apps/benchmark/mobile_gpu_imagenet_bench.py

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"""Benchmark script for ImageNet models on mobile GPU.
see README.md for the usage and results of this script.
"""
import argparse

import numpy as np

import tvm
from tvm import te
from tvm.contrib.util import tempdir
import tvm.contrib.graph_runtime as runtime
from tvm import relay

from util import get_network, print_progress


def evaluate_network(network, target, target_host, dtype, repeat):
    # connect to remote device
    tracker = tvm.rpc.connect_tracker(args.host, args.port)
    remote = tracker.request(args.rpc_key)

    print_progress(network)
    net, params, input_shape, output_shape = get_network(network, batch_size=1, dtype=dtype)

    print_progress("%-20s building..." % network)
    with tvm.transform.PassContext(opt_level=3):
        lib = relay.build(net, target=target, target_host=target_host, params=params)

    tmp = tempdir()
    if "android" in str(target) or "android" in str(target_host):
        from tvm.contrib import ndk

        filename = "%s.so" % network
        lib.export_library(tmp.relpath(filename), ndk.create_shared)
    else:
        filename = "%s.tar" % network
        lib.export_library(tmp.relpath(filename))

    # upload library and params
    print_progress("%-20s uploading..." % network)
    ctx = remote.context(str(target), 0)
    remote.upload(tmp.relpath(filename))

    rlib = remote.load_module(filename)
    module = runtime.GraphModule(rlib["default"](ctx))
    data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
    module.set_input("data", data_tvm)

    # evaluate
    print_progress("%-20s evaluating..." % network)
    ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=repeat)
    prof_res = np.array(ftimer().results) * 1000  # multiply 1000 for converting to millisecond
    print(
        "%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--network",
        type=str,
        choices=[
            "resnet-18",
            "resnet-34",
            "resnet-50",
            "vgg-16",
            "vgg-19",
            "densenet-121",
            "inception_v3",
            "mobilenet",
            "squeezenet_v1.0",
            "squeezenet_v1.1",
        ],
        help="The name of neural network",
    )
    parser.add_argument(
        "--model",
        type=str,
        choices=["rk3399"],
        default="rk3399",
        help="The model of the test device. If your device is not listed in "
        "the choices list, pick the most similar one as argument.",
    )
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--port", type=int, default=9190)
    parser.add_argument("--rpc-key", type=str, required=True)
    parser.add_argument("--repeat", type=int, default=30)
    parser.add_argument("--dtype", type=str, default="float32")
    args = parser.parse_args()

    if args.network is None:
        networks = ["squeezenet_v1.1", "mobilenet", "resnet-18", "vgg-16"]
    else:
        networks = [args.network]

    target = tvm.target.mali(model=args.model)
    target_host = tvm.target.arm_cpu(model=args.model)

    print("--------------------------------------------------")
    print("%-20s %-20s" % ("Network Name", "Mean Inference Time (std dev)"))
    print("--------------------------------------------------")

    for network in networks:
        evaluate_network(network, target, target_host, args.dtype, args.repeat)