def test_detection_decoder(self, tfds_name):
decoder = tfds_factory.get_detection_decoder(tfds_name)
self.assertIsInstance(decoder, base_decoder.Decoder)
decoded_tensor = decoder.decode(self._create_test_example())
self.assertLen(decoded_tensor, 8)
self.assertIn('image', decoded_tensor)
self.assertIn('source_id', decoded_tensor)
self.assertIn('height', decoded_tensor)
self.assertIn('width', decoded_tensor)
self.assertIn('groundtruth_classes', decoded_tensor)
self.assertIn('groundtruth_is_crowd', decoded_tensor)
self.assertIn('groundtruth_area', decoded_tensor)
self.assertIn('groundtruth_boxes', decoded_tensor)
def build_inputs(
self,
params: exp_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None):
"""Build input dataset."""
if params.tfds_name:
decoder = tfds_factory.get_detection_decoder(params.tfds_name)
else:
decoder_cfg = params.decoder.get()
if params.decoder.type == 'simple_decoder':
decoder = tf_example_decoder.TfExampleDecoder(
regenerate_source_id=decoder_cfg.regenerate_source_id)
elif params.decoder.type == 'label_map_decoder':
decoder = tf_example_label_map_decoder.TfExampleDecoderLabelMap(
label_map=decoder_cfg.label_map,
regenerate_source_id=decoder_cfg.regenerate_source_id)
else:
raise ValueError('Unknown decoder type: {}!'.format(
params.decoder.type))
parser = retinanet_input.Parser(
output_size=self.task_config.model.input_size[:2],
min_level=self.task_config.model.min_level,
max_level=self.task_config.model.max_level,
num_scales=self.task_config.model.anchor.num_scales,
aspect_ratios=self.task_config.model.anchor.aspect_ratios,
anchor_size=self.task_config.model.anchor.anchor_size,
dtype=params.dtype,
match_threshold=params.parser.match_threshold,
unmatched_threshold=params.parser.unmatched_threshold,
aug_type=params.parser.aug_type,
aug_rand_hflip=params.parser.aug_rand_hflip,
aug_scale_min=params.parser.aug_scale_min,
aug_scale_max=params.parser.aug_scale_max,
skip_crowd_during_training=params.parser.
skip_crowd_during_training,
max_num_instances=params.parser.max_num_instances)
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 build_inputs(
self,
params: exp_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None):
"""Build input dataset."""
if params.tfds_name:
decoder = tfds_factory.get_detection_decoder(params.tfds_name)
else:
decoder_cfg = params.decoder.get()
if params.decoder.type == 'simple_decoder':
decoder = tf_example_decoder.TfExampleDecoder(
regenerate_source_id=decoder_cfg.regenerate_source_id)
elif params.decoder.type == 'label_map_decoder':
decoder = tf_example_label_map_decoder.TfExampleDecoderLabelMap(
label_map=decoder_cfg.label_map,
regenerate_source_id=decoder_cfg.regenerate_source_id)
else:
raise ValueError('Unknown decoder type: {}!'.format(
params.decoder.type))
parser = centernet_input.CenterNetParser(
output_height=self.task_config.model.input_size[0],
output_width=self.task_config.model.input_size[1],
max_num_instances=self.task_config.model.max_num_instances,
bgr_ordering=params.parser.bgr_ordering,
channel_means=params.parser.channel_means,
channel_stds=params.parser.channel_stds,
aug_rand_hflip=params.parser.aug_rand_hflip,
aug_scale_min=params.parser.aug_scale_min,
aug_scale_max=params.parser.aug_scale_max,
aug_rand_hue=params.parser.aug_rand_hue,
aug_rand_brightness=params.parser.aug_rand_brightness,
aug_rand_contrast=params.parser.aug_rand_contrast,
aug_rand_saturation=params.parser.aug_rand_saturation,
odapi_augmentation=params.parser.odapi_augmentation,
dtype=params.dtype)
reader = input_reader.InputReader(params,
dataset_fn=tf.data.TFRecordDataset,
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(
params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def _get_data_decoder(self, params):
"""Get a decoder object to decode the dataset."""
if params.tfds_name:
decoder = tfds_factory.get_detection_decoder(params.tfds_name)
else:
decoder_cfg = params.decoder.get()
if params.decoder.type == 'simple_decoder':
self._coco_91_to_80 = decoder_cfg.coco91_to_80
decoder = tf_example_decoder.TfExampleDecoder(
coco91_to_80=decoder_cfg.coco91_to_80,
regenerate_source_id=decoder_cfg.regenerate_source_id)
elif params.decoder.type == 'label_map_decoder':
decoder = tf_example_label_map_decoder.TfExampleDecoderLabelMap(
label_map=decoder_cfg.label_map,
regenerate_source_id=decoder_cfg.regenerate_source_id)
else:
raise ValueError('Unknown decoder type: {}!'.format(
params.decoder.type))
return decoder
def test_doesnt_exit_detection_decoder(self, tfds_name):
with self.assertRaises(ValueError):
_ = tfds_factory.get_detection_decoder(tfds_name)
