diff --git a/example/ppocr/README.md b/example/ppocr/README.md index 3acef7f..f14933a 100644 --- a/example/ppocr/README.md +++ b/example/ppocr/README.md @@ -29,39 +29,39 @@ git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data ### Usage -Run the PPOCR Detection example. +Run the PPOCR Detection example on rk3588 or replace with your Platform model. ``` cd example/ppocr/detect -go run detect.go +go run detect.go -p rk3588 ``` This will result in the output of: ``` -Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11) +Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33) Model Input Number: 1, Ouput Number: 1 Input tensors: index=0, name=x, n_dims=4, dims=[1, 480, 480, 3], n_elems=691200, size=691200, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-14, scale=0.018658 Output tensors: index=0, name=sigmoid_0.tmp_0, n_dims=4, dims=[1, 1, 480, 480], n_elems=230400, size=230400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922 -Model first run speed: inference=27.746374ms, post processing=2.968795ms, total time=30.715169ms -[0]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.991298 -[1]: [(27, 428), (371, 427), (371, 444), (27, 445)] 0.912538 -[2]: [(28, 398), (361, 397), (361, 413), (28, 414)] 0.953752 -[3]: [(368, 368), (476, 368), (476, 388), (368, 388)] 0.989887 -[4]: [(27, 365), (282, 365), (282, 384), (27, 384)] 0.975041 -[5]: [(26, 334), (342, 334), (342, 352), (26, 352)] 0.956719 -[6]: [(26, 303), (253, 303), (253, 320), (26, 320)] 0.974053 -[7]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990559 -[8]: [(26, 240), (242, 240), (242, 259), (26, 259)] 0.986159 -[9]: [(413, 233), (429, 233), (429, 305), (413, 305)] 0.970001 -[10]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.995540 -[11]: [(26, 178), (300, 179), (300, 196), (26, 195)] 0.991055 -[12]: [(28, 143), (280, 144), (280, 164), (28, 163)] 0.974824 -[13]: [(27, 112), (333, 113), (333, 135), (27, 134)] 0.899956 -[14]: [(26, 81), (172, 81), (172, 103), (26, 103)] 0.994091 -[15]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.960498 -Saved image to ../data/ppocr-det-out.png -Benchmark time=3.540086219s, count=100, average total time=35.400862ms +Model first run speed: inference=49.443453ms, post processing=4.237269ms, total time=53.680722ms +Saved image to ../../data/ppocr-det-out.png +[0]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.978851 +[1]: [(29, 430), (370, 429), (370, 443), (29, 444)] 0.936015 +[2]: [(26, 396), (362, 396), (362, 414), (26, 414)] 0.949735 +[3]: [(369, 368), (476, 368), (476, 388), (369, 388)] 0.977374 +[4]: [(27, 366), (282, 365), (282, 384), (27, 385)] 0.908594 +[5]: [(25, 334), (343, 334), (343, 352), (25, 352)] 0.953618 +[6]: [(26, 303), (252, 303), (252, 320), (26, 320)] 0.977526 +[7]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990133 +[8]: [(25, 240), (242, 240), (242, 259), (25, 259)] 0.988332 +[9]: [(413, 233), (429, 233), (429, 304), (413, 304)] 0.967471 +[10]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.998661 +[11]: [(26, 178), (301, 178), (301, 195), (26, 195)] 0.992206 +[12]: [(28, 143), (280, 144), (280, 163), (28, 162)] 0.957155 +[13]: [(27, 112), (332, 113), (332, 134), (27, 133)] 0.902135 +[14]: [(26, 81), (171, 81), (171, 103), (26, 103)] 0.995144 +[15]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.959944 +Benchmark time=3.270392909s, count=100, average total time=32.703929ms done ``` @@ -85,10 +85,25 @@ docker run --rm \ -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \ -w /go/src/app \ swdee/go-rknnlite:latest \ - go run ./example/ppocr/detect/detect.go + go run ./example/ppocr/detect/detect.go -p rk3588 ``` +### Benchmarks + +The following table shows a comparison of the benchmark results across the three distinct platforms. + + +| Platform | Execution Time | Average Inference Time Per Image | +|----------|----------------|----------------------------------| +| rk3588 | 3.27s | 32.70ms | +| rk3576 | 2.95s | 29.52ms | +| rk3566 | 5.75s | 57.51ms | + +Note that these examples are only using a single NPU core to run inference on. The results +would be different when running a Pool of models using all NPU cores available. Secondly +the Rock 4D (rk3576) has DDR5 memory versus the Rock 5B (rk3588) with slower DDR4 memory. + ### Background @@ -101,23 +116,23 @@ This PPOCR Detect example is a Go conversion of the [C API example](https://gith ### Usage -Run the PPOCR Recognition example. +Run the PPOCR Recognition example on rk3588 or replace with your Platform model. ``` cd example/ppocr/recognise -go run recognise.go +go run recognise.go -p rk3588 ``` This will result in the output of: ``` -Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11) +Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33) Model Input Number: 1, Ouput Number: 1 Input tensors: index=0, name=x, n_dims=4, dims=[1, 48, 320, 3], n_elems=46080, size=92160, fmt=NHWC, type=FP16, qnt_type=AFFINE, zp=0, scale=1.000000 Output tensors: index=0, name=softmax_11.tmp_0, n_dims=3, dims=[1, 40, 6625, 0], n_elems=265000, size=530000, fmt=UNDEFINED, type=FP16, qnt_type=AFFINE, zp=0, scale=1.000000 -Model first run speed: inference=26.486118ms, post processing=461.404µs, total time=26.947522ms +Model first run speed: inference=31.240498ms, post processing=494.659µs, total time=31.735157ms Recognize result: JOINT, score=0.71 -Benchmark time=2.528564774s, count=100, average total time=25.285647ms +Benchmark time=1.655360827s, count=100, average total time=16.553608ms done ``` @@ -147,7 +162,7 @@ docker run --rm \ -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \ -w /go/src/app \ swdee/go-rknnlite:latest \ - go run ./example/ppocr/recognise/recognise.go + go run ./example/ppocr/recognise/recognise.go -p rk3588 ``` @@ -246,6 +261,23 @@ is a [discussion](https://github.com/PaddlePaddle/PaddleOCR/issues/11859) on how to improve the situation. Hopefully the other languages get updates to v4 models in the future. + +### Benchmarks + +The following table shows a comparison of the benchmark results across the three distinct platforms. + + +| Platform | Execution Time | Average Inference Time Per Image | +|----------|----------------|----------------------------------| +| rk3588 | 1.65s | 16.55ms | +| rk3576 | 2.34s | 23.40ms | +| rk3566 | 5.93s | 59.35ms | + +Note that these examples are only using a single NPU core to run inference on. The results +would be different when running a Pool of models using all NPU cores available. + + + ### Background This PPOCR Recognise example is a Go conversion of the [C API example](https://github.com/airockchip/rknn_model_zoo/blob/main/examples/PPOCR/PPOCR-Rec/cpp/main.cc). @@ -260,62 +292,62 @@ This PPOCR Recognise example is a Go conversion of the [C API example](https://g ### Usage -Run the PPOCR System example. +Run the PPOCR System example on rk3588 or replace with your Platform model. ``` cd example/ppocr/system -go run system.go +go run system.go -p rk3588 ``` This will result in the output of: ``` -Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11) +Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33) Model Input Number: 1, Ouput Number: 1 Input tensors: index=0, name=x, n_dims=4, dims=[1, 48, 320, 3], n_elems=46080, size=92160, fmt=NHWC, type=FP16, qnt_type=AFFINE, zp=0, scale=1.000000 Output tensors: index=0, name=softmax_11.tmp_0, n_dims=3, dims=[1, 40, 6625, 0], n_elems=265000, size=530000, fmt=UNDEFINED, type=FP16, qnt_type=AFFINE, zp=0, scale=1.000000 -Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11) +Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33) Model Input Number: 1, Ouput Number: 1 Input tensors: index=0, name=x, n_dims=4, dims=[1, 480, 480, 3], n_elems=691200, size=691200, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-14, scale=0.018658 Output tensors: index=0, name=sigmoid_0.tmp_0, n_dims=4, dims=[1, 1, 480, 480], n_elems=230400, size=230400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922 -[0]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.960498 +[0]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.959944 Recognize result: 纯臻营养护发素, score=0.71 -[1]: [(26, 81), (172, 81), (172, 103), (26, 103)] 0.994091 +[1]: [(26, 81), (171, 81), (171, 103), (26, 103)] 0.995144 +[2]: [(27, 112), (332, 113), (332, 134), (27, 133)] 0.902135 Recognize result: 产品信息/参数, score=0.71 -[2]: [(27, 112), (333, 113), (333, 135), (27, 134)] 0.899956 Recognize result: （45元/每公斤，100公斤起订）, score=0.69 -[3]: [(28, 143), (280, 144), (280, 164), (28, 163)] 0.974824 -Recognize result: 每瓶22元，1000瓶起订）, score=0.70 -[4]: [(26, 178), (300, 179), (300, 196), (26, 195)] 0.991055 -Recognize result: （品牌】：代加工方式/OEMODM, score=0.67 -[5]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.995540 -Recognize result: 【品名】：纯臻营养护发素, score=0.70 -[6]: [(26, 240), (242, 240), (242, 259), (26, 259)] 0.986159 +[3]: [(28, 143), (280, 144), (280, 163), (28, 162)] 0.957155 +Recognize result: 每瓶22元，1000瓶起订）, score=0.68 +[4]: [(26, 178), (301, 178), (301, 195), (26, 195)] 0.992206 +Recognize result: 【品牌】：代加工方式/OEMODM, score=0.64 +[5]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.998661 +Recognize result: 【品名】：纯臻营养护发素, score=0.71 +[6]: [(25, 240), (242, 240), (242, 259), (25, 259)] 0.988332 +[7]: [(413, 233), (429, 233), (429, 304), (413, 304)] 0.967471 Recognize result: 【产品编号】：YM-X-3011, score=0.71 -[7]: [(413, 233), (429, 233), (429, 305), (413, 305)] 0.970001 -Recognize result: ODMOEM, score=0.71 -[8]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990559 +[8]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990133 +Recognize result: ODMOEM, score=0.70 +[9]: [(26, 303), (252, 303), (252, 320), (26, 320)] 0.977526 Recognize result: 【净含量】：220ml, score=0.71 -[9]: [(26, 303), (253, 303), (253, 320), (26, 320)] 0.974053 Recognize result: 【适用人群】：适合所有肤质, score=0.71 -[10]: [(26, 334), (342, 334), (342, 352), (26, 352)] 0.956719 -Recognize result: （主要成分》:皖蜡硬脂醇、燕麦-葡聚, score=0.59 -[11]: [(27, 365), (282, 365), (282, 384), (27, 384)] 0.975041 -Recognize result: 糖、椰油酰胺丙基甜菜碱、泛酸, score=0.68 -[12]: [(368, 368), (476, 368), (476, 388), (368, 388)] 0.989887 -Recognize result: （成品包材）, score=0.71 -[13]: [(28, 398), (361, 397), (361, 413), (28, 414)] 0.953752 -Recognize result: 干型功能:可降较以发确员，从而大有, score=0.41 -[14]: [(27, 428), (371, 427), (371, 444), (27, 445)] 0.912538 -Recognize result: 即时语久改基发光器的双果，给干强的头, score=0.47 -[15]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.991298 +[10]: [(25, 334), (343, 334), (343, 352), (25, 352)] 0.953618 +[11]: [(27, 366), (282, 365), (282, 384), (27, 385)] 0.908594 +Recognize result: 【主要成分】:皖蜡硬脂醇、燕麦β-葡聚, score=0.62 +[12]: [(369, 368), (476, 368), (476, 388), (369, 388)] 0.977374 +Recognize result: 糖、椰油酰胺丙基甜菜碱、泛酯, score=0.67 +Recognize result: （成品包材）, score=0.70 +[13]: [(26, 396), (362, 396), (362, 414), (26, 414)] 0.949735 +[14]: [(29, 430), (370, 429), (370, 443), (29, 444)] 0.936015 +Recognize result: 【主要功能】：可紧致头发磷层，从而达到, score=0.66 +[15]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.978851 +Recognize result: RA V型S发研至NW果 治 N2, score=0.27 Recognize result: 发足够的滋养, score=0.71 Run speed: - Detect processing=32.056505ms - Recognise processing=362.731907ms - Total time=394.788412ms + Detect processing=58.256248ms + Recognise processing=264.197813ms + Total time=322.454061ms done ``` @@ -338,11 +370,10 @@ docker run --rm \ -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \ -w /go/src/app \ swdee/go-rknnlite:latest \ - go run ./example/ppocr/system/system.go + go run ./example/ppocr/system/system.go -p rk3588 ``` - ### Background This PPOCR System example is a Go conversion of the [C API example](https://github.com/airockchip/rknn_model_zoo/blob/main/examples/PPOCR/PPOCR-System/cpp/main.cc). diff --git a/example/ppocr/detect/detect.go b/example/ppocr/detect/detect.go index c030c7f..e391311 100644 --- a/example/ppocr/detect/detect.go +++ b/example/ppocr/detect/detect.go @@ -12,6 +12,7 @@ import ( "image/color" "log" "os" + "strings" "time" ) @@ -20,19 +21,26 @@ func main() { log.SetFlags(0) // read in cli flags - modelFile := flag.String("m", "../../data/ppocrv4_det-rk3588.rknn", "RKNN compiled model file") + modelFile := flag.String("m", "../../data/models/rk3588/ppocrv4_det-rk3588.rknn", "RKNN compiled model file") imgFile := flag.String("i", "../../data/ppocr-det-test.png", "Image file to run inference on") saveFile := flag.String("o", "../../data/ppocr-det-out.png", "The output PNG file with object detection markers") + rkPlatform := flag.String("p", "rk3588", "Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588]") flag.Parse() - err := rknnlite.SetCPUAffinity(rknnlite.RK3588FastCores) + err := rknnlite.SetCPUAffinityByPlatform(*rkPlatform, rknnlite.FastCores) if err != nil { log.Printf("Failed to set CPU Affinity: %v\n", err) } + // check if user specified model file or if default is being used. if default + // then pick the default platform model to use. + if f := flag.Lookup("m"); f != nil && f.Value.String() == f.DefValue && *rkPlatform != "rk3588" { + *modelFile = strings.ReplaceAll(*modelFile, "rk3588", *rkPlatform) + } + // create rknn runtime instance - rt, err := rknnlite.NewRuntime(*modelFile, rknnlite.NPUCoreAuto) + rt, err := rknnlite.NewRuntimeByPlatform(*rkPlatform, *modelFile) if err != nil { log.Fatal("Error initializing RKNN runtime: ", err) diff --git a/example/ppocr/recognise/recognise.go b/example/ppocr/recognise/recognise.go index ae71003..f2b527c 100644 --- a/example/ppocr/recognise/recognise.go +++ b/example/ppocr/recognise/recognise.go @@ -12,6 +12,7 @@ import ( "gocv.io/x/gocv" "log" "os" + "strings" "time" ) @@ -20,19 +21,26 @@ func main() { log.SetFlags(0) // read in cli flags - modelFile := flag.String("m", "../../data/ppocrv4_rec-rk3588.rknn", "RKNN compiled model file") + modelFile := flag.String("m", "../../data/models/rk3588/ppocrv4_rec-rk3588.rknn", "RKNN compiled model file") imgFile := flag.String("i", "../../data/ppocr-rec-test.png", "Image file to run inference on") keysFile := flag.String("k", "../../data/ppocr_keys_v1.txt", "Text file containing OCR character keys") + rkPlatform := flag.String("p", "rk3588", "Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588]") flag.Parse() - err := rknnlite.SetCPUAffinity(rknnlite.RK3588FastCores) + err := rknnlite.SetCPUAffinityByPlatform(*rkPlatform, rknnlite.FastCores) if err != nil { log.Printf("Failed to set CPU Affinity: %v\n", err) } + // check if user specified model file or if default is being used. if default + // then pick the default platform model to use. + if f := flag.Lookup("m"); f != nil && f.Value.String() == f.DefValue && *rkPlatform != "rk3588" { + *modelFile = strings.ReplaceAll(*modelFile, "rk3588", *rkPlatform) + } + // create rknn runtime instance - rt, err := rknnlite.NewRuntime(*modelFile, rknnlite.NPUCoreAuto) + rt, err := rknnlite.NewRuntimeByPlatform(*rkPlatform, *modelFile) if err != nil { log.Fatal("Error initializing RKNN runtime: ", err) diff --git a/example/ppocr/system/system.go b/example/ppocr/system/system.go index 3599abf..c564387 100644 --- a/example/ppocr/system/system.go +++ b/example/ppocr/system/system.go @@ -13,6 +13,7 @@ import ( "math" "os" "sort" + "strings" "time" ) @@ -21,26 +22,36 @@ func main() { log.SetFlags(0) // read in cli flags - detectModelFile := flag.String("d", "../../data/ppocrv4_det-rk3588.rknn", "RKNN compiled model file for OCR Detection") - recogniseModelFile := flag.String("r", "../../data/ppocrv4_rec-rk3588.rknn", "RKNN compiled model file for OCR Recognition") + detectModelFile := flag.String("d", "../../data/models/rk3588/ppocrv4_det-rk3588.rknn", "RKNN compiled model file for OCR Detection") + recogniseModelFile := flag.String("r", "../../data/models/rk3588/ppocrv4_rec-rk3588.rknn", "RKNN compiled model file for OCR Recognition") keysFile := flag.String("k", "../../data/ppocr_keys_v1.txt", "Text file containing OCR character keys") imgFile := flag.String("i", "../../data/ppocr-det-test.png", "Image file to run inference on") + rkPlatform := flag.String("p", "rk3588", "Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588]") flag.Parse() - err := rknnlite.SetCPUAffinity(rknnlite.RK3588FastCores) + err := rknnlite.SetCPUAffinityByPlatform(*rkPlatform, rknnlite.FastCores) if err != nil { log.Printf("Failed to set CPU Affinity: %v\n", err) } + // check if user specified model file or if default is being used. if default + // then pick the default platform model to use. + if f := flag.Lookup("d"); f != nil && f.Value.String() == f.DefValue && *rkPlatform != "rk3588" { + *detectModelFile = strings.ReplaceAll(*detectModelFile, "rk3588", *rkPlatform) + } + if f := flag.Lookup("r"); f != nil && f.Value.String() == f.DefValue && *rkPlatform != "rk3588" { + *recogniseModelFile = strings.ReplaceAll(*recogniseModelFile, "rk3588", *rkPlatform) + } + // create rknn runtime instance - detectRt, err := rknnlite.NewRuntime(*detectModelFile, rknnlite.NPUCoreAuto) + detectRt, err := rknnlite.NewRuntimeByPlatform(*rkPlatform, *detectModelFile) if err != nil { log.Fatal("Error initializing Detect RKNN runtime: ", err) } - recogniseRt, err := rknnlite.NewRuntime(*recogniseModelFile, rknnlite.NPUCoreAuto) + recogniseRt, err := rknnlite.NewRuntimeByPlatform(*rkPlatform, *recogniseModelFile) if err != nil { log.Fatal("Error initializing Recognise RKNN runtime: ", err)