benchmark normalization layers to run benchmarks see the following command for an example please change the flag to your custom value python3 m benchmarks layerbenchmark normalizationbenchmark benchmarknamebenchmarkbatchnormalization numsamples2048 batchsize256 jitcompiletrue this code is a benchmarking tool for evaluating different normalization layers in neural networks using keras and tensorflow it includes benchmark functions for batch normalization group normalization layer normalization and unit normalization each function creates a layerbenchmark object with the specified layer name and initialization arguments if any and sets the input shape for the layer the benchmark predict and benchmark train methods are then used to measure the performance of each normalization layer in terms of prediction and training the main function uses the absl library to parse command line arguments allowing the user to specify the benchmark name number of samples batch size and whether to use jit compilation it runs the specified benchmark or all benchmarks if none is specified and handles cases where an invalid benchmark name is provided the benchmarks are organized in a dictionary for easy reference and execution
from absl import app
from absl import flags

from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark

FLAGS = flags.FLAGS


def benchmark_batch_normalization(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "BatchNormalization"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 256, 4],
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_group_normalization(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "GroupNormalization"
    init_args = {
        "groups": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 256, 4],
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_layer_normalization(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "LayerNormalization"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 128, 4],
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


def benchmark_unit_normalization(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "UnitNormalization"
    init_args = {}
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 128, 4],
        jit_compile=jit_compile,
    )

    benchmark.benchmark_predict(
        num_samples=num_samples,
        batch_size=batch_size,
    )

    benchmark.benchmark_train(
        num_samples=num_samples,
        batch_size=batch_size,
    )


BENCHMARK_NAMES = {
    "benchmark_batch_normalization": benchmark_batch_normalization,
    "benchmark_group_normalization": benchmark_group_normalization,
    "benchmark_layer_normalization": benchmark_layer_normalization,
    "benchmark_unit_normalization": benchmark_unit_normalization,
}


def main(_):
    benchmark_name = FLAGS.benchmark_name
    num_samples = FLAGS.num_samples
    batch_size = FLAGS.batch_size
    jit_compile = FLAGS.jit_compile

    if benchmark_name is None:
        for name, benchmark_fn in BENCHMARK_NAMES.items():
            benchmark_fn(num_samples, batch_size, jit_compile)
        return

    if benchmark_name not in BENCHMARK_NAMES:
        raise ValueError(
            f"Invalid benchmark name: {benchmark_name}, `benchmark_name` must "
            f"be one of {BENCHMARK_NAMES.keys()}"
        )
    benchmark_fn = BENCHMARK_NAMES[benchmark_name]
    benchmark_fn(num_samples, batch_size, jit_compile)


if __name__ == "__main__":
    app.run(main)