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

import numpy as np

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

from util import get_network


def benchmark(network, target):
    net, params, input_shape, output_shape = get_network(network, batch_size=1)

    with tvm.transform.PassContext(opt_level=3):
        lib = relay.build(net, target=target, params=params)

    # create runtime
    ctx = tvm.context(str(target), 0)
    module = runtime.GraphModule(lib["default"](ctx))
    data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
    module.set_input("data", data_tvm)

    # evaluate
    ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=args.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(
        "--device",
        type=str,
        choices=["amd_apu"],
        default="amd_apu",
        help="The name of the test device. If your device is not listed in "
        "the choices list, pick the most similar one as argument.",
    )
    parser.add_argument(
        "--model",
        type=str,
        choices=["1080ti", "titanx", "tx2", "gfx900", "v1000"],
        default="1080ti",
        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("--repeat", type=int, default=600)
    parser.add_argument(
        "--target",
        type=str,
        choices=["cuda", "opencl", "rocm", "nvptx", "metal", "vulkan"],
        default="cuda",
        help="The tvm compilation target",
    )
    parser.add_argument("--thread", type=int, default=1, help="The number of threads to be run.")
    args = parser.parse_args()

    dtype = "float32"

    if args.network is None:
        networks = ["resnet-50", "mobilenet", "vgg-19", "inception_v3"]
    else:
        networks = [args.network]

    target = tvm.target.Target("%s -device=%s -model=%s" % (args.target, args.device, args.model))

    print("--------------------------------------------------")
    print("%-20s %-20s" % ("Network Name", "Mean Inference Time (std dev)"))
    print("--------------------------------------------------")
    for network in networks:
        if args.thread == 1:
            benchmark(network, target)
        else:
            threads = list()
            for n in range(args.thread):
                thread = threading.Thread(
                    target=benchmark, args=([network, target]), name="thread%d" % n
                )
                threads.append(thread)

            for thread in threads:
                thread.start()

            for thread in threads:
                thread.join()