def test_model_initializing(self, init_checkpoint_modules):
shared_backbone = ('segmentation_backbone' not in init_checkpoint_modules)
shared_decoder = ('segmentation_decoder' not in init_checkpoint_modules and
shared_backbone)
task_config = cfg.PanopticMaskRCNNTask(
model=cfg.PanopticMaskRCNN(
num_classes=2,
input_size=[640, 640, 3],
segmentation_model=segmentation_cfg.SemanticSegmentationModel(
decoder=decoder_cfg.Decoder(type='fpn')),
shared_backbone=shared_backbone,
shared_decoder=shared_decoder))
task = panoptic_maskrcnn.PanopticMaskRCNNTask(task_config)
model = task.build_model()
ckpt = tf.train.Checkpoint(**model.checkpoint_items)
ckpt_save_dir = self.create_tempdir().full_path
ckpt.save(os.path.join(ckpt_save_dir, 'ckpt'))
if (init_checkpoint_modules == ['all'] or
'backbone' in init_checkpoint_modules):
task._task_config.init_checkpoint = ckpt_save_dir
if ('segmentation_backbone' in init_checkpoint_modules or
'segmentation_decoder' in init_checkpoint_modules):
task._task_config.segmentation_init_checkpoint = ckpt_save_dir
task._task_config.init_checkpoint_modules = init_checkpoint_modules
task.initialize(model)
def test_builder(self, backbone_type, input_size,
segmentation_backbone_type, segmentation_decoder_type):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
segmentation_output_stride = 16
level = int(np.math.log2(segmentation_output_stride))
segmentation_model = semantic_segmentation.SemanticSegmentationModel(
num_classes=2,
backbone=backbones.Backbone(type=segmentation_backbone_type),
decoder=decoders.Decoder(type=segmentation_decoder_type),
head=semantic_segmentation.SegmentationHead(level=level))
model_config = panoptic_maskrcnn_cfg.PanopticMaskRCNN(
num_classes=num_classes,
segmentation_model=segmentation_model,
backbone=backbones.Backbone(type=backbone_type),
shared_backbone=segmentation_backbone_type is None,
shared_decoder=segmentation_decoder_type is None)
l2_regularizer = tf.keras.regularizers.l2(5e-5)
_ = factory.build_panoptic_maskrcnn(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
