def __init__(self, params):
super(ShapeMaskModel, self).__init__(params)
self._params = params
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._retinanet_head_fn = factory.retinanet_head_generator(params)
self._shape_prior_head_fn = factory.shapeprior_head_generator(params)
self._coarse_mask_fn = factory.coarsemask_head_generator(params)
self._fine_mask_fn = factory.finemask_head_generator(params)
self._outer_box_scale = params.architecture.outer_box_scale
# Loss function.
self._cls_loss_fn = losses.RetinanetClassLoss(
params.retinanet_loss, params.architecture.num_classes)
self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss)
self._box_loss_weight = params.retinanet_loss.box_loss_weight
# Mask loss function.
self._shapemask_prior_loss_fn = losses.ShapemaskMseLoss()
self._shapemask_loss_fn = losses.ShapemaskLoss()
self._shape_prior_loss_weight = (
params.shapemask_loss.shape_prior_loss_weight)
self._coarse_mask_loss_weight = (
params.shapemask_loss.coarse_mask_loss_weight)
self._fine_mask_loss_weight = (
params.shapemask_loss.fine_mask_loss_weight)
# Predict function.
self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator(
params.architecture.min_level, params.architecture.max_level,
params.postprocess)
def __init__(self, params):
super(RetinanetModel, self).__init__(params)
# For eval metrics.
self._params = params
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._head_fn = factory.retinanet_head_generator(params.retinanet_head)
# Loss function.
self._cls_loss_fn = losses.RetinanetClassLoss(params.retinanet_loss)
self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss)
self._box_loss_weight = params.retinanet_loss.box_loss_weight
self._keras_model = None
# Predict function.
self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator(
params.postprocess)
self._transpose_input = params.train.transpose_input
assert not self._transpose_input, 'Transpose input is not supportted.'
# Input layer.
input_shape = (params.retinanet_parser.output_size +
[params.retinanet_parser.num_channels])
self._input_layer = tf.keras.layers.Input(
shape=input_shape,
name='',
batch_size=self._params.train.batch_size,
dtype=tf.bfloat16 if self._use_bfloat16 else tf.float32)
def __init__(self, params):
super(RetinanetModel, self).__init__(params)
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._head_fn = factory.retinanet_head_generator(params.retinanet_head)
# Loss function.
self._cls_loss_fn = losses.RetinanetClassLoss(params.retinanet_loss)
self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss)
self._box_loss_weight = params.retinanet_loss.box_loss_weight
# Predict function.
self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator(
params.postprocess)
self._transpose_input = params.train.transpose_input
def __init__(self, params):
super(RetinanetModel, self).__init__(params)
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._head_fn = factory.retinanet_head_generator(params.retinanet_head)
# Loss function.
self._cls_loss_fn = losses.RetinanetClassLoss(params.retinanet_loss)
self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss)
self._box_loss_weight = params.retinanet_loss.box_loss_weight
# Predict function.
self._generate_detections_fn = postprocess.GenerateOneStageDetections(
params.postprocess)
self._l2_weight_decay = params.architecture.l2_weight_decay
def __init__(self, params):
super(RetinanetModel, self).__init__(params)
self._params = params
# Architecture generators.
self._backbone_fn = factory.backbone_generator(params)
self._fpn_fn = factory.multilevel_features_generator(params)
self._head_fn = factory.retinanet_head_generator(params)
# Loss function.
self._cls_loss_fn = losses.RetinanetClassLoss(
params.retinanet_loss, params.architecture.num_classes)
self._box_loss_fn = losses.RetinanetBoxLoss(params.retinanet_loss)
self._box_loss_weight = params.retinanet_loss.box_loss_weight
self._focal_loss_normalizer_momentum = (
params.retinanet_loss.normalizer_momentum)
# Predict function.
self._generate_detections_fn = postprocess_ops.MultilevelDetectionGenerator(
params.architecture.min_level, params.architecture.max_level,
params.postprocess)
