Difference between revisions of "RK Platform NPU SDK"
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1. rknn_ssd_demo | 1. rknn_ssd_demo | ||
| + | <pre> | ||
| + | cd /tools/test/adv/npu2/rknn_ssd_demo | ||
| + | ./rknn_ssd_demo model/ssd_inception_v2.rknn model/bus.jpg | ||
| + | |||
| + | |||
| + | resize 640 640 to 300 300 | ||
| + | Loading model ... | ||
| + | rknn_init ... | ||
| + | model input num: 1, output num: 2 | ||
| + | input tensors: | ||
| + | index=0, name=Preprocessor/sub:0, n_dims=4, dims=[1, 300, 300, 3], n_elems=270000, size=270000, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=0, scale=0.007843 | ||
| + | output tensors: | ||
| + | index=0, name=concat:0, n_dims=4, dims=[1, 1917, 1, 4], n_elems=7668, size=7668, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=50, scale=0.090787 | ||
| + | index=1, name=concat_1:0, n_dims=4, dims=[1, 1917, 91, 1], n_elems=174447, size=174447, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=58, scale=0.140090 | ||
| + | rknn_run | ||
| + | loadLabelName | ||
| + | ssd - loadLabelName ./model/coco_labels_list.txt | ||
| + | loadBoxPriors | ||
| + | person @ (106 245 216 535) 0.994422 | ||
| + | bus @ (87 132 568 432) 0.991533 | ||
| + | person @ (213 231 288 511) 0.843047 | ||
| + | |||
| + | </pre> | ||
2. rknn_mobilenet_demo | 2. rknn_mobilenet_demo | ||
| + | |||
| + | <pre> | ||
| + | cd /tools/test/adv/npu2/rknn_mobilenet_demo | ||
| + | ./rknn_mobilenet_demo model/mobilenet_v1.rknn model/cat_224x224.jpg | ||
| + | |||
| + | |||
| + | |||
| + | </pre> | ||
= '''rknn-toolkit2''' = | = '''rknn-toolkit2''' = | ||
TBD | TBD | ||
Revision as of 06:59, 8 August 2023
Contents
Preface
NPU Introduce
RK3568
- Neural network acceleration engine with processing performance up to 0.8 TOPS
- Support integer 4, integer 8, integer 16, float 16, Bfloat 16 and tf32 operation
- Support deep learning frameworks: TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN, etc.
- One isolated voltage domain to support DVFS
RK3588
- Neural network acceleration engine with processing performance up to 6 TOPS
- Include triple NPU core, and support triple core co-work, dual core co-work, and work independently
- Support integer 4, integer 8, integer 16, float 16, Bfloat 16 and tf32 operation
- Embedded 384KBx3 internal buffer
- Multi-task, multi-scenario in parallel
- Support deep learning frameworks: TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN, etc.
- One isolated voltage domain to support DVFS
RKNN SDK
RKNN SDK （Password: a887）include two parts:
- rknn-toolkit2
- rknpu2
├── rknn-toolkit2
│ ├── doc
│ ├── examples
│ ├── packages
│ └── rknn_toolkit_lite2
└── rknpu2
├── doc
├── examples
└── runtime
rknpu2
'rknpu2' include documents (rknpu2/doc) and examples (rknpu2/examples) to help to fast develop AI applications using rknn model(*.rknn).
Other models (eg:Caffe、TensorFlow etc) can be translated to rknn model through 'rknn-toolkit2'.
RKNN API Library file librknnrt.so and header file rknn_api.h can be found in rknpu2/runtime.
Released BSP and images have already include NPU driver and runtime libraries.
Here is two examples built in released images:
1. rknn_ssd_demo
cd /tools/test/adv/npu2/rknn_ssd_demo ./rknn_ssd_demo model/ssd_inception_v2.rknn model/bus.jpg resize 640 640 to 300 300 Loading model ... rknn_init ... model input num: 1, output num: 2 input tensors: index=0, name=Preprocessor/sub:0, n_dims=4, dims=[1, 300, 300, 3], n_elems=270000, size=270000, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=0, scale=0.007843 output tensors: index=0, name=concat:0, n_dims=4, dims=[1, 1917, 1, 4], n_elems=7668, size=7668, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=50, scale=0.090787 index=1, name=concat_1:0, n_dims=4, dims=[1, 1917, 91, 1], n_elems=174447, size=174447, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=58, scale=0.140090 rknn_run loadLabelName ssd - loadLabelName ./model/coco_labels_list.txt loadBoxPriors person @ (106 245 216 535) 0.994422 bus @ (87 132 568 432) 0.991533 person @ (213 231 288 511) 0.843047
2. rknn_mobilenet_demo
cd /tools/test/adv/npu2/rknn_mobilenet_demo ./rknn_mobilenet_demo model/mobilenet_v1.rknn model/cat_224x224.jpg
rknn-toolkit2
TBD