def _test_run_benchmark(self, params):
"""Tests that run_benchmark() runs successfully with the params."""
logs = []
with test_util.monkey_patch(all_reduce_benchmark,
log_fn=test_util.print_and_add_to_list(logs)):
bench_cnn = benchmark_cnn.BenchmarkCNN(params)
all_reduce_benchmark.run_benchmark(bench_cnn, num_iters=5)
self.assertRegex(logs[-1], '^Average time per step: [0-9.]+$')
def _run_benchmark_cnn_with_fake_images(self, params, images, labels):
logs = []
benchmark_cnn.log_fn = test_util.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
def _get_benchmark_cnn_losses(self, inputs, params):
"""Returns the losses of BenchmarkCNN on the given inputs and params."""
logs = []
model = test_util.TestCNNModel()
with test_util.monkey_patch(benchmark_cnn,
log_fn=test_util.print_and_add_to_list(logs),
LOSS_AND_ACCURACY_DIGITS_TO_SHOW=15):
bench = benchmark_cnn.BenchmarkCNN(
params, dataset=test_util.TestDataSet(), model=model)
# The test model does not use labels when computing loss, so the label
# values do not matter as long as it's the right shape.
labels = np.array([1] * inputs.shape[0])
bench.image_preprocessor.set_fake_data(inputs, labels)
bench.run()
outputs = test_util.get_training_outputs_from_logs(
logs, params.print_training_accuracy)
return [x.loss for x in outputs]
def _run_benchmark_cnn(self, params): logs = [] benchmark_cnn.log_fn = test_util.print_and_add_to_list(logs) benchmark_cnn.BenchmarkCNN(params).run() return logs