adjusted LPRNet example to work across all rk35xx models

example/lprnet/README.md

@@ -11,32 +11,47 @@ cd example/
 git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data
 ```
 
-Run the LPRNet example.
+Run the LPRNet example on rk3588 or replace with your Platform model.
 ```
 cd example/lprnet
-go run lprnet.go
+go run lprnet.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=input, n_dims=4, dims=[1, 24, 94, 3], n_elems=6768, size=6768, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=0, scale=0.007843
 Output tensors:
-  index=0, name=output, n_dims=3, dims=[1, 68, 18, 0], n_elems=1224, size=1224, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=47, scale=0.911201
-Model first run speed: inference=7.787585ms, post processing=25.374µs, total time=7.812959ms
+  index=0, name=output, n_dims=3, dims=[1, 68, 18, 0], n_elems=1224, size=1224, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=50, scale=0.643529
+Model first run speed: inference=4.203128ms, post processing=30.916µs, total time=4.234044ms
 License plate recognition result: 湘F6CL03
-Benchmark time=61.070751ms, count=10, average total time=6.107075ms
+Benchmark time=350.625899ms, count=100, average total time=3.506258ms
 done
 ```
 
 To use your own RKNN compiled model and images.
 ```
-go run lprnet.go -m <RKNN model file> -i <image file>
+go run lprnet.go -m <RKNN model file> -i <image file> -p <platform>
 ```
 
+
+See the help for command line parameters.
+```
+$ go run lprnet.go --help
+
+Usage of /tmp/go-build233788912/b001/exe/lprnet:
+  -i string
+        Image file to run inference on (default "../data/lplate.jpg")
+  -m string
+        RKNN compiled model file (default "../data/models/rk3588/lprnet-rk3588.rknn")
+  -p string
+        Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588] (default "rk3588")
+```
+
+
 ### Docker
 
 To run the ALPR example using the prebuilt docker image, make sure the data files have been downloaded first,
@@ -52,7 +67,7 @@ docker run --rm \
   -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
   -w /go/src/app \
   swdee/go-rknnlite:latest \
-  go run ./example/lprnet/lprnet.go
+  go run ./example/lprnet/lprnet.go -p rk3588
 ```
 
 
@@ -64,6 +79,23 @@ the `postprocess.NewLPRNet` with your specific `LPRNetParams` containing the
 maximum length of your countries number plates and character set used.
 
 
+
+## 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   | 0.35s          | 3.50ms                           |
+| rk3576   | 0.49s          | 4.96ms                           |
+| rk3566   | 1.63s          | 16.32ms                          |
+
+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 LPRNet example is a Go conversion of the [C API Example](https://github.com/airockchip/rknn_model_zoo/blob/main/examples/LPRNet/cpp/main.cc)

example/lprnet/lprnet.go

@@ -11,6 +11,7 @@ import (
 	"image"
 	"log"
 	"os"
+	"strings"
 	"time"
 )
 
@@ -19,18 +20,25 @@ func main() {
 	log.SetFlags(0)
 
 	// read in cli flags
-	modelFile := flag.String("m", "../data/lprnet-rk3588.rknn", "RKNN compiled model file")
+	modelFile := flag.String("m", "../data/models/rk3588/lprnet-rk3588.rknn", "RKNN compiled model file")
 	imgFile := flag.String("i", "../data/lplate.jpg", "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("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)
@@ -123,7 +131,7 @@ func main() {
 func runBenchmark(rt *rknnlite.Runtime, lprnetProcesser *postprocess.LPRNet,
 	mats []gocv.Mat) {
 
-	count := 10
+	count := 100
 	start := time.Now()
 
 	for i := 0; i < count; i++ {