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.assertRegexpMatches(logs[-1],
'^Average time per step: [0-9.]+$')
def run_with_test_model(params):
"""Runs tf_cnn_benchmarks with a test model."""
model = test_util.TestCNNModel()
inputs = test_util.get_fake_var_update_inputs()
with test_util.monkey_patch(benchmark_cnn,
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.input_preprocessor.set_fake_data(inputs, labels)
bench.run()