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update rknn-toolkit2/rknn-toolkit-lite2 to 1.3.0 

Signed-off-by: raul.rao <raul.rao@rock-chips.com>
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raul.rao 3 years ago
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  1. 20
      README.md
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      Rockchip_Quick_Start_RKNN_SDK_CN-1.2.0.pdf
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      Rockchip_Quick_Start_RKNN_SDK_V1.3.0_CN.pdf
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      doc/RKNNToolKit2_OP_Support-1.3.0.md
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      doc/Rockchip_Quick_Start_RKNN_Toolkit2_CN-1.3.0.pdf
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      doc/Rockchip_User_Guide_RKNN_Toolkit2_CN-1.2.0.pdf
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  15. 13
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  16. 2
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20
README.md
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@ -1,27 +1,31 @@ @@ -1,27 +1,31 @@
# Introduction
RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms (RK3566, RK3568, RK3588, RK3588S).
RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference and performance evaluation on PC and Rockchip NPU platforms (RK3566, RK3568, RK3588, RK3588S, RV1103, RV1106).
RKNN-Toolkit-Lite2 provides Python programming interfaces for Rockchip NPU platform (RK3566, RK3568, RK3588, RK3588S) to help users deploy RKNN models and accelerate the implementation of AI applications.
# Download
- You can also download all packages, docker image, examples, docs and platform-tools from baidu cloud: [rknn-toolkit2-v1.2.0](https://eyun.baidu.com/s/3jJ4k51s), fetch code: rknn
- You can also download all packages, docker image, examples, docs and platform-tools from baidu cloud: [RK_NPU_SDK](https://eyun.baidu.com/s/3eTDMk6Y), fetch code: rknn
# Notes
- Currently rknn-toolkit2 is not compatible with [rknn-toolkit](https://github.com/rockchip-linux/rknn-toolkit)
- Currently only support on Ubuntu 18.04 python 3.6
- Currently only support on Ubuntu 18.04 python 3.6 / Ubuntu 20.04 python 3.8
- If you have problem about RKNN-Toolkit2, it is recommended to create a issue or get answers from [Issues](https://github.com/rockchip-linux/rknn-toolkit2/issues).
- Latest version:1.2.0(Release version)
- Latest version:1.3.0(Release version)
# Checksums
## MD5
```
148f076c8707b066307f169975259bbb rknn_toolkit2-1.2.0_f7bb160f-cp36-cp36m-linux_x86_64.whl
69ffac3f944cf682acf563245e532405 packages/rknn_toolkit2-1.3.0_11912b58-cp36-cp36m-linux_x86_64.whl
fdf38d4ec4a8a0ac3c9faeba38baa908 rknn-toolkit2-1.2.0-cp36-docker.tar.gz
f37e2296e631587b929a75028dd235bf packages/rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl
e4430d25066487172a4a861b04098495 rknn_toolkit_lite2-1.2.0-cp37-cp37m-linux_aarch64.whl
f269a9cf3fa209b2615efdd0711f3f18 docker/rknn-toolkit2-1.3.0-cp36-docker.tar.gz
dd9f5509d3342b6cbc52d5814d0909d3 rknn_toolkit_lite2-1.2.0-cp39-cp39-linux_aarch64.whl
db40be96541a121b4c77109d829089b6 docker/rknn-toolkit2-1.3.0-cp38-docker.tar.gz
4dff57052f94a9c080774ab0e6af1d56 rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.3.0-cp37-cp37m-linux_aarch64.whl
1085cd7ee4bb18ef17b03d66e5428d69 rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.3.0-cp39-cp39-linux_aarch64.whl
```
# Feedback and Community Suport
Two ways are followed:

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doc/RKNNToolKit2_OP_Support-1.2.0.md → doc/RKNNToolKit2_OP_Support-1.3.0.md
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@ -2,11 +2,11 @@ @@ -2,11 +2,11 @@
## Explanation of terms:
Remarks:
**Remarks**:
Operators' specifications must meet the remarks' requirements.
Broadcast rule:
**Broadcast rule**:
- per-layer：
@ -26,6 +26,30 @@ Broadcast rule: @@ -26,6 +26,30 @@ Broadcast rule:
shape(A) = (2, 3, 4, 5), shape(B) = (5,) ==> shape(result) = (2, 3, 4, 5)
**Input Size Restrictions Description**
Assuming that input size is [N,H,W,C] (layout is NHWC)
- Case 1: the first layer is **Convolution**, whose kernel size is [kernel_height, kernel_width]
**W * kernel_height < 7168**
**kernel_height * kernel_width < 128**
- Case 2: first layer is not Convolution, and C == 1 or C == 3 or C == 4
**W < 7168**
- others:
**No Restrictions**
## ONNX OPs supported by RKNN Toolkit2
@ -33,63 +57,65 @@ According to [ONNX official instructions](https://github.com/microsoft/onnxrunti @@ -33,63 +57,65 @@ According to [ONNX official instructions](https://github.com/microsoft/onnxrunti
The list of ONNX OPs supported by RKNN Toolkit2 is as follows:
| **Operators** | **Remarks** |
|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Add | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
| AveragePool | channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7] |
| AveragePool | channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7]<br />auto_pad: NOTSET<br />count_include_pad: 1 <br />ceil_mode: 0 |
| ArgMin | |
| ArgMax | |
| BatchNormalization | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| cast | only support bool/int8/float |
| Clip | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Concat | axis: 1,2,3 |
| Conv | channel: unlimited <br />kernel height/width: [1, 31]<br />stride height/width: [1, 7]<br />kernels: [1, 8184]<br />pad left/right/top/bottom: [0, 15]<br />dilation: [1, 31]<br />group: unlimited |
| ConvTranspose | channel: unlimited<br />kernel height/width: [1, 31]<br />stride height/width: 2, 4, 8<br />kernels: [1, 8192]<br />pad left/right/top/bottom: [0, 15]<br />dilation: [1, 31]<br />group: unlimited |
| Concat | |
| Conv | channel: [0, 150000] <br />kernel height/width: [1, 31]<br />stride height/width: [1, 7]<br />pad left/right/top/bottom: [0, 15] |
| ConvTranspose | channel: [0, 150000]<br />kernel height/width: [1, 31]<br />stride height/width: 2, 4, 8<br />kernels: [1, 8192]<br />pad left/right/top/bottom: [0, 15] |
| DepthToSpace | |
| Div | support broadcast rule: per-element/other |
| Elu ||
| Div | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192]<br />support broadcast rule: per-element/other |
| Elu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br /> |
| Exp | |
| Flatten | |
| Gather | |
| Gemm | channel: unlimited<br /> One input should be Const |
| Gemm | channel: [0, 150000]<br />One input should be Const |
| GlobalAveragePool | channel: [1, 8192]<br />kernel height/width: [1, 343]<br /> |
| GlobalMaxPool | channel: [1, 8192]<br />kernel height/width: [1, 343]<br /> |
| Greater | support broadcast rule: per-element/other |
| HardSigmoid ||
| HardSigmoid | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| HardSwish | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| InstanceNormalization | |
| HardSwish ||
| LeakyRelu | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176] |
| LeakyRelu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Less | support broadcast rule: per-element/other |
| LpNormalization | |
| LRN | |
| LSTM | batchsize: 1 |
| LSTM | batchsize: 1<br />input_forget: 0 |
| GRU | batchsize: 1 |
| MatMul | |
| Max | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br /> dims=4 |
| MaxPool | channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7]<br />auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations |
| Max | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| MaxPool | channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7]<br />auto_pad: NOTSET<br />ceil_mode: 0<br />dilations: unsupport<br />storage_order: 0 |
| MaxRoiPool | |
| MaxUnpool | unsupport pad |
| Min | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Mul | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
| Pad | pad value should >= 0; pad dims must be 2 when mode is reflect or edge |
| Pad | width: [1, 8176]<br />mode: constant<br />pads n_begin/n_end/c_begin/c_end: 1 |
| Pow | |
| PRelu | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176]<br />slope support broadcast rule:: per-layer/channel |
| PRelu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />slope support broadcast rule: per-layer/channel |
| ReduceMax | |
| ReduceMean | output dims <= 4 |
| ReduceSum | output dims <= 4 |
| ReduceMean | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
| ReduceSum | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
| ReduceMin | |
| Relu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Reshape ||
| Resize | bilinear（not support tf_crop_and_resize）; nearest2d |
| ReverseSequence ||
| Reshape | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Resize | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />mode: nearest<br />scales: [1, 8] |
| ReverseSequence | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />batch_axis: 1 <br />time_axis: 0 |
| RoiAlign | pool type: average |
| Sigmoid ||
| Slice ||
| Softmax ||
| Sigmoid | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Slice | steps: 1 |
| Softmax | channel: [1, 8192]<br />axis: 1 |
| SpaceToDetph | |
| Split ||
| Split | axis: 1(channel) |
| Sub | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
| Squeeze | |
| Tanh | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176] |
| Tanh | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Tile | |
| Transpose ||
| Transpose | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Upsample (resize) | coordinate_transformation_mode unsupport tf_crop_and_resize |
@ -100,13 +126,13 @@ The protocol based on the official revision of berkeley comes from [berkeley caf @@ -100,13 +126,13 @@ The protocol based on the official revision of berkeley comes from [berkeley caf
Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 is as follows:
| **Operators** | **Remarks** |
|------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
| ---------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| BatchNorm | same as onnx BatchNormalization |
| bn (BatchNorm + Scale) | same as onnx BatchNormalization according to https://github.com/TimoSaemann/caffe-segnet-cudnn5 |
| BNLL | |
| Concat | same as onnx Concat |
| Convolution | same as onnx Conv |
| ConvolutionDepthwise | channel:unlimited<br />kernel height/width: [1, 8]<br />stride height/width: [1, 7]<br />kernels: 1<br />pad left/right/top/bottom: [0, 15] |
| ConvolutionDepthwise | kernel height/width: [1, 8]<br />kernels: 1<br />others same as onnx Conv |
| Crop | |
| Deconvolution | same as ConvTranspose |
| Dropout | |
@ -130,7 +156,7 @@ Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 is as f @@ -130,7 +156,7 @@ Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 is as f
| Resize | bilinear; nearest |
| Reverse | |
| ROIPooling | same as MaxRoiPool according to https://github.com/twmht/caffe-pva-faster-rcnn |
| Scale | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| Scale | same as onnx Mul |
| Sigmoid | same as onnx Sigmoid |
| Slice | same as onnx Split |
| Softmax | same as onnx Softmax |
@ -147,7 +173,7 @@ The Pytorch version supported by RKNN Toolkit2 is >1.6.0, models generated by ot @@ -147,7 +173,7 @@ The Pytorch version supported by RKNN Toolkit2 is >1.6.0, models generated by ot
The list of Pytorch OPs supported by RKNN Toolkit2 is as follows:
| **Operators** | **Remarks** |
|---------------------------|------------------------------------|
| ------------------------- | --------------------------------------------------------------- |
| aten::_convolution | same as onnx Conv |
| aten::add | same as onnx Add |
| aten::avg_pool2d | same as onnx AveragePool |
@ -159,7 +185,7 @@ The list of Pytorch OPs supported by RKNN Toolkit2 is as follows: @@ -159,7 +185,7 @@ The list of Pytorch OPs supported by RKNN Toolkit2 is as follows:
| aten::flatten | |
| aten::hardswish | same as onnx HardSwish |
| aten::instance_norm | same as onnx InstanceNormalization |
| aten::layer_norm ||
| aten::layer_norm | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
| aten::leaky_relu | same as onnx LeakyRelu |
| aten::linear | same as onnx Gemm |
| aten::matmul | same as onnx MatMul |
@ -188,13 +214,13 @@ The pb files (contain OPs belows) generated by TensorFlow version 1.12 - 1.15 fo @@ -188,13 +214,13 @@ The pb files (contain OPs belows) generated by TensorFlow version 1.12 - 1.15 fo
The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows:
| **Operators** | **Remarks** |
|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
| --------------------- | ------------------------------------------------------------------------- |
| Add | same as onnx Add |
| AvgPool | same as onnx AveragePool |
| Concat | same as onnx Concat |
| Conv2D | same as onnx Conv |
| DepthToSpace | |
| DepthwiseConv2d | channel:unlimited<br />kernel height/width: [1, 8]<br />stride height/width: [1, 7]<br />kernels: 1<br />pad left/right/top/bottom: [0, 15] |
| DepthwiseConv2d | kernel height/width: [1, 8]<br />kernels: 1<br />others same as onnx Conv |
| Div | same as onnx Div |
| Dropout | |
| Flatten | |
@ -212,7 +238,7 @@ The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows: @@ -212,7 +238,7 @@ The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows:
| Sigmoid | |
| Slice | |
| Softmax | |
| Softplus ||
| Softplus | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| SpaceToDepth | |
| Split | |
| Squeeze | |
@ -224,16 +250,16 @@ The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows: @@ -224,16 +250,16 @@ The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows:
The list of Darknet OPs supported by RKNN Toolkit2 is as follows:
| **Operators** | **Remarks** |
|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| add | same as onnx Add |
| batchnormalize | same as onnx BatchNormalization |
| concat | same as onnx Concat |
| convolutional | same as onnx Conv |
| depthwise_convolutional | channel:unlimited<br />kernel height/width: [1, 8]<br />stride height/width: [1, 7]<br />kernels: 1<br />pad left/right/top/bottom: [0, 15] |
| depthwise_convolutional | kernel height/width: [1, 8]<br />kernels: 1<br />others same as onnx Conv |
| fullconnect | |
| leakyrelu | same as onnx LeakyRelu |
| mish ||
| pooling | **AveragePool**:<br />channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7]<br /><br /> **GlobalAveragePool**:<br /> channel: [1, 8192]<br />kernel height/width: [1, 128]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7] <br /> <br /> **MaxPool/GlobalMaxPool**:<br /> channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7]<br /><br />MaxPool: <br />auto_pad only support NOTSET，ceil_mode only support 0，unsupport dilations |
| mish | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
| pooling | **AveragePool**: same as onnx AveragePool <br /> **GlobalAveragePool**: same as onnx GlobalAveragePool <br /> **MaxPool/GlobalMaxPool**: same as onnx MaxPool/GlobalMaxPool |
| route | |
| shortcut | |
| softmax | |

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@ -1,8 +1,32 @@ @@ -1,8 +1,32 @@
2021-4-22
版本: v1.3.0:
更新内容:
1. 新功能: python3.8/ubuntu20.04 平台支持
2. 修复一些已知的bug:
1) 修复了一些图优化和量化bug
2021-4-7
版本: v1.2.5:
更新内容:
1. 新功能: rv1103/rv1109平台的支持.
2. 修复一些已知的bug:
1) 修复了一些QAT模型转换问题
2) 修复了一些图优化bug
2021-1-27
版本: v1.2.1-beta:
更新内容:
1. 新功能: 多batch的NHWC格式输入时,在H维度,有效元素个数与实际内存中的元素个数不一致时,支持H方向实际元素个数按照h_stride设置.
2. 修复一些已知的bug:
1) LSTM算子内部变量重名的问题.
2021-1-12
版本：v1.2.0
更新内容:
1. 新功能: rk3588平台的支持; rknn模型加密支持; tensorflow/tflite/pytorch量化感知模型支持; 增加了一些新的 op 支持: InstanceNormalization, Swish, Conv1D等（详见 op support list）；增加了参数量计算以及算力分析
2. examples 更新：增加了从 pytorch 转 onnx 的转换 demo：resnet18_export_onnx ；增加了pytorch量化感知模型的加载demo：resnet18_qat demo；增加了模型加密功能
2. examples 更新：增加了从 pytorch 转 onnx 的转换 demo：resnet18_export_onnx ；增加了pytorch量化感知模型的加载demo：resnet18_qat demo；增加了模型加密功能：添加了3588平台 rknn 转换 demo
3. 接口更改：移除了 config，load_caffe，load_tensorflow等接口的一些不必要的参数设置，更新了 eval_perf 接口，详细改动见Uer_Guide文档
4. 修复一些已知的bug:
1) 修复了一些模型无法转换rknn的问题

1
doc/requirements_cp36-1.2.0.txt → doc/requirements_cp36-1.3.0.txt
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@ -15,5 +15,6 @@ requests==2.21.0 @@ -15,5 +15,6 @@ requests==2.21.0
opencv-python==4.4.0.46
PuLP==2.4
scikit_image==0.17.2
# if install bfloat16 failed, please install numpy manually first. "pip install numpy==1.16.6"
bfloat16==1.1
flatbuffers==1.12

19
doc/requirements_cp38-1.3.0.txt
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@ -0,0 +1,19 @@ @@ -0,0 +1,19 @@
numpy==1.17.3
onnx==1.7.0
onnxoptimizer==0.1.0
onnxruntime==1.6.0
tensorflow==2.2.0
tensorboard==2.2.2
protobuf==3.12.0
torch==1.6.0
torchvision==0.7.0
psutil==5.6.2
ruamel.yaml==0.15.81
scipy==1.4.1
tqdm==4.27.0
requests==2.21.0
opencv-python==4.4.0.46
PuLP==2.4
scikit_image==0.17.2
# if install bfloat16 failed, please install numpy manually first. "pip install numpy==1.17.3"
bfloat16==1.1

13
examples/functions/board_test/test.py
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@ -29,7 +29,7 @@ if __name__ == '__main__': @@ -29,7 +29,7 @@ if __name__ == '__main__':
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82],
quant_img_RGB2BGR=True, target_platform='rk3566')
quant_img_RGB2BGR=True, target_platform='rk3588')
print('done')
# Load model
@ -72,14 +72,21 @@ if __name__ == '__main__': @@ -72,14 +72,21 @@ if __name__ == '__main__':
# Set inputs
img = cv2.imread('./dog_224x224.jpg')
print('--> List devices')
rknn.list_devices()
# Init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime(target='rk3566', perf_debug=True, eval_mem=True)
ret = rknn.init_runtime(target='rk3588', perf_debug=True, eval_mem=True)
if ret != 0:
print('Init runtime environment failed!')
exit(ret)
print('done')
print('--> Get sdk version')
sdk_version = rknn.get_sdk_version()
print(sdk_version)
# eval perf
print('--> Eval perf')
rknn.eval_perf(inputs=[img])
@ -97,7 +104,7 @@ if __name__ == '__main__': @@ -97,7 +104,7 @@ if __name__ == '__main__':
# Accuracy analysis
print('--> Accuracy analysis')
ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot', target='rk3566')
ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot', target='rk3588')
if ret != 0:
print('Accuracy analysis failed!')
exit(ret)

2
examples/onnx/yolov5/test.py
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@ -231,7 +231,7 @@ if __name__ == '__main__': @@ -231,7 +231,7 @@ if __name__ == '__main__':
# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], output_tensor_type='int8')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]])
print('done')
# Load ONNX model

27
examples/readme.txt
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@ -0,0 +1,27 @@ @@ -0,0 +1,27 @@
The directory structure of examples is as follows:
.
├── caffe
│ ├── mobilenet_v2 # mobilenet_v2 float model
│ └── vgg-ssd # vgg-ssd float model
├── onnx
│ ├── resnet50v2 # resnet50v2 float model
│ └── yolov5 # yolov5 float model
├── pytorch
│ ├── resnet18 # resnet18 float model
│ ├── resnet18_qat # resnet18 QAT model
│ └── resnet18_export_onnx # how to export onnx model from pytorch
├── tensorflow
│ ├── ssd_mobilenet_v1 # ssd_mobilenet_v1 float model
│ └── inception_v3_qat # inception_v3 QAT model
├── tflite
│ ├── mobilenet_v1 # mobilenet_v1 float model
│ └── mobilenet_v1_qat # mobilenet_v1 QAT model
├── darknet
│ └── yolov3_416x416 # yolov3 float model
└── functions
├── accuracy_analysis # how to use accuracy-analysis function
├── batch_size # how to expand batch for use multi-batch function
├── multi_input_test # multi-input float model
├── hybrid_quant # how to use hybrid-quantization function
├── mmse # how to use mmse function
└── board_test # how to connect the board for debugging

2
examples/tensorflow/inception_v3_qat/test.py
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@ -104,7 +104,7 @@ if __name__ == '__main__': @@ -104,7 +104,7 @@ if __name__ == '__main__':
# Pre-process config
print('--> Config model')
rknn.config()
rknn.config(mean_values=[104, 117, 123], std_values=[128, 128, 128])
print('done')
# Load model

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rknn-toolkit-lite2-v1.2.0/doc/change_log.txt
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@ -1,6 +0,0 @@ @@ -1,6 +0,0 @@
2022-01-14
版本：v1.2.0
1. 新功能：
1.1 RKNN模型推理；
1.2 SDK版本查询；
1.3 模型可运行平台查询。

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rknn_toolkit_lite2/docs/change_log.txt
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@ -0,0 +1,12 @@ @@ -0,0 +1,12 @@
2022-04-27
版本: v1.3.0
1. 功能完善：
1.1 完善init_runtime失败的提示信息；
1.2 适配1.3.0版本NPU驱动。
2022-01-14
版本：v1.2.0
1. 新功能：
1.1 RKNN模型推理；
1.2 SDK版本查询；
1.3 模型可运行平台查询。

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rknn-toolkit-lite2-v1.2.0/examples/inference_with_lite/resnet18_for_rk356x.rknn → rknn_toolkit_lite2/examples/inference_with_lite/resnet18_for_rk356x.rknn
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rknn-toolkit-lite2-v1.2.0/examples/inference_with_lite/resnet18_for_rk3588.rknn → rknn_toolkit_lite2/examples/inference_with_lite/resnet18_for_rk3588.rknn
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4dff57052f94a9c080774ab0e6af1d56 rknn_toolkit_lite2-1.3.0-cp37-cp37m-linux_aarch64.whl
1085cd7ee4bb18ef17b03d66e5428d69 rknn_toolkit_lite2-1.3.0-cp39-cp39-linux_aarch64.whl
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