def _create_centernet_model(model_id: int = 52,
num_hourglasses: int = 2
) -> centernet_model.CenterNetModel:
"""Create centernet model to load TF1 weights."""
task_config = centernet.CenterNetTask(
model=centernet.CenterNetModel(
backbone=backbones.Backbone(
type="hourglass",
hourglass=backbones.Hourglass(
model_id=model_id, num_hourglasses=num_hourglasses))))
model_config = task_config.model
backbone = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[1, 512, 512, 3]),
backbone_config=model_config.backbone,
norm_activation_config=model_config.norm_activation)
task_outputs = task_config.get_output_length_dict()
head = centernet_head.CenterNetHead(
input_specs=backbone.output_specs,
task_outputs=task_outputs,
input_levels=model_config.head.input_levels)
detect_generator_obj = detection_generator.CenterNetDetectionGenerator()
model = centernet_model.CenterNetModel(
backbone=backbone, head=head, detection_generator=detect_generator_obj)
logging.info("Successfully created centernet model.")
return model
def testBuildCenterNet(self):
backbone = hourglass.build_hourglass(
input_specs=tf.keras.layers.InputSpec(shape=[None, 512, 512, 3]),
backbone_config=backbones.Backbone(type='hourglass'),
norm_activation_config=common.NormActivation(use_sync_bn=True))
task_config = {
'ct_heatmaps': 90,
'ct_offset': 2,
'ct_size': 2,
}
input_levels = ['2_0', '2']
head = centernet_head.CenterNetHead(task_outputs=task_config,
input_specs=backbone.output_specs,
input_levels=input_levels)
detection_ge = detection_generator.CenterNetDetectionGenerator()
model = centernet_model.CenterNetModel(
backbone=backbone, head=head, detection_generator=detection_ge)
outputs = model(tf.zeros((5, 512, 512, 3)))
self.assertLen(outputs['raw_output'], 3)
self.assertLen(outputs['raw_output']['ct_heatmaps'], 2)
self.assertLen(outputs['raw_output']['ct_offset'], 2)
self.assertLen(outputs['raw_output']['ct_size'], 2)
self.assertEqual(outputs['raw_output']['ct_heatmaps'][0].shape,
(5, 128, 128, 90))
self.assertEqual(outputs['raw_output']['ct_offset'][0].shape,
(5, 128, 128, 2))
self.assertEqual(outputs['raw_output']['ct_size'][0].shape,
(5, 128, 128, 2))
def test_hourglass(self):
backbone = hourglass.build_hourglass(
input_specs=tf.keras.layers.InputSpec(shape=[None, 512, 512, 3]),
backbone_config=backbones.Backbone(type='hourglass'),
norm_activation_config=common.NormActivation(use_sync_bn=True))
inputs = np.zeros((2, 512, 512, 3), dtype=np.float32)
outputs = backbone(inputs)
self.assertEqual(outputs['2_0'].shape, (2, 128, 128, 256))
self.assertEqual(outputs['2'].shape, (2, 128, 128, 256))
class CenterNetModel(hyperparams.Config):
"""Config for centernet model."""
num_classes: int = 90
max_num_instances: int = 128
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='hourglass', hourglass=backbones.Hourglass(model_id=52))
head: CenterNetHead = CenterNetHead()
# pylint: disable=line-too-long
detection_generator: CenterNetDetectionGenerator = CenterNetDetectionGenerator()
norm_activation: common.NormActivation = common.NormActivation(
norm_momentum=0.1, norm_epsilon=1e-5, use_sync_bn=True)