# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Benchmark script for ImageNet models on ARM CPU. 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, 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) print_progress("%-20s building..." % network) with relay.build_config(opt_level=3): graph, lib, params = relay.build( net, target=target, target_host=target_host, params=params) tmp = tempdir() if 'android' in str(target): 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.create(graph, rlib, ctx) data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype)) module.set_input('data', data_tvm) module.set_input(**params) # 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', 'mate10', 'mate10pro', 'p20', 'p20pro', 'pixel2', 'rasp3b', 'pynq'], 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=10) args = parser.parse_args() dtype = 'float32' if args.network is None: networks = ['squeezenet_v1.1', 'mobilenet', 'resnet-18', 'vgg-16'] else: networks = [args.network] target = tvm.target.arm_cpu(model=args.model) target_host = None print("--------------------------------------------------") print("%-20s %-20s" % ("Network Name", "Mean Inference Time (std dev)")) print("--------------------------------------------------") for network in networks: evaluate_network(network, target, target_host, args.repeat)