# c/c++开发者 ## caffe-ssd300算法移植 BMNETC是针对caffe的模型编译器，可将某网络的caffemodel和prototxt编译成BMRuntime 所需要的文件。而且在编译的同时，支持每一层的NPU模型计算结果都会和CPU的计算结果进行对比，保证正确性。Caffe模型转换的主要操作工具介绍如下： Command name: bmnetc - BMNet compiler command for Caffe mode ``` /path/to/bmnetc [--model=] \ [--weight=] \ [--shapes=] \ [--net_name=] \ [--opt=] \ [--dyn=] \ [--outdir=] \ [--target=] \ [--cmp=] \ [--mode=] \ [--enable_profile=] \ [--show_args] \ [--check_model] ``` | args | type | Description | | --------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------- | | model | string | Necessary. Caffe prototxt path | | weight | string | Necessary. Caffemodel(weight) path | | shapes | string | Optional. Shapes of all inputs, default use the shape in prototxt, format \[x,x,x,x],\[x,x]…, these correspond to inputs one by one in sequence | | net\_name | string | Optional. Name of the network, default use the name in prototxt | | opt | int | Optional. Optimization level. Option: 0, 1, 2, default 2. | | dyn | bool | Optional. Use dynamic compilation, default false. | | outdir | string | Necessary. Output directory | | target | string | Necessary. Option: BM1682, BM1684; default: BM1682 | | cmp | bool | Optional.Check result during compilation. Default: true | | mode | string | Optional. Set bmnetc mode. Option: compile, GenUmodel. Default: compile. | | enable\_profile | bool | Optional. Enable profile log. Default: false | | show\_args | | Optional. Display arguments passed to bmnetc compiler. | | check\_model | | Optional. Check unsupported layer types from input model. | * 下载SSD模型 ``` ** 从公网下载模型，或者使用自己训练好的模型 ** SSD模型：https://docs.google.com/uc?export=download&id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA ** Sophon将从公网获取caffe-ssd模型做成了脚本 ** # SSD_object/model# ./download_model.sh ``` * 使用Sophon complier编译模型 ``` ** Sophon将编译caffe-ssd模型做成了脚本 ** SSD_object/model# ./generate_verify_bmodel.sh ** 该脚本的主要内容 ** bmnetc --model=${model_dir}/ssd300_deploy.prototxt \ --weight=${model_dir}/ssd300.caffemodel \ --shapes=[1,3,300,300] \ --net_name="ssd300-caffe" \ --outdir=./out/ssd300 \ --target=BM1682 ** 执行完脚本后正常的输出结果如下 ** ============================================================ *** Store bmodel of BMCompiler... ============================================================ I1018 13:52:19.306128 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [data] I1018 13:52:19.306155 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [detection_out] I1018 13:52:19.884819 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [data] I1018 13:52:19.884840 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_loc] I1018 13:52:19.884846 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_priorbox] I1018 13:52:19.884850 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_conf_flatten] I1018 13:52:19.884862 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_loc] I1018 13:52:19.884869 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_conf_flatten] I1018 13:52:19.884872 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [mbox_priorbox] I1018 13:52:19.884876 211 bmcompiler_bmodel.cpp:123] [BMCompiler:I] save_tensor inout name [detection_out] =====do check new Bmodel===== I1018 13:52:20.933038 218 bmrt_test.cpp:829] [BMRT_TEST:I] Loop num: 1 mult engine c_model init bmcpu init: skip cpu_user_defined open usercpu.so, init user_cpu_init I1018 13:52:20.937220 218 bmruntime_bmodel.cpp:683] [BMRuntime:I] Loading bmodel from [./out/ssd300//compilation.bmodel]. Thanks for your patience... I1018 13:52:20.937304 218 bmruntime_bmodel.cpp:665] [BMRuntime:I] pre net num: 0, load net num: 1 I1018 13:52:20.966521 218 bmrt_test.cpp:458] [BMRT_TEST:I] ==> running network #0, name: ssd300-caffe, loop: 0 INFO Couldn't find any detections I1018 13:52:39.612751 218 bmrt_test.cpp:561] [BMRT_TEST:I] the last_api_process_time_us is 0 us I1018 13:52:39.612772 218 bmrt_test.cpp:603] [BMRT_TEST:I] +++ The network[ssd300-caffe] stage[0] cmp success +++ I1018 13:52:39.612779 218 bmrt_test.cpp:614] [BMRT_TEST:I] load input time(s): 0.000368 I1018 13:52:39.612798 218 bmrt_test.cpp:615] [BMRT_TEST:I] calculate time(s): 18.6458 I1018 13:52:39.612803 218 bmrt_test.cpp:616] [BMRT_TEST:I] get output time(s): 4e-06 I1018 13:52:39.612807 218 bmrt_test.cpp:617] [BMRT_TEST:I] compare time(s): 4.5e-05 ** 结果判断 ** ** 有“+++ The network[ssd300-caffe] stage[0] cmp success +++”的提示，** ** 则模型编译流程正确，与原生模型的精度一致。** ** 编译生成的模型存放在如下目录 ** ├── out │ ├── ssd300 │ │ ├── compilation.bmodel │ │ ├── input_ref_data.dat │ │ └── output_ref_data.dat ``` * 加载Sophon bmodel ``` /** create device handle **/ bm_status_t status = bm_dev_request(&g_bm_handle_, 0); /** create inference runtime handle **/ g_p_bmrt_ = bmrt_create(g_bm_handle_); /** load bmodel by file **/ bool flag = bmrt_load_bmodel(g_p_bmrt_, bmodel_file.c_str()); ... ... /** input outpur tensor (device memory <-> system memory))**/ auto net_info = bmrt_get_network_info(p_bmrt_, net_names_[0]); /** define the input/output tensor of net with shape fixed shape **/ bmrt_tensor(&input_tensor_, p_bmrt_, net_info->input_dtypes[0], input_shape); bmrt_tensor(&output_tensor_, p_bmrt_, net_info->output_dtypes[0], output_shape); /** mmap device memory to system memory **/ status = bm_mem_mmap_device_mem(bm_handle_, &input_tensor_.device_mem, (unsigned long long*)&input_); status = bm_mem_mmap_device_mem(bm_handle_, &output_tensor_.device_mem, (unsigned long long*)&output_); ``` * 数据预处理 ``` ... /** Sophon支持Opencv读取图形文件 **/ cv::Mat img = cv::imread(input_url); ... /** set mean **/ std::vector mean_values; mean_values.push_back(123); mean_values.push_back(117); mean_values.push_back(104); /** resize to 300 * 300 **/ cv::Mat sample_resized(input_geometry_.height, input_geometry_.width, CV_8UC3); cv::resize(sample, sample_resized, input_geometry_); /** uint8 to float **/ sample_resized.convertTo(sample_float, CV_32FC3); /** sample normalized **/ cv::subtract(sample_float, mean_, sample_normalized); cv::split(sample_normalized, *input_channels); ``` * 执行Sophon Model推理 ``` /** flush inference **/ status = bm_mem_flush_device_mem(bm_handle_, &input_tensor_.device_mem); /** ssd inference **/ bool ret = bmrt_launch_tensor_ex(p_bmrt_, net_names_[0], &input_tensor_, 1, &output_tensor_, 1, true, false); /** sync, wait for finishing inference **/ status = bm_thread_sync(bm_handle_); /** free device memory **/ status = bm_mem_invalidate_device_mem(bm_handle_, &output_tensor_.device_mem); ``` * 数据后处理 ``` /** get the number of the elements with shape **/ int output_count = bmrt_shape_count(&output_shape); /** parse the output tensor with elements**/ for (int i = 0; i < output_count; i += 7) { ... /** output_ is a system global memory for output tensor**/ float *proposal = &output_[i]; detection.class_id = proposal[1]; detection.score = proposal[2]; detection.x1 = proposal[3] * image.cols; detection.y1 = proposal[4] * image.rows; detection.x2 = proposal[5] * image.cols; detection.y2 = proposal[6] * image.rows; } ``` * 实例演示 ``` $ cd bmnnsdk2-bm1682_v1.1.4 $ ./docker_run_bmnnsdk.sh # cd scripts # ./intall_lib.sh nntc # source envsetup_cmodel.sh # cd ../examples/SSD_object/cpp/ # make -f Makefile.arm clean && make -f Makefile.arm # make -f Makefile.arm install ** 将install目录复制到SOC单板上 ** **"YOUR_SOC_IP"字符串替换为实际的soc单板ip地址** # scp -r /workspace/install linaro@YOUR_SOC_IP:~/ # exit $ ssh linaro@YOUR_SOC_IP ** 安装 SE3 driver ** $ sudo /system/data/chdriver.sh $ cd install $ ./bin/ssd300_object.arm image res/ssd300/vehicle_1.jpg model/ssd300 1 1 bmcpu init: skip cpu_user_defined open usercpu.so, init user_cpu_init > get model ssd300-caffe successfully ... thread-0 | 43 (us) >> [0]lifecycle thread-0 | 125 (us) >> [0]ssd overall thread-0 | 126 (us) >> [0]read image thread-0 | 13222 (us) << [0]read image thread-0 | 13291 (us) >> [0]detection thread-0 | 13300 (us) >> [0]ssd pre-process thread-0 | 25845 (us) << [0]ssd pre-process thread-0 | 25850 (us) >> [0]flush inference thread-0 | 25958 (us) << [0]flush inference thread-0 | 25959 (us) >> [0]ssd inference thread-0 | 67027 (us) << [0]ssd inference thread-0 | 67042 (us) >> [0]ssd post-process thread-0 | 67054 (us) << [0]ssd post-process thread-0 | 67055 (us) << [0]detection thread-0 | 82971 (us) << [0]ssd overall thread-0 | 83061 (us) << [0]lifecycle ############################ DURATIONS: main process ############################ [ total time] - iteration <0> : 83572 us ** 执行完成后，会在当前目录下生成 detect image ** $ ls bin model models out-t0_0_vehicle_1.jpg res ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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