def test_decoder(self, image_height, image_width, num_instances):
decoder = classification_input.Decoder(image_field_key=IMAGE_FIELD_KEY,
label_field_key=LABEL_FIELD_KEY)
serialized_example = tfexample_utils.create_classification_example(
image_height, image_width)
decoded_tensors = decoder.decode(
tf.convert_to_tensor(serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertCountEqual([IMAGE_FIELD_KEY, LABEL_FIELD_KEY],
results.keys())
self.assertEqual(0, results[LABEL_FIELD_KEY])
def test_parser(self, output_size, dtype, is_training, aug_name,
is_multilabel, decode_jpeg_only, image_format):
serialized_example = tfexample_utils.create_classification_example(
output_size[0], output_size[1], image_format, is_multilabel)
if aug_name == 'randaug':
aug_type = common.Augmentation(
type=aug_name, randaug=common.RandAugment(magnitude=10))
elif aug_name == 'autoaug':
aug_type = common.Augmentation(
type=aug_name,
autoaug=common.AutoAugment(augmentation_name='test'))
else:
aug_type = None
decoder = classification_input.Decoder(image_field_key=IMAGE_FIELD_KEY,
label_field_key=LABEL_FIELD_KEY,
is_multilabel=is_multilabel)
parser = classification_input.Parser(output_size=output_size[:2],
num_classes=10,
image_field_key=IMAGE_FIELD_KEY,
label_field_key=LABEL_FIELD_KEY,
is_multilabel=is_multilabel,
decode_jpeg_only=decode_jpeg_only,
aug_rand_hflip=False,
aug_type=aug_type,
dtype=dtype)
decoded_tensors = decoder.decode(serialized_example)
image, label = parser.parse_fn(is_training)(decoded_tensors)
self.assertAllEqual(image.numpy().shape, output_size)
if not is_multilabel:
self.assertAllEqual(label, 0)
else:
self.assertAllEqual(label.numpy().shape, [10])
if dtype == 'float32':
self.assertAllEqual(image.dtype, tf.float32)
elif dtype == 'float16':
self.assertAllEqual(image.dtype, tf.float16)
elif dtype == 'bfloat16':
self.assertAllEqual(image.dtype, tf.bfloat16)
def build_inputs(
self,
params: base_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Builds classification input."""
num_classes = self.task_config.model.num_classes
input_size = self.task_config.model.input_size
image_field_key = self.task_config.train_data.image_field_key
label_field_key = self.task_config.train_data.label_field_key
is_multilabel = self.task_config.train_data.is_multilabel
if params.tfds_name:
if params.tfds_name in tfds_classification_decoders.TFDS_ID_TO_DECODER_MAP:
decoder = tfds_classification_decoders.TFDS_ID_TO_DECODER_MAP[
params.tfds_name]()
else:
raise ValueError('TFDS {} is not supported'.format(params.tfds_name))
else:
decoder = classification_input.Decoder(
image_field_key=image_field_key, label_field_key=label_field_key,
is_multilabel=is_multilabel)
parser = classification_input.Parser(
output_size=input_size[:2],
num_classes=num_classes,
image_field_key=image_field_key,
label_field_key=label_field_key,
decode_jpeg_only=params.decode_jpeg_only,
aug_rand_hflip=params.aug_rand_hflip,
aug_type=params.aug_type,
is_multilabel=is_multilabel,
dtype=params.dtype)
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