benchmark conv layers to run benchmarks see the following command for an example please change the flag to your custom value python3 m benchmarks layerbenchmark convbenchmark benchmarknamebenchmarkconv2d numsamples2046 batchsize256 jitcompiletrue this code provides a framework for benchmarking various convolutional layers in neural networks using keras and tensorflow it defines benchmark functions for different types of convolutional layers including conv1d conv2d conv3d depthwise conv1d depthwise conv2d separable conv1d separable conv2d conv1d transpose conv2d transpose and conv3d transpose each benchmark function initializes a layerbenchmark object with specific layer parameters such as filters and kernel size and input shape it then performs prediction and training benchmarks for the specified number of samples and batch size the main function uses the absl library to parse command line arguments for selecting a specific benchmark or running all benchmarks if none is specified it also handles invalid benchmark name errors the benchmarks are stored in a dictionary for easy access and execution
from absl import app
from absl import flags

from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark

FLAGS = flags.FLAGS


def benchmark_conv1D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv1D"
    init_args = {
        "filters": 64,
        "kernel_size": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[1024, 256],
        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_conv2D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv2D"
    init_args = {
        "filters": 16,
        "kernel_size": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[128, 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_conv3D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv3D"
    init_args = {
        "filters": 16,
        "kernel_size": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[32, 32, 32, 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_depthwise_conv1D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "DepthwiseConv1D"
    init_args = {
        "kernel_size": 16,
        "depth_multiplier": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 64],
        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_depthwise_conv2D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "DepthwiseConv2D"
    init_args = {
        "kernel_size": 16,
        "depth_multiplier": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[128, 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_separable_conv1D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "SeparableConv1D"
    init_args = {
        "kernel_size": 16,
        "depth_multiplier": 2,
        "filters": 3,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 64],
        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_separable_conv2D(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "SeparableConv2D"
    init_args = {
        "kernel_size": 16,
        "depth_multiplier": 2,
        "filters": 3,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[128, 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_conv1D_transpose(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv1DTranspose"
    init_args = {
        "filters": 32,
        "kernel_size": 4,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[256, 256],
        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_conv2D_transpose(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv2DTranspose"
    init_args = {
        "filters": 16,
        "kernel_size": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[128, 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_conv3D_transpose(
    num_samples,
    batch_size,
    jit_compile=True,
):
    layer_name = "Conv3DTranspose"
    init_args = {
        "filters": 16,
        "kernel_size": 2,
    }
    benchmark = LayerBenchmark(
        layer_name,
        init_args,
        input_shape=[32, 32, 32, 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_conv1D": benchmark_conv1D,
    "benchmark_conv2D": benchmark_conv2D,
    "benchmark_conv3D": benchmark_conv3D,
    "benchmark_depthwise_conv1D": benchmark_depthwise_conv1D,
    "benchmark_depthwise_conv2D": benchmark_depthwise_conv2D,
    "benchmark_separable_conv1D": benchmark_separable_conv1D,
    "benchmark_separable_conv2D": benchmark_separable_conv2D,
    "benchmark_conv1D_transpose": benchmark_conv1D_transpose,
    "benchmark_conv2D_transpose": benchmark_conv2D_transpose,
    "benchmark_conv3D_transpose": benchmark_conv3D_transpose,
}


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:
            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)