Function f

caffe2/python/benchmarks/sparse_normalize_benchmark.py:33–40

(_, outputs)

    # In order to produce truly random lengths and indices, we will embed a
    # Python operator in the net to generate them.
    def f(_, outputs):
        lengths = np.random.randint(
            int(average_len * 0.75), int(average_len * 1.25), batch_size
        ).astype(np.int32)
        indices = np.random.randint(0, categorical_limit, np.sum(lengths)).astype(
            np.int64
        )
        outputs[0].feed(indices)

    workspace.FeedBlob("X", data)
    workspace.FeedBlob("huge_blob", np.random.randn(l3_cache_size).astype(np.float32))