mirror of
https://github.com/swdee/go-rknnlite.git
synced 2025-12-24 10:30:56 +08:00
adjusted PPOCR example to work across all rk35xx models
This commit is contained in:
swdee
@@ -29,39 +29,39 @@ git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data
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### Usage
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Run the PPOCR Detection example.
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Run the PPOCR Detection example on rk3588 or replace with your Platform model.
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```
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cd example/ppocr/detect
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go run detect.go
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go run detect.go -p rk3588
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```
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This will result in the output of:
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```
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Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
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Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
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Model Input Number: 1, Ouput Number: 1
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Input tensors:
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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
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Output tensors:
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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
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Model first run speed: inference=27.746374ms, post processing=2.968795ms, total time=30.715169ms
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[0]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.991298
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[1]: [(27, 428), (371, 427), (371, 444), (27, 445)] 0.912538
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[2]: [(28, 398), (361, 397), (361, 413), (28, 414)] 0.953752
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[3]: [(368, 368), (476, 368), (476, 388), (368, 388)] 0.989887
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[4]: [(27, 365), (282, 365), (282, 384), (27, 384)] 0.975041
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[5]: [(26, 334), (342, 334), (342, 352), (26, 352)] 0.956719
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[6]: [(26, 303), (253, 303), (253, 320), (26, 320)] 0.974053
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[7]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990559
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[8]: [(26, 240), (242, 240), (242, 259), (26, 259)] 0.986159
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[9]: [(413, 233), (429, 233), (429, 305), (413, 305)] 0.970001
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[10]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.995540
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[11]: [(26, 178), (300, 179), (300, 196), (26, 195)] 0.991055
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[12]: [(28, 143), (280, 144), (280, 164), (28, 163)] 0.974824
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[13]: [(27, 112), (333, 113), (333, 135), (27, 134)] 0.899956
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[14]: [(26, 81), (172, 81), (172, 103), (26, 103)] 0.994091
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[15]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.960498
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Saved image to ../data/ppocr-det-out.png
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Benchmark time=3.540086219s, count=100, average total time=35.400862ms
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Model first run speed: inference=49.443453ms, post processing=4.237269ms, total time=53.680722ms
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Saved image to ../../data/ppocr-det-out.png
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[0]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.978851
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[1]: [(29, 430), (370, 429), (370, 443), (29, 444)] 0.936015
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[2]: [(26, 396), (362, 396), (362, 414), (26, 414)] 0.949735
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[3]: [(369, 368), (476, 368), (476, 388), (369, 388)] 0.977374
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[4]: [(27, 366), (282, 365), (282, 384), (27, 385)] 0.908594
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[5]: [(25, 334), (343, 334), (343, 352), (25, 352)] 0.953618
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[6]: [(26, 303), (252, 303), (252, 320), (26, 320)] 0.977526
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[7]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990133
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[8]: [(25, 240), (242, 240), (242, 259), (25, 259)] 0.988332
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[9]: [(413, 233), (429, 233), (429, 304), (413, 304)] 0.967471
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[10]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.998661
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[11]: [(26, 178), (301, 178), (301, 195), (26, 195)] 0.992206
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[12]: [(28, 143), (280, 144), (280, 163), (28, 162)] 0.957155
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[13]: [(27, 112), (332, 113), (332, 134), (27, 133)] 0.902135
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[14]: [(26, 81), (171, 81), (171, 103), (26, 103)] 0.995144
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[15]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.959944
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Benchmark time=3.270392909s, count=100, average total time=32.703929ms
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done
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```
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@@ -85,10 +85,25 @@ docker run --rm \
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-v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
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-w /go/src/app \
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swdee/go-rknnlite:latest \
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go run ./example/ppocr/detect/detect.go
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go run ./example/ppocr/detect/detect.go -p rk3588
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```
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### Benchmarks
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The following table shows a comparison of the benchmark results across the three distinct platforms.
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| Platform | Execution Time | Average Inference Time Per Image |
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|----------|----------------|----------------------------------|
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| rk3588 | 3.27s | 32.70ms |
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| rk3576 | 2.95s | 29.52ms |
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| rk3566 | 5.75s | 57.51ms |
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Note that these examples are only using a single NPU core to run inference on. The results
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would be different when running a Pool of models using all NPU cores available. Secondly
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the Rock 4D (rk3576) has DDR5 memory versus the Rock 5B (rk3588) with slower DDR4 memory.
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### Background
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@@ -101,23 +116,23 @@ This PPOCR Detect example is a Go conversion of the [C API example](https://gith
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### Usage
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Run the PPOCR Recognition example.
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Run the PPOCR Recognition example on rk3588 or replace with your Platform model.
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```
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cd example/ppocr/recognise
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go run recognise.go
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go run recognise.go -p rk3588
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```
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This will result in the output of:
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```
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Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
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Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
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Model Input Number: 1, Ouput Number: 1
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Input tensors:
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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
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Output tensors:
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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
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Model first run speed: inference=26.486118ms, post processing=461.404µs, total time=26.947522ms
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Model first run speed: inference=31.240498ms, post processing=494.659µs, total time=31.735157ms
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Recognize result: JOINT, score=0.71
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Benchmark time=2.528564774s, count=100, average total time=25.285647ms
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Benchmark time=1.655360827s, count=100, average total time=16.553608ms
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done
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```
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@@ -147,7 +162,7 @@ docker run --rm \
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-v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
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-w /go/src/app \
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swdee/go-rknnlite:latest \
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go run ./example/ppocr/recognise/recognise.go
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go run ./example/ppocr/recognise/recognise.go -p rk3588
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```
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@@ -246,6 +261,23 @@ is a [discussion](https://github.com/PaddlePaddle/PaddleOCR/issues/11859) on how
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to improve the situation. Hopefully the other languages get updates to v4 models
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in the future.
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### Benchmarks
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The following table shows a comparison of the benchmark results across the three distinct platforms.
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| Platform | Execution Time | Average Inference Time Per Image |
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|----------|----------------|----------------------------------|
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| rk3588 | 1.65s | 16.55ms |
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| rk3576 | 2.34s | 23.40ms |
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| rk3566 | 5.93s | 59.35ms |
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Note that these examples are only using a single NPU core to run inference on. The results
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would be different when running a Pool of models using all NPU cores available.
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### Background
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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).
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@@ -260,62 +292,62 @@ This PPOCR Recognise example is a Go conversion of the [C API example](https://g
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### Usage
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Run the PPOCR System example.
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Run the PPOCR System example on rk3588 or replace with your Platform model.
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```
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cd example/ppocr/system
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go run system.go
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go run system.go -p rk3588
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```
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This will result in the output of:
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```
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Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
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Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
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Model Input Number: 1, Ouput Number: 1
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Input tensors:
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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
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Output tensors:
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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
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Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
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Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
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Model Input Number: 1, Ouput Number: 1
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Input tensors:
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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
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Output tensors:
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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
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[0]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.960498
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[0]: [(28, 38), (302, 39), (302, 71), (28, 70)] 0.959944
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Recognize result: 纯臻营养护发素, score=0.71
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[1]: [(26, 81), (172, 81), (172, 103), (26, 103)] 0.994091
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[1]: [(26, 81), (171, 81), (171, 103), (26, 103)] 0.995144
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[2]: [(27, 112), (332, 113), (332, 134), (27, 133)] 0.902135
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Recognize result: 产品信息/参数, score=0.71
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[2]: [(27, 112), (333, 113), (333, 135), (27, 134)] 0.899956
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Recognize result: （45元/每公斤，100公斤起订）, score=0.69
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[3]: [(28, 143), (280, 144), (280, 164), (28, 163)] 0.974824
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Recognize result: 每瓶22元，1000瓶起订）, score=0.70
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[4]: [(26, 178), (300, 179), (300, 196), (26, 195)] 0.991055
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Recognize result: （品牌】：代加工方式/OEMODM, score=0.67
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[5]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.995540
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Recognize result: 【品名】：纯臻营养护发素, score=0.70
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[6]: [(26, 240), (242, 240), (242, 259), (26, 259)] 0.986159
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[3]: [(28, 143), (280, 144), (280, 163), (28, 162)] 0.957155
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Recognize result: 每瓶22元，1000瓶起订）, score=0.68
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[4]: [(26, 178), (301, 178), (301, 195), (26, 195)] 0.992206
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Recognize result: 【品牌】：代加工方式/OEMODM, score=0.64
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[5]: [(26, 209), (235, 209), (235, 227), (26, 227)] 0.998661
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Recognize result: 【品名】：纯臻营养护发素, score=0.71
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[6]: [(25, 240), (242, 240), (242, 259), (25, 259)] 0.988332
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[7]: [(413, 233), (429, 233), (429, 304), (413, 304)] 0.967471
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Recognize result: 【产品编号】：YM-X-3011, score=0.71
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[7]: [(413, 233), (429, 233), (429, 305), (413, 305)] 0.970001
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Recognize result: ODMOEM, score=0.71
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[8]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990559
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[8]: [(25, 270), (179, 270), (179, 289), (25, 289)] 0.990133
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Recognize result: ODMOEM, score=0.70
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[9]: [(26, 303), (252, 303), (252, 320), (26, 320)] 0.977526
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Recognize result: 【净含量】：220ml, score=0.71
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[9]: [(26, 303), (253, 303), (253, 320), (26, 320)] 0.974053
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Recognize result: 【适用人群】：适合所有肤质, score=0.71
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[10]: [(26, 334), (342, 334), (342, 352), (26, 352)] 0.956719
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Recognize result: （主要成分》:皖蜡硬脂醇、燕麦-葡聚, score=0.59
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[11]: [(27, 365), (282, 365), (282, 384), (27, 384)] 0.975041
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Recognize result: 糖、椰油酰胺丙基甜菜碱、泛酸, score=0.68
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[12]: [(368, 368), (476, 368), (476, 388), (368, 388)] 0.989887
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Recognize result: （成品包材）, score=0.71
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[13]: [(28, 398), (361, 397), (361, 413), (28, 414)] 0.953752
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Recognize result: 干型功能:可降较以发确员，从而大有, score=0.41
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[14]: [(27, 428), (371, 427), (371, 444), (27, 445)] 0.912538
|
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Recognize result: 即时语久改基发光器的双果，给干强的头, score=0.47
|
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[15]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.991298
|
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[10]: [(25, 334), (343, 334), (343, 352), (25, 352)] 0.953618
|
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[11]: [(27, 366), (282, 365), (282, 384), (27, 385)] 0.908594
|
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Recognize result: 【主要成分】:皖蜡硬脂醇、燕麦β-葡聚, score=0.62
|
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[12]: [(369, 368), (476, 368), (476, 388), (369, 388)] 0.977374
|
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Recognize result: 糖、椰油酰胺丙基甜菜碱、泛酯, score=0.67
|
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Recognize result: （成品包材）, score=0.70
|
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[13]: [(26, 396), (362, 396), (362, 414), (26, 414)] 0.949735
|
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[14]: [(29, 430), (370, 429), (370, 443), (29, 444)] 0.936015
|
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Recognize result: 【主要功能】：可紧致头发磷层，从而达到, score=0.66
|
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[15]: [(27, 459), (136, 459), (136, 478), (27, 478)] 0.978851
|
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Recognize result: RA V型S发研至NW果 治 N2, score=0.27
|
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Recognize result: 发足够的滋养, score=0.71
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Run speed:
|
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Detect processing=32.056505ms
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Recognise processing=362.731907ms
|
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Total time=394.788412ms
|
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Detect processing=58.256248ms
|
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Recognise processing=264.197813ms
|
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Total time=322.454061ms
|
||||
done
|
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```
|
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|
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@@ -338,11 +370,10 @@ docker run --rm \
|
||||
-v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
|
||||
-w /go/src/app \
|
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swdee/go-rknnlite:latest \
|
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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).
|
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|
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@@ -12,6 +12,7 @@ import (
|
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"image/color"
|
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"log"
|
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"os"
|
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"strings"
|
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"time"
|
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)
|
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|
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@@ -20,19 +21,26 @@ func main() {
|
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log.SetFlags(0)
|
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|
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// read in cli flags
|
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modelFile := flag.String("m", "../../data/ppocrv4_det-rk3588.rknn", "RKNN compiled model file")
|
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modelFile := flag.String("m", "../../data/models/rk3588/ppocrv4_det-rk3588.rknn", "RKNN compiled model file")
|
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imgFile := flag.String("i", "../../data/ppocr-det-test.png", "Image file to run inference on")
|
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saveFile := flag.String("o", "../../data/ppocr-det-out.png", "The output PNG file with object detection markers")
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rkPlatform := flag.String("p", "rk3588", "Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588]")
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flag.Parse()
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|
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err := rknnlite.SetCPUAffinity(rknnlite.RK3588FastCores)
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err := rknnlite.SetCPUAffinityByPlatform(*rkPlatform, rknnlite.FastCores)
|
||||
|
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if err != nil {
|
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log.Printf("Failed to set CPU Affinity: %v\n", err)
|
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}
|
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|
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// check if user specified model file or if default is being used. if default
|
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// then pick the default platform model to use.
|
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if f := flag.Lookup("m"); f != nil && f.Value.String() == f.DefValue && *rkPlatform != "rk3588" {
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*modelFile = strings.ReplaceAll(*modelFile, "rk3588", *rkPlatform)
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}
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|
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// create rknn runtime instance
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rt, err := rknnlite.NewRuntime(*modelFile, rknnlite.NPUCoreAuto)
|
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rt, err := rknnlite.NewRuntimeByPlatform(*rkPlatform, *modelFile)
|
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|
||||
if err != nil {
|
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log.Fatal("Error initializing RKNN runtime: ", err)
|
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|
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@@ -12,6 +12,7 @@ import (
|
||||
"gocv.io/x/gocv"
|
||||
"log"
|
||||
"os"
|
||||
"strings"
|
||||
"time"
|
||||
)
|
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|
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@@ -20,19 +21,26 @@ func main() {
|
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log.SetFlags(0)
|
||||
|
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// 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)
|
||||
|
||||
@@ -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)
|
||||
|
||||
Reference in New Issue
Block a user