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, input_context=None):
"""Build input dataset."""
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))
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_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_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.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, input_context=None):
"""Builds classification input."""
num_classes = self.task_config.model.num_classes
input_size = self.task_config.model.input_size
decoder = classification_input.Decoder()
parser = classification_input.Parser(output_size=input_size[:2],
num_classes=num_classes,
aug_policy=params.aug_policy,
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