def test_parser(self, output_size, dtype, is_training):
params = cfg.DataConfig(input_path='imagenet-2012-tfrecord/train*',
global_batch_size=2,
is_training=True,
examples_consume=4)
decoder = classification_input.Decoder()
parser = classification_input.Parser(output_size=output_size[:2],
num_classes=1001,
aug_rand_hflip=False,
dtype=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()
images, labels = next(iter(dataset))
self.assertAllEqual(images.numpy().shape,
[params.global_batch_size] + output_size)
self.assertAllEqual(labels.numpy().shape, [params.global_batch_size])
if dtype == 'float32':
self.assertAllEqual(images.dtype, tf.float32)
elif dtype == 'float16':
self.assertAllEqual(images.dtype, tf.float16)
elif dtype == 'bfloat16':
self.assertAllEqual(images.dtype, tf.bfloat16)
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
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()
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
def build_inputs(
self,
params: exp_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:
decoder = tfds_factory.get_classification_decoder(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,
color_jitter=params.color_jitter,
random_erasing=params.random_erasing,
is_multilabel=is_multilabel,
dtype=params.dtype)
postprocess_fn = None
if params.mixup_and_cutmix:
postprocess_fn = augment.MixupAndCutmix(
mixup_alpha=params.mixup_and_cutmix.mixup_alpha,
cutmix_alpha=params.mixup_and_cutmix.cutmix_alpha,
prob=params.mixup_and_cutmix.prob,
label_smoothing=params.mixup_and_cutmix.label_smoothing,
num_classes=num_classes)
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),
postprocess_fn=postprocess_fn)
dataset = reader.read(input_context=input_context)
return dataset
def build_inputs(
self,
params: exp_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,
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
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,
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 test_decoder(self, image_height, image_width, num_instances):
decoder = classification_input.Decoder()
image = _encode_image(np.uint8(
np.random.rand(image_height, image_width, 3) * 255),
fmt='JPEG')
label = 2
serialized_example = tf.train.Example(features=tf.train.Features(
feature={
'image/encoded': (tf.train.Feature(
bytes_list=tf.train.BytesList(value=[image]))),
'image/class/label': (tf.train.Feature(
int64_list=tf.train.Int64List(value=[label]))),
})).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertCountEqual(['image/encoded', 'image/class/label'],
results.keys())
self.assertEqual(label, results['image/class/label'])
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
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:
decoder = tfds_factory.get_classification_decoder(params.tfds_name)
else:
decoder = classification_input_base.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
