File size: 6,377 Bytes
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#
# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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.
import sys
import time
import ncnn
param_root = "../benchmark/"
g_warmup_loop_count = 8
g_loop_count = 4
g_enable_cooling_down = True
g_vkdev = None
g_blob_vkallocator = None
g_staging_vkallocator = None
g_blob_pool_allocator = ncnn.UnlockedPoolAllocator()
g_workspace_pool_allocator = ncnn.PoolAllocator()
def benchmark(comment, _in, opt):
_in.fill(0.01)
g_blob_pool_allocator.clear()
g_workspace_pool_allocator.clear()
if opt.use_vulkan_compute:
g_blob_vkallocator.clear()
g_staging_vkallocator.clear()
net = ncnn.Net()
net.opt = opt
if net.opt.use_vulkan_compute:
net.set_vulkan_device(g_vkdev)
net.load_param(param_root + comment + ".param")
dr = ncnn.DataReaderFromEmpty()
net.load_model(dr)
input_names = net.input_names()
output_names = net.output_names()
if g_enable_cooling_down:
time.sleep(10)
# warm up
for i in range(g_warmup_loop_count):
# test with statement
with net.create_extractor() as ex:
ex.input(input_names[0], _in)
ex.extract(output_names[0])
time_min = sys.float_info.max
time_max = -sys.float_info.max
time_avg = 0.0
for i in range(g_loop_count):
start = time.time()
# test net keep alive until ex freed
ex = net.create_extractor()
ex.input(input_names[0], _in)
ex.extract(output_names[0])
end = time.time()
timespan = end - start
time_min = timespan if timespan < time_min else time_min
time_max = timespan if timespan > time_max else time_max
time_avg += timespan
time_avg /= g_loop_count
print(
"%20s min = %7.2f max = %7.2f avg = %7.2f"
% (comment, time_min * 1000, time_max * 1000, time_avg * 1000)
)
if __name__ == "__main__":
loop_count = 4
num_threads = ncnn.get_cpu_count()
powersave = 0
gpu_device = -1
cooling_down = 1
argc = len(sys.argv)
if argc >= 2:
loop_count = int(sys.argv[1])
if argc >= 3:
num_threads = int(sys.argv[2])
if argc >= 4:
powersave = int(sys.argv[3])
if argc >= 5:
gpu_device = int(sys.argv[4])
if argc >= 6:
cooling_down = int(sys.argv[5])
use_vulkan_compute = gpu_device != -1
g_enable_cooling_down = cooling_down != 0
g_loop_count = loop_count
g_blob_pool_allocator.set_size_compare_ratio(0.0)
g_workspace_pool_allocator.set_size_compare_ratio(0.5)
if use_vulkan_compute:
g_warmup_loop_count = 10
g_vkdev = ncnn.get_gpu_device(gpu_device)
g_blob_vkallocator = ncnn.VkBlobAllocator(g_vkdev)
g_staging_vkallocator = ncnn.VkStagingAllocator(g_vkdev)
opt = ncnn.Option()
opt.lightmode = True
opt.num_threads = num_threads
opt.blob_allocator = g_blob_pool_allocator
opt.workspace_allocator = g_workspace_pool_allocator
if use_vulkan_compute:
opt.blob_vkallocator = g_blob_vkallocator
opt.workspace_vkallocator = g_blob_vkallocator
opt.staging_vkallocator = g_staging_vkallocator
opt.use_winograd_convolution = True
opt.use_sgemm_convolution = True
opt.use_int8_inference = True
opt.use_vulkan_compute = use_vulkan_compute
opt.use_fp16_packed = True
opt.use_fp16_storage = True
opt.use_fp16_arithmetic = True
opt.use_int8_storage = True
opt.use_int8_arithmetic = True
opt.use_packing_layout = True
opt.use_shader_pack8 = False
opt.use_image_storage = False
ncnn.set_cpu_powersave(powersave)
ncnn.set_omp_dynamic(0)
ncnn.set_omp_num_threads(num_threads)
print("loop_count =", loop_count)
print("num_threads =", num_threads)
print("powersave =", ncnn.get_cpu_powersave())
print("gpu_device =", gpu_device)
print("cooling_down =", g_enable_cooling_down)
benchmark("squeezenet", ncnn.Mat((227, 227, 3)), opt)
benchmark("squeezenet_int8", ncnn.Mat((227, 227, 3)), opt)
benchmark("mobilenet", ncnn.Mat((224, 224, 3)), opt)
benchmark("mobilenet_int8", ncnn.Mat((224, 224, 3)), opt)
benchmark("mobilenet_v2", ncnn.Mat((224, 224, 3)), opt)
# benchmark("mobilenet_v2_int8", ncnn.Mat(w=224, h=224, c=3), opt)
benchmark("mobilenet_v3", ncnn.Mat((224, 224, 3)), opt)
benchmark("shufflenet", ncnn.Mat((224, 224, 3)), opt)
benchmark("shufflenet_v2", ncnn.Mat((224, 224, 3)), opt)
benchmark("mnasnet", ncnn.Mat((224, 224, 3)), opt)
benchmark("proxylessnasnet", ncnn.Mat((224, 224, 3)), opt)
benchmark("efficientnet_b0", ncnn.Mat((224, 224, 3)), opt)
benchmark("regnety_400m", ncnn.Mat((224, 224, 3)), opt)
benchmark("blazeface", ncnn.Mat((128, 128, 3)), opt)
benchmark("googlenet", ncnn.Mat((224, 224, 3)), opt)
benchmark("googlenet_int8", ncnn.Mat((224, 224, 3)), opt)
benchmark("resnet18", ncnn.Mat((224, 224, 3)), opt)
benchmark("resnet18_int8", ncnn.Mat((224, 224, 3)), opt)
benchmark("alexnet", ncnn.Mat((227, 227, 3)), opt)
benchmark("vgg16", ncnn.Mat((224, 224, 3)), opt)
benchmark("vgg16_int8", ncnn.Mat((224, 224, 3)), opt)
benchmark("resnet50", ncnn.Mat((224, 224, 3)), opt)
benchmark("resnet50_int8", ncnn.Mat((224, 224, 3)), opt)
benchmark("squeezenet_ssd", ncnn.Mat((300, 300, 3)), opt)
benchmark("squeezenet_ssd_int8", ncnn.Mat((300, 300, 3)), opt)
benchmark("mobilenet_ssd", ncnn.Mat((300, 300, 3)), opt)
benchmark("mobilenet_ssd_int8", ncnn.Mat((300, 300, 3)), opt)
benchmark("mobilenet_yolo", ncnn.Mat((416, 416, 3)), opt)
benchmark("mobilenetv2_yolov3", ncnn.Mat((352, 352, 3)), opt)
benchmark("yolov4-tiny", ncnn.Mat((416, 416, 3)), opt)
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