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- # Tencent is pleased to support the open source community by making ncnn available.
- #
- # 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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