def testSegmentationInputReader(self, input_size, num_classes, num_channels):
params = cfg.DataConfig(
input_path=self._data_path, global_batch_size=2, is_training=False)
decoder = segmentation_input_3d.Decoder()
parser = segmentation_input_3d.Parser(
input_size=input_size,
num_classes=num_classes,
num_channels=num_channels)
reader = input_reader.InputReader(
params,
dataset_fn=dataset_fn.pick_dataset_fn('tfrecord'),
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read()
iterator = iter(dataset)
image, labels = next(iterator)
# Checks image shape.
self.assertEqual(
list(image.numpy().shape),
[2, input_size[0], input_size[1], input_size[2], num_channels])
self.assertEqual(
list(labels.numpy().shape),
[2, input_size[0], input_size[1], input_size[2], num_classes])
def testSegmentationInputReader(self, input_size, num_classes,
num_channels, is_training):
decoder = segmentation_input_3d.Decoder()
parser = segmentation_input_3d.Parser(input_size=input_size,
num_classes=num_classes,
num_channels=num_channels)
decoded_tensor = decoder.decode(self._example.SerializeToString())
image, labels = parser.parse_fn(
is_training=is_training)(decoded_tensor)
# Checks image shape.
self.assertEqual(
list(image.numpy().shape),
[input_size[0], input_size[1], input_size[2], num_channels])
self.assertEqual(
list(labels.numpy().shape),
[input_size[0], input_size[1], input_size[2], num_classes])
def build_inputs(self, params, input_context=None) -> tf.data.Dataset:
"""Builds classification input."""
decoder = segmentation_input_3d.Decoder(
image_field_key=params.image_field_key,
label_field_key=params.label_field_key)
parser = segmentation_input_3d.Parser(
input_size=params.input_size,
num_classes=params.num_classes,
num_channels=params.num_channels,
image_field_key=params.image_field_key,
label_field_key=params.label_field_key,
dtype=params.dtype,
label_dtype=params.label_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
