Permalink
Branch: master
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
89 lines (72 sloc) 3.54 KB
"""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.contrib.util import tempdir
import tvm.contrib.graph_runtime as runtime
import nnvm.compiler
import nnvm.testing
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 nnvm.compiler.build_config(opt_level=3):
graph, lib, params = nnvm.compiler.build(
net, target=target, target_host=target_host,
shape={'data': input_shape}, params=params, dtype=dtype)
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.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', 'mobilenet_v2', '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)