def _load_train_config(self):
model_dir = sly.TaskPaths(determine_in_project=False).model_dir
train_config_rw = TrainConfigRW(model_dir)
if not train_config_rw.train_config_exists:
raise RuntimeError(
'Unable to run inference, config from training wasn\'t found.')
self.train_config = train_config_rw.load()
src_size = self.train_config['settings']['input_size']
self.input_size_wh = (src_size['width'], src_size['height'])
logger.info('Model input size is read (for auto-rescale).',
extra={
'input_size': {
'width': self.input_size_wh[0],
'height': self.input_size_wh[1]
}
})
self.class_title_to_idx = self.train_config['class_title_to_idx']
self.train_classes = sly.FigClasses(self.train_config['out_classes'])
logger.info('Read model internal class mapping',
extra={'class_mapping': self.class_title_to_idx})
logger.info('Read model out classes',
extra={'classes': self.train_classes.py_container})
self.out_class_mapping = {
x: self.class_title_to_idx[x]
for x in (x['title'] for x in self.train_classes)
}
def _load_train_config(self):
train_config_rw = TrainConfigRW(self.helper.paths.model_dir)
if not train_config_rw.train_config_exists:
raise RuntimeError(
'Unable to run inference, config from training wasn\'t found.')
self.train_config = train_config_rw.load()
logger.info(
'Model input size is read (for auto-rescale).',
extra={
'input_size': {
'width': 1200,
'height':
1200 # input shape is fixed for Faster with NasNet encoder
}
})
self.class_title_to_idx = self.train_config['mapping']
self.train_classes = sly.FigClasses(self.train_config['classes'])
logger.info('Read model internal class mapping',
extra={'class_mapping': self.class_title_to_idx})
logger.info('Read model out classes',
extra={'classes': self.train_classes.py_container})
out_class_mapping = {
x: self.class_title_to_idx[x]
for x in (x['title'] for x in self.train_classes)
}
self.inv_mapping = inverse_mapping(out_class_mapping)
def _check_prev_model_config(self):
prev_model_dir = self.helper.paths.model_dir
prev_config_rw = TrainConfigRW(prev_model_dir)
if not prev_config_rw.train_config_exists:
raise RuntimeError('Unable to continue_training, config for previous training wasn\'t found.')
prev_config = prev_config_rw.load()
old_class_mapping = prev_config.get('class_title_to_idx', {})
if self.class_title_to_idx != old_class_mapping:
raise RuntimeError('Unable to continue training, class mapping is inconsistent with previous model.')
def dump_model(saver, sess, is_best, opt_data):
out_dir = self.helper.checkpoints_saver.get_dir_to_write()
TrainConfigRW(out_dir).save(self.out_config)
model_fpath = os.path.join(out_dir, 'model_weights', 'model.ckpt')
saver.save(sess, model_fpath)
self.helper.checkpoints_saver.saved(is_best, opt_data)
def main():
args = parse_args()
with open(args.in_file) as f:
lines = f.readlines()
lines = [ln for ln in (line.strip() for line in lines) if ln]
out_classes = sly.FigClasses()
for x in construct_detection_classes(lines):
out_classes.add(x)
cls_mapping = {x: i for i, x in enumerate(lines)}
res_cfg = {
'settings': {},
'out_classes': out_classes.py_container,
'class_title_to_idx': cls_mapping,
}
saver = TrainConfigRW(args.out_dir)
saver.save(res_cfg)
print('Done: {} -> {}'.format(args.in_file, saver.train_config_fpath))
def _load_train_config(self):
train_config_rw = TrainConfigRW(self.helper.paths.model_dir)
if not train_config_rw.train_config_exists:
raise RuntimeError(
'Unable to run inference, config from training wasn\'t found.')
self.train_config = train_config_rw.load()
self.class_title_to_idx = self.train_config['mapping']
self.train_classes = sly.FigClasses(self.train_config['classes'])
logger.info('Read model internal class mapping',
extra={'class_mapping': self.class_title_to_idx})
logger.info('Read model out classes',
extra={'classes': self.train_classes.py_container})
out_class_mapping = {
x: self.class_title_to_idx[x]
for x in (x['title'] for x in self.train_classes)
}
self.inv_mapping = inverse_mapping(out_class_mapping)
def _load_train_config(self):
model_dir = self.helper.paths.model_dir
train_config_rw = TrainConfigRW(model_dir)
if not train_config_rw.train_config_exists:
raise RuntimeError(
'Unable to run inference, config from training wasn\'t found.')
train_config = train_config_rw.load()
self.train_classes = sly.FigClasses(train_config['out_classes'])
tr_class_mapping = train_config['class_title_to_idx']
# create
rev_mapping = {v: k for k, v in tr_class_mapping.items()}
self.train_names = [rev_mapping[i]
for i in range(len(rev_mapping))] # ordered
logger.info('Read model internal class mapping',
extra={'class_mapping': tr_class_mapping})
logger.info('Read model out classes',
extra={'classes': self.train_classes.py_container})
def _create_checkpoints_dir(self):
for epoch in range(self.config['epochs']):
ckpt_dir = os.path.join(self.helper.paths.results_dir, '{:08}'.format(epoch))
sly.mkdir(ckpt_dir)
save_config(self.yolo_config, os.path.join(ckpt_dir, 'model.cfg'))
TrainConfigRW(ckpt_dir).save(self.out_config)
def _dump_model(self, is_best, opt_data):
out_dir = self.helper.checkpoints_saver.get_dir_to_write()
TrainConfigRW(out_dir).save(self.out_config)
WeightsRW(out_dir).save(self.model)
self.helper.checkpoints_saver.saved(is_best, opt_data)