def imagenet(mode, params):
"""An input_fn for ImageNet (ILSVRC 2012) data."""
model_mode = utils.estimator_mode_to_model_mode(mode)
hparams = params['hparams']
is_training = model_mode == enums.ModelMode.TRAIN
preprocessor = preprocessing.ImageToMultiViewedImagePreprocessor(
is_training=is_training,
preprocessing_options=hparams.input_data.preprocessing,
dataset_options=preprocessing.DatasetOptions(decode_input=False),
bfloat16_supported=params['use_tpu'])
imagenet_input = TfdsInput(
dataset_name='imagenet2012:5.*.*',
split='train' if is_training else 'validation',
mode=model_mode,
preprocessor=preprocessor,
shuffle_buffer=1024,
shard_per_host=hparams.input_data.shard_per_host,
cache=is_training,
num_parallel_calls=64,
max_samples=hparams.input_data.max_samples,
label_noise_prob=hparams.input_data.label_noise_prob,
num_classes=get_num_classes(hparams),
data_dir=params['data_dir'],
)
return imagenet_input.input_fn(params)
def cifar10(mode, params):
"""CIFAR10 dataset creator."""
# Images are naturally 32x32.
model_mode = utils.estimator_mode_to_model_mode(mode)
hparams = params['hparams']
is_training = model_mode == enums.ModelMode.TRAIN
preprocessor = preprocessing.ImageToMultiViewedImagePreprocessor(
is_training=is_training,
preprocessing_options=hparams.input_data.preprocessing,
dataset_options=preprocessing.DatasetOptions(
decode_input=False,
image_mean_std=(np.array([[[-0.0172, -0.0356, -0.107]]]),
np.array([[[0.4046, 0.3988, 0.402]]]))),
bfloat16_supported=params['use_tpu'])
cifar_input = TfdsInput(
dataset_name='cifar10:3.*.*',
split='train' if is_training else 'test',
mode=model_mode,
preprocessor=preprocessor,
shard_per_host=hparams.input_data.shard_per_host,
cache=is_training,
shuffle_buffer=50000,
num_parallel_calls=64,
max_samples=hparams.input_data.max_samples,
label_noise_prob=hparams.input_data.label_noise_prob,
num_classes=get_num_classes(hparams),
data_dir=params['data_dir'],
)
return cifar_input.input_fn(params)
def test_image_to_multi_viewed_image_preprocessor(self, is_training,
decode_image, num_views):
# tf.random.uniform() doesn't allow generating random values that are uint8.
image = tf.cast(
tf.random.uniform(
shape=(300, 400, 3), minval=0, maxval=255, dtype=tf.float32),
dtype=tf.uint8)
if decode_image:
image = tf.image.encode_jpeg(image)
image_size = 32
preprocessor = preprocessing.ImageToMultiViewedImagePreprocessor(
is_training=is_training,
preprocessing_options=hparams.ImagePreprocessing(
image_size=image_size, num_views=num_views),
dataset_options=preprocessing.DatasetOptions(decode_input=decode_image))
output = preprocessor.preprocess(image)
self.assertEqual(output.dtype, tf.float32)
self.assertEqual([image_size, image_size, 3 * num_views],
output.shape.as_list())