def build_inputs(self, params, input_context=None):
"""Builds classification input."""
input_size = self.task_config.model.input_size
ignore_label = self.task_config.losses.ignore_label
decoder = segmentation_input.Decoder()
parser = segmentation_input.Parser(
output_size=input_size[:2],
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,
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 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 build_inputs(self, params, input_context=None):
"""Builds classification input."""
ignore_label = self.task_config.losses.ignore_label
if params.tfds_name:
if params.tfds_name in tfds_segmentation_decoders.TFDS_ID_TO_DECODER_MAP:
decoder = tfds_segmentation_decoders.TFDS_ID_TO_DECODER_MAP[
params.tfds_name]()
else:
raise ValueError('TFDS {} is not supported'.format(
params.tfds_name))
else:
decoder = segmentation_input.Decoder()
parser = segmentation_input.Parser(
output_size=params.output_size,
train_on_crops=params.train_on_crops,
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)
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, input_context=None):
"""Builds classification input."""
ignore_label = self.task_config.losses.ignore_label
decoder = segmentation_input.Decoder()
parser = segmentation_input.Parser(
output_size=params.output_size,
train_on_crops=params.train_on_crops,
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)
reader = input_reader.InputReader(
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):
"""Builds classification input."""
ignore_label = self.task_config.losses.ignore_label
if params.tfds_name:
if params.tfds_name in tfds_segmentation_decoders.TFDS_ID_TO_DECODER_MAP:
decoder = tfds_segmentation_decoders.TFDS_ID_TO_DECODER_MAP[
params.tfds_name]()
else:
raise ValueError('TFDS {} is not supported'.format(
params.tfds_name))
else:
decoder = segmentation_input.Decoder()
parser = segmentation_input.Parser(
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,
aug_policy=params.aug_policy,
randaug_magnitude=params.randaug_magnitude,
randaug_available_ops=params.randaug_available_ops,
preserve_aspect_ratio=params.preserve_aspect_ratio,
rotate_min=params.rotate_min,
rotate_max=params.rotate_max,
bright_min=params.bright_min,
bright_max=params.bright_max,
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
def build_inputs(self,
params: exp_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None):
"""Builds BASNet input."""
ignore_label = self.task_config.losses.ignore_label
decoder = segmentation_input.Decoder()
parser = segmentation_input.Parser(
output_size=params.output_size,
crop_size=params.crop_size,
ignore_label=ignore_label,
aug_rand_hflip=params.aug_rand_hflip,
dtype=params.dtype)
reader = input_reader.InputReader(
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
