使用NCNN benchmark测出每一层的时间

最新推荐文章于 2025-06-01 09:14:44 发布

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最新推荐文章于 2025-06-01 09:14:44 发布

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分类专栏：深度学习 CNN 文章标签： NCNN 移动端 benchmark

本文链接：https://blog.youkuaiyun.com/zym19941119/article/details/86563818

本文详细介绍了如何在腾讯NCNN框架中实现测量模型推理时间及每一层平均时间的功能。通过修改源码，添加了一个hash map记录层时间，并在forward_layer方法中计算每层耗时。在手机上运行，可以输出测试次数、线程数、powersave模式和每层的平均时间。修改包括在Net类中添加记录时间的结构，调整Extractor类的extract方法，并编写benchmark测试代码。

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腾讯NCNN框架中自带了测模型推理时间和每层时间的功能，然而，这些功能并没有文档写明白如何使用，也没有默认开启和计算每层的平均时间，为了更好地进行速度的测试，我重写了NCNN源码，并以此文记录下以下功能是如何实现的。

效果

在手机上，给出任何一个网络可以用的ncnn param文件，可以得到以下的结果：

loop_count = 10
num_threads = 4
powersave = 2
output_blob_name: prob
mode: cls

resnet18.param :

Convolution conv1 8.014ms | feature_map: 224 x 224 inch: 3 outch: 64 kernel: 7 x 7 stride: 2
BatchNorm bn_conv1 0.336ms | feature_map: 112 x 112 inch: 64 outch: 64
Scale scale_conv1 0.218ms | feature_map: 112 x 112 inch: 64 outch: 64
ReLU conv1_relu 0.200ms | feature_map: 112 x 112 inch: 64 outch: 64
Pooling pool1 0.534ms | feature_map: 112 x 112 inch: 64 outch: 64 kernel: 3 x 3 stride: 2
Split splitncnn_0 0.001ms |
Convolution res2a_branch1 0.854ms | feature_map: 56 x 56 inch: 64 outch: 64 kernel: 1 x 1
BatchNorm bn2a_branch1 0.052ms | feature_map: 56 x 56 inch: 64 outch: 64
Scale scale2a_branch1 0.028ms | feature_map: 56 x 56 inch: 64 outch: 64
Convolution res2a_branch2a 3.417ms | feature_map: 56 x 56 inch: 64 outch: 64 kernel: 3 x 3
BatchNorm bn2a_branch2a 0.120ms | feature_map: 56 x 56 inch: 64 outch: 64
Scale scale2a_branch2a 0.078ms | feature_map: 56 x 56 inch: 64 outch: 64
ReLU res2a_branch2a_relu 0.047ms | feature_map: 56 x 56 inch: 64 outch: 64
Convolution res2a_branch2b 3.274ms | feature_map: 56 x 56 inch: 64 outch: 64 kernel: 3 x 3
BatchNorm bn2a_branch2b 0.113ms | feature_map: 56 x 56 inch: 64 outch: 64
Scale scale2a_branch2b 0.139ms | feature_map: 56 x 56 inch: 64 outch: 64
Eltwise res2a 0.146ms |
ReLU res2a_relu 0.095ms | feature_map: 56 x 56 inch: 64 outch: 64
Split splitncnn_1 0.001ms |
Convolution res2b_branch2a 3.356ms | feature_map: 56 x 56 inch: 64 outch: 64 kernel: 3 x 3
BatchNorm bn2b_branch2a 0.107ms | feature_map: 56 x 56 inch: 64 outch: 64
Scale scale2b_branch2a 0.085ms | feature_map: 56 x 56 inch: 64 outch: 64
ReLU res2b_branch2a_relu 0.106ms | feature_map: 56 x 56 inch: 64 outch: 64
Convolution res2b_branch2b 3.544ms | feature_map: 56 x 56 inch: 64 outch: 64 kernel: 3 x 3
BatchNorm bn2b_branch2b 0.065ms | feature_map: 56 x 56 inch: 64 outch: 64
Scale scale2b_branch2b 0.128ms | feature_map: 56 x 56 inch: 64 outch: 64
Eltwise res2b 0.159ms |
ReLU res2b_relu 0.110ms | feature_map: 56 x 56 inch: 64 outch: 64
Split splitncnn_2 0.001ms |
Convolution res3a_branch1 0.679ms | feature_map: 56 x 56 inch: 64 outch: 128 kernel: 1 x 1 stride: 2
BatchNorm bn3a_branch1 0.044ms | feature_map: 28 x 28 inch: 128 outch: 128
Scale scale3a_branch1 0.119ms | feature_map: 28 x 28 inch: 128 outch: 128
Convolution res3a_branch2a 2.173ms | feature_map: 56 x 56 inch: 64 outch: 128 kernel: 3 x 3 stride: 2
BatchNorm bn3a_branch2a 0.071ms | feature_map: 28 x 28 inch: 128 outch: 128
Scale scale3a_branch2a 0.035ms | feature_map: 28 x 28 inch: 128 outch: 128
ReLU res3a_branch2a_relu 0.115ms | feature_map: 28 x 28 inch: 128 outch: 128
Convolution res3a_branch2b 2.586ms | feature_map: 28 x 28 inch: 128 outch: 128 kernel: 3 x 3
BatchNorm bn3a_branch2b 0.102ms | feature_map: 28 x 28 inch: 128 outch: 128
Scale scale3a_branch2b 0.045ms | feature_map: 28 x 28 inch: