def build_decoder(input_specs,
model_config,
l2_regularizer: tf.keras.regularizers.Regularizer = None):
"""Builds decoder from a config.
Args:
input_specs: `dict` input specifications. A dictionary consists of
{level: TensorShape} from a backbone.
model_config: A OneOfConfig. Model config.
l2_regularizer: tf.keras.regularizers.Regularizer instance. Default to None.
Returns:
tf.keras.Model instance of the decoder.
"""
decoder_type = model_config.decoder.type
decoder_cfg = model_config.decoder.get()
norm_activation_config = model_config.norm_activation
if decoder_type == 'identity':
decoder = None
elif decoder_type == 'fpn':
decoder = decoders.FPN(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_filters=decoder_cfg.num_filters,
use_separable_conv=decoder_cfg.use_separable_conv,
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)
elif decoder_type == 'nasfpn':
decoder = decoders.NASFPN(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_filters=decoder_cfg.num_filters,
num_repeats=decoder_cfg.num_repeats,
use_separable_conv=decoder_cfg.use_separable_conv,
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)
elif decoder_type == 'aspp':
decoder = decoders.ASPP(
level=decoder_cfg.level,
dilation_rates=decoder_cfg.dilation_rates,
num_filters=decoder_cfg.num_filters,
dropout_rate=decoder_cfg.dropout_rate,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
activation=norm_activation_config.activation,
kernel_regularizer=l2_regularizer)
else:
raise ValueError('Decoder {!r} not implement'.format(decoder_type))
return decoder
def test_fpn_decoder_creation(self, num_filters, use_separable_conv):
"""Test creation of FPN decoder."""
min_level = 3
max_level = 7
input_specs = {}
for level in range(min_level, max_level):
input_specs[str(level)] = tf.TensorShape(
[1, 128 // (2**level), 128 // (2**level), 3])
network = decoders.FPN(input_specs=input_specs,
num_filters=num_filters,
use_separable_conv=use_separable_conv,
use_sync_bn=True)
model_config = configs.retinanet.RetinaNet()
model_config.min_level = min_level
model_config.max_level = max_level
model_config.num_classes = 10
model_config.input_size = [None, None, 3]
model_config.decoder = decoders_cfg.Decoder(
type='fpn',
fpn=decoders_cfg.FPN(num_filters=num_filters,
use_separable_conv=use_separable_conv))
factory_network = factory.build_decoder(input_specs=input_specs,
model_config=model_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def build_decoder(
input_specs: Mapping[str, tf.TensorShape],
model_config: hyperparams.Config,
l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
) -> tf.keras.Model:
"""Builds decoder from a config.
Args:
input_specs: A `dict` of input specifications. A dictionary consists of
{level: TensorShape} from a backbone.
model_config: A OneOfConfig. Model config.
l2_regularizer: A `tf.keras.regularizers.Regularizer` instance. Default to
None.
Returns:
A `tf.keras.Model` instance of the decoder.
"""
decoder_type = model_config.decoder.type
decoder_cfg = model_config.decoder.get()
norm_activation_config = model_config.norm_activation
if decoder_type == 'identity':
decoder = None
elif decoder_type == 'fpn':
decoder = decoders.FPN(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_filters=decoder_cfg.num_filters,
use_separable_conv=decoder_cfg.use_separable_conv,
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)
else:
raise ValueError('Decoder {!r} not implement'.format(decoder_type))
return decoder
def build_decoder(
input_specs: Mapping[str, tf.TensorShape],
model_config: hyperparams.Config,
norm_activation_config: hyperparams.Config = None,
l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
) -> tf.keras.Model:
"""Builds decoder from a config.
Args:
input_specs: A `dict` of input specifications. A dictionary consists of
{level: TensorShape} from a backbone.
model_config: A OneOfConfig. Model config.
l2_regularizer: A `tf.keras.regularizers.Regularizer` instance. Default to
None.
Returns:
A `tf.keras.Model` instance of the decoder.
"""
decoder_type = model_config.decoder.type
decoder_cfg = model_config.decoder.get()
if norm_activation_config is None:
norm_activation_config = model_config.norm_activation
if decoder_type == 'identity':
decoder = None
elif decoder_type == 'fpn':
decoder = decoders.FPN(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_filters=decoder_cfg.num_filters,
use_separable_conv=decoder_cfg.use_separable_conv,
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)
elif decoder_type == 'nasfpn':
decoder = decoders.NASFPN(
input_specs=input_specs,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_filters=decoder_cfg.num_filters,
num_repeats=decoder_cfg.num_repeats,
use_separable_conv=decoder_cfg.use_separable_conv,
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)
elif decoder_type == 'aspp':
decoder = decoders.ASPP(
level=decoder_cfg.level,
dilation_rates=decoder_cfg.dilation_rates,
num_filters=decoder_cfg.num_filters,
pool_kernel_size=decoder_cfg.pool_kernel_size,
dropout_rate=decoder_cfg.dropout_rate,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
activation=norm_activation_config.activation,
kernel_regularizer=l2_regularizer)
elif decoder_type == 'hardnet':
decoder = decoders.HardNetDecoder(
model_id=decoder_cfg.model_id,
input_specs=input_specs,
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)
elif decoder_type == 'pan':
decoder = decoders.PAN(
input_specs=input_specs,
routes=decoder_cfg.levels,
num_filters=decoder_cfg.num_filters,
num_convs=decoder_cfg.num_convs,
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)
else:
raise ValueError('Decoder {!r} not implement'.format(decoder_type))
return decoder
