# 演示DEMO ## ​1. DEMO概述 当前SDK内有关于模型转换的一些演示demo: | 示例目录 | 功能 | 网络模型 | 输入 | 输出 | | -------------------------- | ------------------------------------- | --------------- | --------------- | ----------- | | create\_lmdb\_demo | 基于imageset创建lmdb文件 | 无 | image set | lmdb | | classify\_demo | 分类网络模型转换以及推理流程 | resnet18 | fp32umodel | int8 umodel | | detect\_demo | 人脸检测网络模型转换以及推理流程 | squeezenet\_21k | fp32umodel | int8 umodel | | googlenet\_bmodel\_convert | googlenet转换为int8 bmodel完整流程 | googlenet | fp32 caffemodel | int8 bmodel | | resnet50\_bmodel\_convert | resnet50　caffemodel转换为int8 bmodel完成流程 | resnet50 | fp32 caffemodel | int8 bmodel | ## 2. create\_lmdb\_demo create\_lmdb\_demo文件路径为： /workspace/examples/calibration/create\_lmdb\_demo demo主要描述了如何使用convert\_imageset来生成量化所使用的lmdb数据集合： ``` function convert_imageset_to_lmdb_demo() { rm $IMG_DIR/img_lmdb -rif cali_gen_imagelist; ret=$?; if [ $ret -ne 0 ]; then echo "#ERROR: cali_gen_imagelist"; return $ret; fi # argv[1] : image dir # argv[2] : image list convert_imageset --shuffle --resize_height=256 --resize_width=256 \ $IMG_DIR/ $IMG_DIR/ImgList.txt $IMG_DIR/img_lmdb ret=$?; if [ $ret -ne 0 ]; then echo "#ERROR: convt imgage to lmdb"; fi echo "#INFO: Convert Images to lmdb done" return $ret; } ``` 其中resize\_width需要根据网络模型的实际输入大小来确定 在此路径下，直接执行启动脚本，可以生成对应的lmdb: ``` root@cai-thinkpad-t470p:/workspace/examples/calibration/create_lmdb_demo# ./create_lmdb_demo.sh /workspace/examples/calibration/create_lmdb_demo/images /workspace/examples/calibration/create_lmdb_demo #INFO: Create Imageset Done /workspace/examples/calibration/create_lmdb_demo I1008 14:43:46.345486 232 convert_imageset.cpp:86] Shuffling data I1008 14:43:46.345930 232 convert_imageset.cpp:89] A total of 2 images. I1008 14:43:46.346113 232 db_lmdb.cpp:35] Opened lmdb ./images//img_lmdb I1008 14:43:46.370638 232 convert_imageset.cpp:153] Processed 2 files. #INFO: Convert Images to lmdb done root@cai-thinkpad-t470p:/workspace/examples/calibration/create_lmdb_demo# tree . |-- create_lmdb_demo.sh `-- images |-- ImgList.txt |-- cat\ gray.jpg |-- cat.jpg |-- cat_gray.jpg |-- fish-bike.jpg `-- img_lmdb |-- data.mdb `-- lock.mdb ``` 其中的images/img\_lmdb就是生成的lmdb数据集。用户可以参考此示例生成模型对应的数据集合。 ## 3. classify\_demo 主要是使用resnet18.caffemodel如何生成对应的int8 umodel，并验证int8下的精度。 其中自带lmdb文件，并且校准迭代次数简化为10次，方便快速执行： ``` root@cai-thinkpad-t470p:/workspace/examples/calibration/classify_demo# ./classify_demo.sh FLAGS_iterations = 10 FLAGS_BITWITH = TO_INT8 I1008 14:50:15.044811 242 common.cpp:345] use_GPU have not be set ! I1008 14:50:15.045114 242 calibration_use_pb.cpp:499] Use CPU. I1008 14:50:15.056399 242 net.cpp:1420] Initializing net from parameters: name: "ResNet-18" state { phase: TEST } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 224 mean_value: 103.939 mean_value: 116.779 mean_value: 123.68 } data_param { source: "./lmdb/imagenet_s/ilsvrc12_val_lmdb" batch_size: 1 backend: LMDB } } ... I1008 14:50:34.734228 245 accuracy_layer.cpp:101] use un-unique top_k I1008 14:50:34.734349 245 ufw.cpp:297] Batch 9, acc/top-1 = 0 I1008 14:50:34.734374 245 ufw.cpp:297] Batch 9, acc/top-5 = 1 I1008 14:50:34.734385 245 ufw.cpp:302] Batch 9 Finished I1008 14:50:34.734391 245 ufw.cpp:305] Loss: 0 I1008 14:50:34.734407 245 ufw.cpp:313] acc/top-5 = 0.9 I1008 14:50:34.734426 245 ufw.cpp:313] acc/top-1 = 0.6 #INFO: Test Resnet-INT8 Done ``` 执行后打印上述top\_k的置信度。同时生成了对应的umodel: ``` root@cai-thinkpad-t470p:/workspace/examples/calibration/classify_demo# tree . |-- classify_demo.sh |-- lmdb | `-- imagenet_s | `-- ilsvrc12_val_lmdb | |-- data.mdb | `-- lock.mdb |-- models | |-- resnet18.fp32umodel | |-- resnet18.int8umodel | |-- resnet18.prototxt | |-- resnet18_deploy_fp32_unique_top.prototxt | |-- resnet18_deploy_int8_unique_top.prototxt | |-- resnet18_test_fp32_unique_top.prototxt | `-- resnet18_test_int8_unique_top.prototxt `-- resnet18_test_int8_unique_top_accuracy_int8.md ``` ## 4. detect\_demo 主要使用squeezenet网络来做detect演示，主要包括： 1. 检测程序编译。 2. squeezennet int8模型的量化生成。 3. 利用int8模型进行人脸检测。 用户可以根据需要参考face\_demo.sh文件的最后几行执行命令。 ``` root@cai-thinkpad-t470p:/workspace/examples/calibration/detect_demo# ./face_demo.sh FLAGS_iterations = 100 FLAGS_BITWITH = TO_INT8 I1008 14:59:36.823330 257 common.cpp:345] use_GPU have not be set ! I1008 14:59:36.823693 257 calibration_use_pb.cpp:499] Use CPU. I1008 14:59:36.831105 257 upgrade_proto.cpp:69] Attempting to upgrade input file specified using deprecated input fields: models/squeezenet/squeezenet_21k_test.prototxt I1008 14:59:36.831171 257 upgrade_proto.cpp:72] Successfully upgraded file specified using deprecated input fields. W1008 14:59:36.831178 257 upgrade_proto.cpp:74] Note that future Ufw releases will only support input layers and not input fields. I1008 14:59:36.839711 257 net.cpp:1420] Initializing net from parameters: name: "SqueezeNet" state { phase: TEST } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mean_value: 104 mean_value: 117 mean_value: 123 } data_param { source: "./lmdb/100_pics_squeezenet_711_400_lmdb" batch_size: 1 backend: LMDB } } ... I1008 15:07:12.407119 284 layer.hpp:481] UFW INT8_NEURON MODEL final predict 19 bboxes ... ``` 大约10分钟后，执行完毕，使用in8umodel最终检测出19个目标框，可以查看生成的图片确认。另外可以修改face\_demo.sh中的执行命令，测试fp32网络的检测结果，二者可以比对。 ## 5. googlenet\_bmodel\_convert 此示例包含下载作者版模型，并且脚本一键转换模型，用户可以直接基于此demo框架添加私有模型，并修改脚本内对应参数，既可以从caffemodel转成部署使用的bmodel: ``` root@cai-thinkpad-t470p:/workspace/examples/calibration/googlenet_bmodel_convert# ./download_resnet50_model.sh root@cai-thinkpad-t470p:/workspace/examples/calibration/googlenet_bmodel_convert# ./generate_int8_bmodel.sh function convert_to_int8_umodel() { calibration_use_pb release \ -model=./models/googlenet-quantize.prototxt \ -weights=./models/bvlc-googlenet.caffemodel \ -iterations=${CALI_NUM} \ -bitwidth=TO_INT8 ret=$?; if [ $ret -ne 0 ]; then echo "#ERROR: googlenet Fp32ToInt8"; popd; return $ret;fi echo "#INFO: Run Example (googlenet Fp32ToInt8) Done" return $ret; } function convert_to_bmodel() { mkdir -p int8model bmnetu -model ./models/bvlc-googlenet_deploy_int8_unique_top.prototxt \ -weight ./models/bvlc-googlenet.int8umodel \ -max_n 1 \ -prec=INT8 \ -dyn=0 \ -cmp=1 \ -target=BM1684 \ -outdir=./int8model } ``` 最终会生成所需bmodel: ``` |-- int8model | |-- compilation.bmodel | |-- compiler_profile_0.dat | |-- input_ref_data.dat | `-- output_ref_data.dat ``` 并且会验证生成的bmodel的精度是否与原caffemodel一致(bmrt\_test)： ``` ******do bmodel test****** [BMRT][deal_with_options:901] INFO : Loop num: 1 begin to cmodel init... mult engine c_model init l2_fill complete npu num = 64 eu num = 32 global mem size = 4294967296 bmcpu init: skip cpu_user_defined open usercpu.so, init user_cpu_init [BMRT][load_bmodel:660] INFO : Loading bmodel from [./int8model/compilation.bmodel]. Thanks for your patience... [BMRT][load_bmodel:642] INFO : pre net num: 0, load net num: 1 [BMRT][bmrt_test:503] INFO : ==> running network #0, name: ResNet-50, loop: 0 [BMRT][bmrt_test:614] INFO : net[ResNet-50] stage[0], launch total time is 5297646 us (npu 0 us, cpu 5297646 us) [BMRT][bmrt_test:650] INFO : +++ The network[ResNet-50] stage[0] cmp success +++ [BMRT][bmrt_test:674] INFO : load input time(s): 0.000130 [BMRT][bmrt_test:675] INFO : calculate time(s): 5.297647 [BMRT][bmrt_test:676] INFO : get output time(s): 0.000015 [BMRT][bmrt_test:677] INFO : compare time(s): 0.000044 BMLIB Send Quit Message ``` 为方便演示，自带lmdb文件，为Imagenet. ## 6. resnet50\_bmodel\_convert 此demo与googlenet的量化类似，用户可以直接基于此demo框架添加私有模型，并修改脚本内对应参数，既可以从caffemodel转成部署使用的bmodel: ``` function convert_to_int8_umodel() { calibration_use_pb release \ -model=./models/ResNet-50-quantize.prototxt \ -weights=./models/ResNet-50-model.caffemodel \ -iterations=${CALI_NUM} \ -bitwidth=TO_INT8 ret=$?; if [ $ret -ne 0 ]; then echo "#ERROR: Resnet50 Fp32ToInt8"; popd; return $ret;fi echo "#INFO: Run Example (Resnet18 Fp32ToInt8) Done" return $ret; } function convert_to_bmodel() { mkdir -p int8model bmnetu -model ./models/ResNet-50-model_test_int8_unique_top.prototxt \ -weight ./models/ResNet-50-model.int8umodel \ -max_n 1 \ -prec=INT8 \ -dyn=0 \ -cmp=1 \ -target=BM1684 \ -outdir=./int8model } ``` 并且会验证生成的bmodel的精度是否与原caffemodel一致(bmrt\_test)： ``` ******do bmodel test****** [BMRT][deal_with_options:901] INFO : Loop num: 1 begin to cmodel init... mult engine c_model init l2_fill complete npu num = 64 eu num = 32 global mem size = 4294967296 bmcpu init: skip cpu_user_defined open usercpu.so, init user_cpu_init [BMRT][load_bmodel:660] INFO : Loading bmodel from [./int8model/compilation.bmodel]. Thanks for your patience... [BMRT][load_bmodel:642] INFO : pre net num: 0, load net num: 1 [BMRT][bmrt_test:503] INFO : ==> running network #0, name: googlenet, loop: 0 [BMRT][bmrt_test:614] INFO : net[googlenet] stage[0], launch total time is 8462144 us (npu 0 us, cpu 8462144 us) [BMRT][bmrt_test:650] INFO : +++ The network[googlenet] stage[0] cmp success +++ [BMRT][bmrt_test:674] INFO : load input time(s): 0.000133 [BMRT][bmrt_test:675] INFO : calculate time(s): 8.462144 [BMRT][bmrt_test:676] INFO : get output time(s): 0.000016 [BMRT][bmrt_test:677] INFO : compare time(s): 0.000045 BMLIB Send Quit Message ``` 为方便演示，自带lmdb文件，为Imagenet. --- # 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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