def build_basnet_model(
input_specs: tf.keras.layers.InputSpec,
model_config: basnet_cfg.BASNetModel,
l2_regularizer: tf.keras.regularizers.Regularizer = None):
"""Builds BASNet model."""
backbone = backbones.factory.build_backbone(
input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
decoder = decoder_factory.build_decoder(
input_specs=backbone.output_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
refinement = refunet.RefUnet()
#head_config = model_config.head
norm_activation_config = model_config.norm_activation
"""
head = segmentation_heads.SegmentationHead(
num_classes=model_config.num_classes,
level=head_config.level,
num_convs=head_config.num_convs,
num_filters=head_config.num_filters,
upsample_factor=head_config.upsample_factor,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer)
"""
model = basnet_model.BASNetModel(backbone, decoder, refinement)
return model
def test_network_creation(self):
"""Test creation of BASNet Decoder."""
input_size = 224
tf.keras.backend.set_image_data_format('channels_last')
inputs = tf.keras.Input(shape=(input_size, input_size, 3),
batch_size=1)
backbone = basnet_en.BASNet_En()
network = basnet_de.BASNet_De(input_specs=backbone.output_specs)
module = refunet.RefUnet()
endpoints = backbone(inputs)
sups = network(endpoints)
sups['ref'] = module(sups['7'])
self.assertIn(str(ref), sups)
self.assertAllEqual([1, input_size, input_size, 1],
sups[str(ref)].shape.as_list())
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
input_specs=layers.InputSpec(shape=[None, None, None, 1]),
activation='relu',
use_sync_bn=False,
norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
)
module = refunet.RefUnet()
expected_config = dict(kwargs)
self.assertEqual(module.get_config(), expected_config)
# Create another network object from the first object's config.
new_module = refunet.RefUnet.from_config(module.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(module.get_config(), new_module.get_config())
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(module.get_config(), new_module.get_config())
"""
if __name__ == '__main__':
tf.test.main()
"""
input_size = 224
tf.keras.backend.set_image_data_format('channels_last')
inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
backbone = basnet_en.BASNet_En()
network = basnet_de.BASNet_De(input_specs=backbone.output_specs)
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
module = refunet.RefUnet()
print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
endpoints = backbone(inputs)
print(endpoints)
sups = network(endpoints)
sups['ref'] = module(sups['7'])
print(sups)
print(a)
print(a.backbone.basnet_en.model_id)
"""
tf.keras.backend.set_image_data_format('channels_last')
input_specs = tf.keras.layers.InputSpec(
shape=[None] + [224, 224, 3])
backbone = backbones.BASNet_En(
input_specs=input_specs)
decoder = decoders.BASNet_De(
input_specs=backbone.output_specs)
refinement = refunet.RefUnet()
backbone.model.load_weight('/home/ghpark/ckpt_basnet/ckpt-274352')
decoder.load_weight('/home/ghpark/ckpt_basnet/ckpt-274352')
refinement.load_weight('/home/ghpark/ckpt_basnet/ckpt-274352')
model = basnet_model.BASNetModel(
backbone=backbone,
decoder=decoder,
refinement=refinement
)
#model.load_weight('/home/ghpark/ckpt_basnet/ckpt-274352')