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/OK3568_Linux_fs/buildroot/package/rockchip/rknn_demo/
H A Drknn_demo.mk20 $(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api.so $(TARGET_DIR)/usr/lib
21 $(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api.so $(STAGING_DIR)/usr/lib
54 RKNN_MODEL_RESOURCE_FILES = rknn/joint/cpm.rknn
58 RKNN_MODEL_RESOURCE_FILES = rknn/frg/frgsdk_rk1808/model/align.rknn \
59 rknn/frg/frgsdk_rk1808/model/detect.rknn \
60 rknn/frg/frgsdk_rk1808/model/recognize.rknn
65 RKNN_MODEL_RESOURCE_FILES = rknn/ssd/ssd_1808/ssd_inception_v2.rknn \
66 rknn/ssd/ssd_1808/coco_labels_list.txt \
67 rknn/ssd/ssd_1808/box_priors.txt
69 RKNN_MODEL_RESOURCE_FILES = rknn/ssd/ssd_3399pro/mobilenet_ssd.rknn \
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/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/board_test/
H A Dtest.py3 from rknn.api import RKNN
27 rknn = RKNN(verbose=True) variable
31 rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82],
37 ret = rknn.load_caffe(model='./../../caffe/mobilenet_v2/mobilenet_v2.prototxt',
46 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
54 ret = rknn.export_rknn('./mobilenet_v2.rknn')
62 ret = rknn.export_encrypted_rknn_model('./mobilenet_v2.rknn', None, 3)
66 ret = rknn.load_rknn('./mobilenet_v2.rknn')
76 rknn.list_devices()
80 ret = rknn.init_runtime(target='rk3588', perf_debug=True, eval_mem=True)
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/OK3568_Linux_fs/docs/cn/Common/NPU/rknn-toolkit2/
H A DRKNNToolKit2_API_Difference_With_Toolkit1-1.4.0.md3 ## rknn.config
73 ## rknn.load_tensorflow
101 ## rknn.load_caffe
114 ## rknn.load_keras
122 ## rknn.load_pytorch
135 ## rknn.load_mxnet
143 ## rknn.build
157 ## rknn.direct_build
165 ## rknn.hybrid_quantization_step1
177 ## rknn.hybrid_quantization_step2
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/OK3568_Linux_fs/external/rknn-toolkit2/doc/
H A DRKNNToolKit2_API_Difference_With_Toolkit1-1.5.0.md3 ## rknn.config
76 ## rknn.load_tensorflow
104 ## rknn.load_caffe
117 ## rknn.load_keras
125 ## rknn.load_pytorch
138 ## rknn.load_mxnet
146 ## rknn.build
160 ## rknn.direct_build
168 ## rknn.hybrid_quantization_step1
180 ## rknn.hybrid_quantization_step2
[all …]
/OK3568_Linux_fs/docs/en/Common/NPU/rknn-toolkit2/
H A DRKNNToolKit2_API_Difference_With_Toolkit1-1.4.0.md3 ## rknn.config
73 ## rknn.load_tensorflow
101 ## rknn.load_caffe
114 ## rknn.load_keras
122 ## rknn.load_pytorch
135 ## rknn.load_mxnet
143 ## rknn.build
157 ## rknn.direct_build
165 ## rknn.hybrid_quantization_step1
177 ## rknn.hybrid_quantization_step2
[all …]
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/multi_input_test/
H A Dtest.py3 from rknn.api import RKNN
8 rknn = RKNN(verbose=True) variable
12 rknn.config(mean_values=[[127.5, 127.5, 127.5], [0, 0, 0], [0, 0, 0], [127.5]],
18 ret = rknn.load_tensorflow(tf_pb='./conv_128.pb',
29 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
37 ret = rknn.export_rknn('./conv_128.rknn')
45 ret = rknn.init_runtime()
64 …outputs = rknn.inference(inputs=[img, input2, input3, img_gray], data_format=['nhwc', 'nchw', 'nch…
70 rknn.release()
/OK3568_Linux_fs/docs/cn/Common/NPU/
H A DREADME.md32 - rknn-toolkit适用RV1109/RV1126/RK1808/RK3399Pro，rknn-toolkit2适用RK356X/RK3588/RV1103/RV1106
33 - rknn-toolkit2与rknn-toolkit API接口基本保持一致，用户不需要太多修改（rknn.config()部分参数有删减）
34 - rknpu2需要与rknn-toolkit2同步升级到1.4.0的版本。之前客户使用rknn toolkit2 1.3.0版本生成的rknn模型建议重新生成
35 - rknn api里面部分demo依赖MPI MMZ/RGA，使用时，需要和系统中相应的库匹配
/OK3568_Linux_fs/external/rknn-toolkit2/examples/tflite/mobilenet_v1_qat/
H A Dtest.py3 from rknn.api import RKNN
27 rknn = RKNN(verbose=True) variable
31 rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128])
36 ret = rknn.load_tflite(model='mobilenet_v1_1.0_224_quant.tflite')
44 ret = rknn.build(do_quantization=False)
52 ret = rknn.export_rknn('./mobilenet_v1.rknn')
65 ret = rknn.init_runtime()
73 outputs = rknn.inference(inputs=[img])
78 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/caffe/mobilenet_v2/
H A Dtest.py3 from rknn.api import RKNN
28 rknn = RKNN(verbose=True) variable
32 …rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2…
37 ret = rknn.load_caffe(model='./mobilenet_v2.prototxt',
46 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
54 ret = rknn.export_rknn('./mobilenet_v2.rknn')
65 ret = rknn.init_runtime()
73 outputs = rknn.inference(inputs=[img])
77 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/model_pruning/
H A Dtest.py3 from rknn.api import RKNN
28 rknn = RKNN(verbose=True) variable
32 …rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2…
37 ret = rknn.load_caffe(model='./mobilenet_deploy.prototxt',
46 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
71 ret = rknn.export_rknn('./mobilenet.rknn')
82 ret = rknn.init_runtime()
90 outputs = rknn.inference(inputs=[img])
94 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/dynamic_input/
H A Dtest.py3 from rknn.api import RKNN
33 rknn = RKNN(verbose=True) variable
45 …rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2…
50 ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2.prototxt',
59 ret = rknn.build(do_quantization=True, dataset='../../caffe/mobilenet_v2/dataset.txt')
67 ret = rknn.export_rknn('./mobilenet_v2.rknn')
75 ret = rknn.init_runtime()
89 outputs = rknn.inference(inputs=[img2], data_format=['nchw'])
96 outputs = rknn.inference(inputs=[img3], data_format=['nchw'])
101 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/tflite/mobilenet_v1/
H A Dtest.py3 from rknn.api import RKNN
27 rknn = RKNN(verbose=True) variable
31 rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128])
36 ret = rknn.load_tflite(model='mobilenet_v1_1.0_224.tflite')
44 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
52 ret = rknn.export_rknn('./mobilenet_v1.rknn')
65 ret = rknn.init_runtime()
73 outputs = rknn.inference(inputs=[img])
78 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/batch_size/
H A Dtest.py3 from rknn.api import RKNN
33 rknn = RKNN(verbose=True) variable
37 …rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2…
42 ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2.prototxt',
51 ret = rknn.build(do_quantization=True, dataset='./dataset.txt', rknn_batch_size=4)
59 ret = rknn.export_rknn('./mobilenet_v2.rknn')
73 ret = rknn.init_runtime()
81 outputs = rknn.inference(inputs=[img])
86 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/hybrid_quant/
H A Dstep2.py3 from rknn.api import RKNN
9 rknn = RKNN(verbose=True) variable
13 ret = rknn.hybrid_quantization_step2(model_input='./ssd_mobilenet_v2.model',
23 ret = rknn.export_rknn('./ssd_mobilenet_v2.rknn')
31 ret = rknn.accuracy_analysis(inputs=['./dog_bike_car_300x300.jpg'], output_dir=None)
43 ret = rknn.init_runtime()
51 outputs = rknn.inference(inputs=[img])
57 rknn.release()
H A Dstep1.py3 from rknn.api import RKNN
8 rknn = RKNN(verbose=True) variable
12 rknn.config(mean_values=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5])
17 ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v2.pb',
28 ret = rknn.hybrid_quantization_step1(dataset='./dataset.txt', proposal=False)
50 rknn.release()
/OK3568_Linux_fs/external/rknpu2/examples/rknn_yolov5_demo/convert_rknn_demo/yolov5/
H A Donnx2rknn.py5 from rknn.api import RKNN
20 rknn = RKNN() variable
27 rknn.config(mean_values=[[0, 0, 0]], std_values=[
31 ret = rknn.load_onnx(MODEL_PATH)
39 ret = rknn.build(do_quantization=True, dataset=DATASET)
49 ret = rknn.export_rknn(RKNN_MODEL_PATH)
55 ret = rknn.load_rknn(RKNN_MODEL_PATH)
57 rknn.release()
/OK3568_Linux_fs/external/rknpu2/examples/RV1106_RV1103/rknn_yolov5_demo/convert_rknn_demo/yolov5/
H A Donnx2rknn.py5 from rknn.api import RKNN
19 rknn = RKNN() variable
25 rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform="rv1106")
28 ret = rknn.load_onnx(MODEL_PATH, outputs=['334', '353', '372'])
36 ret = rknn.build(do_quantization=True, dataset=DATASET)
46 ret = rknn.export_rknn(RKNN_MODEL_PATH)
52 ret = rknn.load_rknn(RKNN_MODEL_PATH)
54 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/functions/mmse/
H A Dtest.py3 from rknn.api import RKNN
27 rknn = RKNN(verbose=True) variable
31 rknn.config(mean_values=[128, 128, 128], std_values=[128, 128, 128],
37 ret = rknn.load_tensorflow(tf_pb='mobilenet_v1.pb',
48 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
56 ret = rknn.accuracy_analysis(inputs=['dog_224x224.jpg'], output_dir=None)
78 ret = rknn.init_runtime()
86 outputs = rknn.inference(inputs=[img])
91 rknn.release()
/OK3568_Linux_fs/buildroot/package/rockchip/rknpu/
H A Drknpu.mk54 mkdir -p $(STAGING_DIR)/usr/include/rknn
55 …$(INSTALL) -D -m 0644 $(@D)/rknn/include/rknn_runtime.h $(STAGING_DIR)/usr/include/rknn/rknn_runti…
57 …$(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api/include/rknn_api.h $(STAGING_DIR)/usr/includ…
82 cp -r $(@D)/rknn/python/rknn $(TARGET_DIR)/usr/lib/python3.6/site-packages/; \
87 …$(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api/lib64/librknn_api.so $(TARGET_DIR)/usr/lib/;…
88 …$(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api/lib64/librknn_api.so $(STAGING_DIR)/usr/lib;…
90 … $(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api/lib/librknn_api.so $(TARGET_DIR)/usr/lib/; \
91 … $(INSTALL) -D -m 0644 $(@D)/rknn/rknn_api/librknn_api/lib/librknn_api.so $(STAGING_DIR)/usr/lib; \
/OK3568_Linux_fs/external/rknn-toolkit2/examples/pytorch/resnet18/
H A Dtest.py3 from rknn.api import RKNN
51 rknn = RKNN(verbose=True) variable
55 rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395])
60 ret = rknn.load_pytorch(model=model, input_size_list=input_size_list)
68 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
76 ret = rknn.export_rknn('./resnet_18.rknn')
88 ret = rknn.init_runtime()
96 outputs = rknn.inference(inputs=[img])
101 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/pytorch/resnet18_qat/
H A Dtest.py3 from rknn.api import RKNN
53 rknn = RKNN(verbose=True) variable
57 rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395])
62 ret = rknn.load_pytorch(model=model, input_size_list=input_size_list)
70 ret = rknn.build(do_quantization=False)
78 ret = rknn.export_rknn('./resnet_18.rknn')
90 ret = rknn.init_runtime()
98 outputs = rknn.inference(inputs=[img])
103 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/darknet/yolov3_416x416/
H A Dtest.py3 from rknn.api import RKNN
25 rknn = RKNN(verbose=True) variable
29 rknn.config(mean_values=[0, 0, 0], std_values=[255, 255, 255])
34 ret = rknn.load_darknet(model=MODEL_PATH, weight=WEIGHT_PATH)
42 ret = rknn.build(do_quantization=True, dataset=DATASET)
50 ret = rknn.export_rknn(RKNN_MODEL_PATH)
62 ret = rknn.init_runtime()
70 outputs = rknn.inference(inputs=[img])
98 rknn.release()
/OK3568_Linux_fs/external/rknpu2/examples/rknn_benchmark/
H A DREADME.md1 rknn_benchmark is used to test the performance of the rknn model. Please make sure that the cpu/ddr…
5 ./rknn_benchmark xxx.rknn [input_data] [loop_count] [core_mask]
18 ./rknn_benchmark mobilenet_v1.rknn
19 ./rknn_benchmark mobilenet_v1.rknn dog.jpg 10 3
20 ./rknn_benchmark mobilenet_v1.rknn dog.npy 10 7
21 ./rknn_benchmark xxx.rknn input1.npy#input2.npy
44 Connect device and push the program and rknn model to `/userdata`
50 - If your board has sshd service, you can use scp or other methods to copy the program and rknn mod…
61 ./rknn_benchmark xxx.rknn
90 ./rknn_benchmark xxx.rknn
/OK3568_Linux_fs/external/rknn-toolkit2/examples/onnx/resnet50v2/
H A Dtest.py8 from rknn.api import RKNN
64 rknn = RKNN(verbose=True) variable
84 rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82])
89 ret = rknn.load_onnx(model=ONNX_MODEL)
97 ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
105 ret = rknn.export_rknn(RKNN_MODEL)
117 ret = rknn.init_runtime()
125 outputs = rknn.inference(inputs=[img])
133 rknn.release()
/OK3568_Linux_fs/external/rknn-toolkit2/examples/tensorflow/inception_v3_qat/
H A Dtest.py10 from rknn.api import RKNN
70 rknn = RKNN(verbose=True) variable
107 rknn.config(mean_values=[104, 117, 123], std_values=[128, 128, 128])
112 ret = rknn.load_tensorflow(tf_pb=PB_FILE,
123 ret = rknn.build(do_quantization=False)
131 ret = rknn.export_rknn(RKNN_MODEL_PATH)
143 ret = rknn.init_runtime()
151 outputs = rknn.inference(inputs=[img])
159 rknn.release()

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