def test_builder(self, backbone_type, input_size):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model_config = maskrcnn_cfg.MaskRCNN(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type))
l2_regularizer = tf.keras.regularizers.l2(5e-5)
_ = factory.build_maskrcnn(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
def build_model(self):
"""Build Mask R-CNN model."""
input_specs = tf.keras.layers.InputSpec(
shape=[None] + self.task_config.model.input_size)
l2_weight_decay = self.task_config.losses.l2_weight_decay
# Divide weight decay by 2.0 to match the implementation of tf.nn.l2_loss.
# (https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l2)
# (https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss)
l2_regularizer = (tf.keras.regularizers.l2(l2_weight_decay / 2.0)
if l2_weight_decay else None)
model = factory.build_maskrcnn(input_specs=input_specs,
model_config=self.task_config.model,
l2_regularizer=l2_regularizer)
return model
def _build_model(self):
if self._batch_size is None:
raise ValueError('batch_size cannot be None for detection models.')
input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
self._input_image_size + [3])
if isinstance(self.params.task.model, configs.maskrcnn.MaskRCNN):
model = factory.build_maskrcnn(
input_specs=input_specs, model_config=self.params.task.model)
elif isinstance(self.params.task.model, configs.retinanet.RetinaNet):
model = factory.build_retinanet(
input_specs=input_specs, model_config=self.params.task.model)
else:
raise ValueError('Detection module not implemented for {} model.'.format(
type(self.params.task.model)))
return model
def _build_model(self):
if self._batch_size is None:
# Only batched NMS is supported with dynamic batch size.
self.params.task.model.detection_generator.nms_version = 'batched'
logging.info(
'nms_version is set to `batched` because only batched NMS is '
'supported with dynamic batch size.')
input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
self._input_image_size + [3])
if isinstance(self.params.task.model, configs.maskrcnn.MaskRCNN):
model = factory.build_maskrcnn(input_specs=input_specs,
model_config=self.params.task.model)
elif isinstance(self.params.task.model, configs.retinanet.RetinaNet):
model = factory.build_retinanet(
input_specs=input_specs, model_config=self.params.task.model)
else:
raise ValueError(
'Detection module not implemented for {} model.'.format(
type(self.params.task.model)))
return model