def build_inputs(self,
params: cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None):
"""Builds classification input."""
ignore_label = self.task_config.losses.ignore_label
if params.tfds_name:
decoder = tfds_factory.get_segmentation_decoder(params.tfds_name)
else:
decoder = segmentation_input.Decoder()
parser = ClassMappingParser(
output_size=params.output_size,
crop_size=params.crop_size,
ignore_label=ignore_label,
resize_eval_groundtruth=params.resize_eval_groundtruth,
groundtruth_padded_size=params.groundtruth_padded_size,
aug_scale_min=params.aug_scale_min,
aug_scale_max=params.aug_scale_max,
aug_rand_hflip=params.aug_rand_hflip,
dtype=params.dtype)
parser.max_class = self.task_config.model.num_classes-1
reader = input_reader_factory.input_reader_generator(
params,
dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def test_segmentation_decoder(self, tfds_name):
decoder = tfds_factory.get_segmentation_decoder(tfds_name)
self.assertIsInstance(decoder, base_decoder.Decoder)
decoded_tensor = decoder.decode(self._create_test_example())
self.assertLen(decoded_tensor, 4)
self.assertIn('image/encoded', decoded_tensor)
self.assertIn('image/segmentation/class/encoded', decoded_tensor)
self.assertIn('image/height', decoded_tensor)
self.assertIn('image/width', decoded_tensor)
def test_doesnt_exit_segmentation_decoder(self, tfds_name):
with self.assertRaises(ValueError):
_ = tfds_factory.get_segmentation_decoder(tfds_name)