feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
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3rdparty/ncnn/python/tests/benchmark.py
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3rdparty/ncnn/python/tests/benchmark.py
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# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import sys
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import time
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import ncnn
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param_root = "../benchmark/"
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g_warmup_loop_count = 8
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g_loop_count = 4
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g_enable_cooling_down = True
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g_vkdev = None
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g_blob_vkallocator = None
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g_staging_vkallocator = None
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g_blob_pool_allocator = ncnn.UnlockedPoolAllocator()
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g_workspace_pool_allocator = ncnn.PoolAllocator()
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def benchmark(comment, _in, opt):
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_in.fill(0.01)
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g_blob_pool_allocator.clear()
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g_workspace_pool_allocator.clear()
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if opt.use_vulkan_compute:
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g_blob_vkallocator.clear()
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g_staging_vkallocator.clear()
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net = ncnn.Net()
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net.opt = opt
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if net.opt.use_vulkan_compute:
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net.set_vulkan_device(g_vkdev)
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net.load_param(param_root + comment + ".param")
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dr = ncnn.DataReaderFromEmpty()
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net.load_model(dr)
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input_names = net.input_names()
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output_names = net.output_names()
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if g_enable_cooling_down:
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time.sleep(10)
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# warm up
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for i in range(g_warmup_loop_count):
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# test with statement
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with net.create_extractor() as ex:
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ex.input(input_names[0], _in)
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ex.extract(output_names[0])
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time_min = sys.float_info.max
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time_max = -sys.float_info.max
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time_avg = 0.0
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for i in range(g_loop_count):
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start = time.time()
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# test net keep alive until ex freed
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ex = net.create_extractor()
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ex.input(input_names[0], _in)
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ex.extract(output_names[0])
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end = time.time()
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timespan = end - start
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time_min = timespan if timespan < time_min else time_min
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time_max = timespan if timespan > time_max else time_max
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time_avg += timespan
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time_avg /= g_loop_count
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print(
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"%20s min = %7.2f max = %7.2f avg = %7.2f"
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% (comment, time_min * 1000, time_max * 1000, time_avg * 1000)
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)
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if __name__ == "__main__":
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loop_count = 4
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num_threads = ncnn.get_cpu_count()
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powersave = 0
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gpu_device = -1
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cooling_down = 1
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argc = len(sys.argv)
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if argc >= 2:
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loop_count = int(sys.argv[1])
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if argc >= 3:
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num_threads = int(sys.argv[2])
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if argc >= 4:
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powersave = int(sys.argv[3])
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if argc >= 5:
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gpu_device = int(sys.argv[4])
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if argc >= 6:
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cooling_down = int(sys.argv[5])
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use_vulkan_compute = gpu_device != -1
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g_enable_cooling_down = cooling_down != 0
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g_loop_count = loop_count
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g_blob_pool_allocator.set_size_compare_ratio(0.0)
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g_workspace_pool_allocator.set_size_compare_ratio(0.5)
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if use_vulkan_compute:
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g_warmup_loop_count = 10
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g_vkdev = ncnn.get_gpu_device(gpu_device)
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g_blob_vkallocator = ncnn.VkBlobAllocator(g_vkdev)
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g_staging_vkallocator = ncnn.VkStagingAllocator(g_vkdev)
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opt = ncnn.Option()
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opt.lightmode = True
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opt.num_threads = num_threads
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opt.blob_allocator = g_blob_pool_allocator
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opt.workspace_allocator = g_workspace_pool_allocator
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if use_vulkan_compute:
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opt.blob_vkallocator = g_blob_vkallocator
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opt.workspace_vkallocator = g_blob_vkallocator
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opt.staging_vkallocator = g_staging_vkallocator
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opt.use_winograd_convolution = True
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opt.use_sgemm_convolution = True
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opt.use_int8_inference = True
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opt.use_vulkan_compute = use_vulkan_compute
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opt.use_fp16_packed = True
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opt.use_fp16_storage = True
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opt.use_fp16_arithmetic = True
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opt.use_int8_storage = True
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opt.use_int8_arithmetic = True
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opt.use_packing_layout = True
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opt.use_shader_pack8 = False
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opt.use_image_storage = False
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ncnn.set_cpu_powersave(powersave)
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ncnn.set_omp_dynamic(0)
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ncnn.set_omp_num_threads(num_threads)
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print("loop_count =", loop_count)
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print("num_threads =", num_threads)
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print("powersave =", ncnn.get_cpu_powersave())
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print("gpu_device =", gpu_device)
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print("cooling_down =", g_enable_cooling_down)
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benchmark("squeezenet", ncnn.Mat((227, 227, 3)), opt)
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benchmark("squeezenet_int8", ncnn.Mat((227, 227, 3)), opt)
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benchmark("mobilenet", ncnn.Mat((224, 224, 3)), opt)
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benchmark("mobilenet_int8", ncnn.Mat((224, 224, 3)), opt)
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benchmark("mobilenet_v2", ncnn.Mat((224, 224, 3)), opt)
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# benchmark("mobilenet_v2_int8", ncnn.Mat(w=224, h=224, c=3), opt)
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benchmark("mobilenet_v3", ncnn.Mat((224, 224, 3)), opt)
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benchmark("shufflenet", ncnn.Mat((224, 224, 3)), opt)
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benchmark("shufflenet_v2", ncnn.Mat((224, 224, 3)), opt)
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benchmark("mnasnet", ncnn.Mat((224, 224, 3)), opt)
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benchmark("proxylessnasnet", ncnn.Mat((224, 224, 3)), opt)
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benchmark("efficientnet_b0", ncnn.Mat((224, 224, 3)), opt)
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benchmark("regnety_400m", ncnn.Mat((224, 224, 3)), opt)
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benchmark("blazeface", ncnn.Mat((128, 128, 3)), opt)
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benchmark("googlenet", ncnn.Mat((224, 224, 3)), opt)
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benchmark("googlenet_int8", ncnn.Mat((224, 224, 3)), opt)
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benchmark("resnet18", ncnn.Mat((224, 224, 3)), opt)
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benchmark("resnet18_int8", ncnn.Mat((224, 224, 3)), opt)
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benchmark("alexnet", ncnn.Mat((227, 227, 3)), opt)
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benchmark("vgg16", ncnn.Mat((224, 224, 3)), opt)
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benchmark("vgg16_int8", ncnn.Mat((224, 224, 3)), opt)
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benchmark("resnet50", ncnn.Mat((224, 224, 3)), opt)
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benchmark("resnet50_int8", ncnn.Mat((224, 224, 3)), opt)
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benchmark("squeezenet_ssd", ncnn.Mat((300, 300, 3)), opt)
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benchmark("squeezenet_ssd_int8", ncnn.Mat((300, 300, 3)), opt)
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benchmark("mobilenet_ssd", ncnn.Mat((300, 300, 3)), opt)
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benchmark("mobilenet_ssd_int8", ncnn.Mat((300, 300, 3)), opt)
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benchmark("mobilenet_yolo", ncnn.Mat((416, 416, 3)), opt)
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benchmark("mobilenetv2_yolov3", ncnn.Mat((352, 352, 3)), opt)
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benchmark("yolov4-tiny", ncnn.Mat((416, 416, 3)), opt)
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