
input_size_wh = (src_size['width'], src_size['height'])
tf_config = load_sample_config(self.base_mask_config_path)
if self.helper.model_dir_is_empty():
checkpoint = None
logger.info('Weights will not be inited.')
else:
checkpoint = osp.join(self.helper.paths.model_dir, 'model_weights',
'model.ckpt')
logger.info('Weights will be loaded from previous train.')
self.tf_config = remake_mask_rcnn_config(
tf_config, 'SUPERVISELY_FORMAT', total_steps,
len(self.out_classes), input_size_wh,
self.config['batch_size']['train'], self.config['lr'], checkpoint)
logger.info('Model config created.')