def test_deeplabv3_builder(self, backbone_type, input_size, weight_decay):
num_classes = 21
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model_config = semantic_segmentation_cfg.SemanticSegmentationModel(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type,
mobilenet=backbones.MobileNet(
model_id='MobileNetV2',
output_stride=16)),
decoder=decoders.Decoder(type='aspp',
aspp=decoders.ASPP(level=4,
num_filters=256,
dilation_rates=[],
spp_layer_version='v1',
output_tensor=True)),
head=semantic_segmentation_cfg.SegmentationHead(
level=4,
low_level=2,
num_convs=1,
upsample_factor=2,
use_depthwise_convolution=True))
l2_regularizer = (tf.keras.regularizers.l2(weight_decay)
if weight_decay else None)
model = factory.build_segmentation_model(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
quantization_config = common.Quantization()
_ = qat_factory.build_qat_segmentation_model(
model=model,
quantization=quantization_config,
input_specs=input_specs)
def build_model(self) -> tf.keras.Model:
"""Builds semantic segmentation model with QAT."""
model = super().build_model()
input_specs = tf.keras.layers.InputSpec(shape=[None] +
self.task_config.model.input_size)
if self.task_config.quantization:
model = factory.build_qat_segmentation_model(
model, self.task_config.quantization, input_specs)
return model
def _build_model(self):
model = super()._build_model()
input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
self._input_image_size + [3])
return qat_factory.build_qat_segmentation_model(
model, self.params.task.quantization, input_specs)