def sample(logdir, subset):
"""Executes the sampling loop."""
logging.info('Beginning sampling loop...')
config = FLAGS.config
batch_size = config.sample.get('batch_size', 1)
# used to parallelize sampling jobs.
skip_batches = config.sample.get('skip_batches', 0)
gen_data_dir = config.sample.get('gen_data_dir', None)
is_gen = gen_data_dir is not None
model_name = config.model.get('name')
if not is_gen and 'upsampler' in model_name:
logging.info('Generated low resolution not provided, using ground '
'truth input.')
# Get ground truth dataset for grayscale image.
tf_dataset = datasets.get_dataset(name=config.dataset,
config=config,
batch_size=batch_size,
subset=subset)
tf_dataset = tf_dataset.skip(skip_batches)
data_iter = iter(tf_dataset)
# Creates dataset from generated TFRecords.
# This is used as low resolution input to the upsamplers.
gen_iter = None
if is_gen:
gen_tf_dataset = datasets.get_gen_dataset(data_dir=gen_data_dir,
batch_size=batch_size)
gen_tf_dataset = gen_tf_dataset.skip(skip_batches)
gen_iter = iter(gen_tf_dataset)
store_samples(data_iter, config, logdir, gen_iter)
def input_fn(input_context=None):
read_config = None
if input_context is not None:
read_config = tfds.ReadConfig(input_context=input_context)
dataset = datasets.get_dataset(name=config.dataset,
config=config,
batch_size=config.batch_size,
subset='train',
read_config=read_config)
return dataset
def input_fn(_=None):
return datasets.get_dataset(name=config.dataset,
config=config,
batch_size=config.eval_batch_size,
subset=subset)
def input_fn(_=None):
dataset = datasets.get_dataset(name=config.dataset,
config=config,
batch_size=config.batch_size,
subset='train')
return dataset