Feed .nii imaging data via a native python generator

feed_from_generator.py

# Generator function
def f():
    fn = read_fn(file_references=all_filenames,
                 mode=tf.estimator.ModeKeys.TRAIN, 
                 params=reader_params)
    
    ex = next(fn)
    # Yield the next image
    yield ex
    
# Timed example with generator io
dataset = tf.data.Dataset.from_generator(
    f, reader_example_dtypes, reader_example_shapes)
dataset = dataset.repeat(None)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1)

iterator = dataset.make_initializable_iterator()
next_dict = iterator.get_next()

with tf.train.MonitoredTrainingSession() as sess_gen:
    # Initialize generator
    sess_gen.run(iterator.initializer)

    with Timer('Generator'):
        for i in range(iterations):
            # Fetch the next batch of images
            gen_batch_feat, gen_batch_lbl = sess_gen.run([next_dict['features'], next_dict['labels']])