def get_test_image_preprocessor(batch_size, params):
"""Returns the preprocessing.TestImagePreprocessor that should be injected.
Returns None if no preprocessor should be injected.
Args:
batch_size: The batch size across all GPUs.
params: BenchmarkCNN's parameters.
Returns:
Returns the preprocessing.TestImagePreprocessor that should be injected.
Raises:
ValueError: Flag --fake_input is an invalid value.
"""
if FLAGS.fake_input == 'none':
return None
elif FLAGS.fake_input == 'zeros_and_ones':
half_batch_size = batch_size // 2
images = np.zeros((batch_size, 227, 227, 3), dtype=np.float32)
images[half_batch_size:, :, :, :] = 1
labels = np.array([0] * half_batch_size + [1] * half_batch_size,
dtype=np.int32)
preprocessor = preprocessing.TestImagePreprocessor(
227, 227, batch_size, params.num_gpus,
benchmark_cnn.get_data_type(params))
preprocessor.set_fake_data(images, labels)
preprocessor.expected_subset = 'validation' if params.eval else 'train'
return preprocessor
else:
raise ValueError('Invalid --fake_input: %s' % FLAGS.fake_input)
def _run_benchmark_cnn_with_fake_images(self, params, images, labels):
logs = []
benchmark_cnn.log_fn = _print_and_add_to_list(logs)
bench = benchmark_cnn.BenchmarkCNN(params)
bench.image_preprocessor = preprocessing.TestImagePreprocessor(
227, 227, params.batch_size * params.num_gpus, params.num_gpus,
benchmark_cnn.get_data_type(params))
bench.dataset._queue_runner_required = True
bench.image_preprocessor.set_fake_data(images, labels)
bench.image_preprocessor.expected_subset = ('validation' if params.eval
else 'train')
bench.run()
return logs
