def test_retinanet_d121(self):
from cral.models.object_detection import RetinanetConfig, retinanet_densenet121
config = RetinanetConfig()
model, preprocessing_fn = retinanet_densenet121(
num_classes=4,
num_anchors_per_location=config.num_anchors(),
weights=None)
# print(model.summary())
keras.backend.clear_session()
def test_retinanet(self):
from cral.pipeline import ObjectDetectionPipe
from cral.models.object_detection import RetinanetConfig
pipe = ObjectDetectionPipe()
pipe.add_data(
train_images_dir=os.path.join(self.dataset, 'images'),
train_anno_dir=os.path.join(self.dataset, 'annotations',
'pascalvoc_xml'),
annotation_format='pascal_voc',
split=0.2)
pipe.lock_data()
pipe.set_algo(feature_extractor='resnet50', config=RetinanetConfig())
pipe.train(
num_epochs=2,
snapshot_prefix='test_retinanet',
snapshot_path='/tmp',
snapshot_every_n=10,
batch_size=1,
steps_per_epoch=2)
tf.keras.backend.clear_session()
