update rknn-toolkit2/rknn-toolkit-lite2 to 1.3.0
Signed-off-by: raul.rao <raul.rao@rock-chips.com>
This commit is contained in:
raul.rao
parent
480fc0ce47
commit
9ad79343fa
20
README.md
20
README.md
@ -1,27 +1,31 @@
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# Introduction
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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).
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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).
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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.
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# Download
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- 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
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- 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
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# Notes
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- Currently rknn-toolkit2 is not compatible with [rknn-toolkit](https://github.com/rockchip-linux/rknn-toolkit)
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- Currently only support on Ubuntu 18.04 python 3.6
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- Currently only support on Ubuntu 18.04 python 3.6 / Ubuntu 20.04 python 3.8
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- 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).
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- Latest version:1.2.0(Release version)
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- Latest version:1.3.0(Release version)
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# Checksums
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## MD5
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```
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148f076c8707b066307f169975259bbb rknn_toolkit2-1.2.0_f7bb160f-cp36-cp36m-linux_x86_64.whl
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69ffac3f944cf682acf563245e532405 packages/rknn_toolkit2-1.3.0_11912b58-cp36-cp36m-linux_x86_64.whl
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fdf38d4ec4a8a0ac3c9faeba38baa908 rknn-toolkit2-1.2.0-cp36-docker.tar.gz
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f37e2296e631587b929a75028dd235bf packages/rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl
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e4430d25066487172a4a861b04098495 rknn_toolkit_lite2-1.2.0-cp37-cp37m-linux_aarch64.whl
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f269a9cf3fa209b2615efdd0711f3f18 docker/rknn-toolkit2-1.3.0-cp36-docker.tar.gz
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dd9f5509d3342b6cbc52d5814d0909d3 rknn_toolkit_lite2-1.2.0-cp39-cp39-linux_aarch64.whl
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db40be96541a121b4c77109d829089b6 docker/rknn-toolkit2-1.3.0-cp38-docker.tar.gz
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4dff57052f94a9c080774ab0e6af1d56 rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.3.0-cp37-cp37m-linux_aarch64.whl
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1085cd7ee4bb18ef17b03d66e5428d69 rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.3.0-cp39-cp39-linux_aarch64.whl
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```
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# Feedback and Community Suport
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Two ways are followed:
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Binary file not shown.
BIN
Rockchip_Quick_Start_RKNN_SDK_V1.3.0_CN.pdf
Normal file
BIN
Rockchip_Quick_Start_RKNN_SDK_V1.3.0_CN.pdf
Normal file
Binary file not shown.
@ -2,11 +2,11 @@
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## Explanation of terms:
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Remarks:
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**Remarks**:
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Operators' specifications must meet the remarks' requirements.
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Broadcast rule:
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**Broadcast rule**:
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- per-layer：
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@ -26,6 +26,30 @@ Broadcast rule:
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shape(A) = (2, 3, 4, 5), shape(B) = (5,) ==> shape(result) = (2, 3, 4, 5)
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**Input Size Restrictions Description**
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Assuming that input size is [N,H,W,C] (layout is NHWC)
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- Case 1: the first layer is **Convolution**, whose kernel size is [kernel_height, kernel_width]
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**W * kernel_height < 7168**
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**kernel_height * kernel_width < 128**
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- Case 2: first layer is not Convolution, and C == 1 or C == 3 or C == 4
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**W < 7168**
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- others:
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**No Restrictions**
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## ONNX OPs supported by RKNN Toolkit2
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@ -33,63 +57,65 @@ According to [ONNX official instructions](https://github.com/microsoft/onnxrunti
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The list of ONNX OPs supported by RKNN Toolkit2 is as follows:
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| **Operators** | **Remarks** |
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|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Add | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
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| AveragePool | channel: [1, 8192]<br />kernel height/width: [1, 7]<br />stride height/width: [1, 8]<br />pad left/right/top/bottom: [0, 7] |
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| ArgMin ||
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| ArgMax ||
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| 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 |
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| ArgMin | |
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| ArgMax | |
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| BatchNormalization | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| cast | only support bool/int8/float |
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| Clip | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Concat | axis: 1,2,3 |
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| 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 |
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| 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 |
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| DepthToSpace ||
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| Div | support broadcast rule: per-element/other |
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| Elu ||
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| Exp ||
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| Flatten ||
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| Gather ||
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| Gemm | channel: unlimited<br /> One input should be Const |
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| Concat | |
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| Conv | channel: [0, 150000] <br />kernel height/width: [1, 31]<br />stride height/width: [1, 7]<br />pad left/right/top/bottom: [0, 15] |
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| 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] |
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| DepthToSpace | |
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| Div | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192]<br />support broadcast rule: per-element/other |
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| Elu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br /> |
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| Exp | |
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| Flatten | |
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| Gather | |
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| Gemm | channel: [0, 150000]<br />One input should be Const |
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| GlobalAveragePool | channel: [1, 8192]<br />kernel height/width: [1, 343]<br /> |
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| GlobalMaxPool | channel: [1, 8192]<br />kernel height/width: [1, 343]<br /> |
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| Greater | support broadcast rule: per-element/other |
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| HardSigmoid ||
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| InstanceNormalization ||
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| HardSwish ||
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| LeakyRelu | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176] |
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| HardSigmoid | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| HardSwish | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| InstanceNormalization | |
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| LeakyRelu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Less | support broadcast rule: per-element/other |
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| LpNormalization ||
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| LRN ||
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| LSTM | batchsize: 1 |
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| LpNormalization | |
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| LRN | |
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| LSTM | batchsize: 1<br />input_forget: 0 |
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| GRU | batchsize: 1 |
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| MatMul | |
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| Max | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br /> dims=4 |
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| 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 |
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| MaxRoiPool ||
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| Max | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| 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 |
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| MaxRoiPool | |
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| MaxUnpool | unsupport pad |
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| Min | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Mul | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
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| Pad | pad value should >= 0; pad dims must be 2 when mode is reflect or edge |
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| Pow ||
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| PRelu | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176]<br />slope support broadcast rule:: per-layer/channel |
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| ReduceMax ||
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| ReduceMean | output dims <= 4 |
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| ReduceSum | output dims <= 4 |
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| ReduceMin ||
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| Pad | width: [1, 8176]<br />mode: constant<br />pads n_begin/n_end/c_begin/c_end: 1 |
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| Pow | |
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| PRelu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />slope support broadcast rule: per-layer/channel |
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| ReduceMax | |
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| ReduceMean | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
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| ReduceSum | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
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| ReduceMin | |
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| Relu | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Reshape ||
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| Resize | bilinear（not support tf_crop_and_resize）; nearest2d |
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| ReverseSequence ||
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| Reshape | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Resize | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />mode: nearest<br />scales: [1, 8] |
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| ReverseSequence | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />batch_axis: 1 <br />time_axis: 0 |
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| RoiAlign | pool type: average |
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| Sigmoid ||
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| Slice ||
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| Softmax ||
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| SpaceToDetph ||
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| Split ||
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| Squeeze ||
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| Tanh | channel: unlimited<br />height: [1, 8192]<br />width: [1, 8176] |
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| Tile ||
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| Transpose ||
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| Sigmoid | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Slice | steps: 1 |
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| Softmax | channel: [1, 8192]<br />axis: 1 |
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| SpaceToDetph | |
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| Split | axis: 1(channel) |
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| Sub | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
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| Squeeze | |
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| Tanh | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Tile | |
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| Transpose | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Upsample (resize) | coordinate_transformation_mode unsupport tf_crop_and_resize |
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@ -100,37 +126,37 @@ The protocol based on the official revision of berkeley comes from [berkeley caf
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Based on this protocol, the list of Caffe OPs supported by RKNN Toolkit2 is as follows:
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| **Operators** | **Remarks** |
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|------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
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| ---------------------- | ---------------------------------------------------------------------------------------------------------------------- |
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| BatchNorm | same as onnx BatchNormalization |
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| bn (BatchNorm + Scale) | same as onnx BatchNormalization according to https://github.com/TimoSaemann/caffe-segnet-cudnn5 |
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| BNLL ||
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| BNLL | |
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| Concat | same as onnx Concat |
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| Convolution | same as onnx Conv |
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| 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] |
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| Crop ||
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| ConvolutionDepthwise | kernel height/width: [1, 8]<br />kernels: 1<br />others same as onnx Conv |
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| Crop | |
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| Deconvolution | same as ConvTranspose |
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| Dropout ||
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| Dropout | |
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| Eltwise | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176]<br />support broadcast rule: per-layer/channel/element |
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| Flatten ||
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| HardSigmoid ||
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| Flatten | |
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| HardSigmoid | |
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| InnerProduct | same as onnx Gemm |
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| LRN | same as onnx LRN |
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| Lstm | same as onnx LSTM according to https://github.com/xmfbit/warpctc-caffe |
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| Normalize ||
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| Normalize | |
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| Permute | same as onnx Transpose |
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| Power ||
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| Power | |
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| Pooling | same as onnx pooling |
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| PRelu | same as onnx PRelu |
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| Proposal | batch: 1 |
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| Reduction | output dims <= 4 |
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| Relu | same as onnx Relu |
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| Relu6 | same as onnx Clip |
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| Reorg ||
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| Reorg | |
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| Reshape | same as onnx Reshape |
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| Resize | bilinear; nearest |
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| Reverse ||
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| Reverse | |
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| ROIPooling | same as MaxRoiPool according to https://github.com/twmht/caffe-pva-faster-rcnn |
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| Scale | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| Scale | same as onnx Mul |
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| Sigmoid | same as onnx Sigmoid |
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| Slice | same as onnx Split |
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| Softmax | same as onnx Softmax |
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@ -147,19 +173,19 @@ The Pytorch version supported by RKNN Toolkit2 is >1.6.0, models generated by ot
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The list of Pytorch OPs supported by RKNN Toolkit2 is as follows:
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| **Operators** | **Remarks** |
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|---------------------------|------------------------------------|
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| ------------------------- | --------------------------------------------------------------- |
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| aten::_convolution | same as onnx Conv |
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| aten::add | same as onnx Add |
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| aten::avg_pool2d | same as onnx AveragePool |
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| aten::batch_norm | same as onnx BatchNormalization |
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| aten::cat | same as onnx Concat |
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| aten::chunk ||
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| aten::dropout ||
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| aten::chunk | |
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| aten::dropout | |
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| aten::elu | same as onnx Elu |
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| aten::flatten ||
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| aten::flatten | |
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| aten::hardswish | same as onnx HardSwish |
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| aten::instance_norm | same as onnx InstanceNormalization |
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| aten::layer_norm ||
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| aten::layer_norm | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8192] |
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| aten::leaky_relu | same as onnx LeakyRelu |
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| aten::linear | same as onnx Gemm |
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| aten::matmul | same as onnx MatMul |
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@ -188,53 +214,53 @@ The pb files (contain OPs belows) generated by TensorFlow version 1.12 - 1.15 fo
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The list of TensorFlow OPs supported by RKNN Toolkit2 is as follows:
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| **Operators** | **Remarks** |
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|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------|
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| --------------------- | ------------------------------------------------------------------------- |
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| Add | same as onnx Add |
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| AvgPool | same as onnx AveragePool |
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| Concat | same as onnx Concat |
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| Conv2D | same as onnx Conv |
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| DepthToSpace ||
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| 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] |
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| DepthToSpace | |
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| DepthwiseConv2d | kernel height/width: [1, 8]<br />kernels: 1<br />others same as onnx Conv |
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| Div | same as onnx Div |
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| Dropout ||
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| Flatten ||
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| Dropout | |
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| Flatten | |
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| LeakyRelu | same as onnx LeakyRelu |
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| Less | same as onnx Less |
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| LRN ||
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| LRN | |
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| MatMul | |
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| MaxPool | same as onnx MaxPool |
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| Mean | output dims <= 4 |
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| Pad | same as onnx Pad |
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| Relu | same as onnx Relu |
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| Reshape ||
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| ResizeBilinear ||
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| ResizeNearestNeighbor ||
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| Sigmoid ||
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| Slice ||
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| Softmax ||
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| Softplus ||
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| SpaceToDepth ||
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| Split ||
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| Squeeze ||
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| StridedSlice ||
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| Reshape | |
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| ResizeBilinear | |
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| ResizeNearestNeighbor | |
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| Sigmoid | |
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| Slice | |
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| Softmax | |
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| Softplus | channel: [1, 8192]<br />height: [1, 8192]<br />width: [1, 8176] |
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| SpaceToDepth | |
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| Split | |
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| Squeeze | |
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| StridedSlice | |
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| Tanh | same as onnx TanH |
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| Transpose ||
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| Transpose | |
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## Darknet OPs supported by RKNN Toolkit2
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The list of Darknet OPs supported by RKNN Toolkit2 is as follows:
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| **Operators** | **Remarks** |
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|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| ----------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| add | same as onnx Add |
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| batchnormalize | same as onnx BatchNormalization |
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| concat | same as onnx Concat |
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| convolutional | same as onnx Conv |
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| 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 |
|
||||
| route ||
|
||||
| shortcut ||
|
||||
| softmax ||
|
||||
| upsampling ||
|
||||
| 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 | |
|
||||
| upsampling | |
|
||||
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doc/Rockchip_User_Guide_RKNN_Toolkit2_CN-1.3.0.pdf
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doc/Rockchip_User_Guide_RKNN_Toolkit2_CN-1.3.0.pdf
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doc/Rockchip_User_Guide_RKNN_Toolkit2_EN-1.3.0.pdf
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doc/Rockchip_User_Guide_RKNN_Toolkit2_EN-1.3.0.pdf
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@ -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
|
||||
@ -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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19
doc/requirements_cp38-1.3.0.txt
Normal file
@ -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
|
||||
@ -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__':
|
||||
# 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__':
|
||||
|
||||
# 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)
|
||||
|
||||
@ -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
Normal file
27
examples/readme.txt
Normal file
@ -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
|
||||
@ -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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packages/rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl
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BIN
packages/rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl
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@ -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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12
rknn_toolkit_lite2/docs/change_log.txt
Normal file
12
rknn_toolkit_lite2/docs/change_log.txt
Normal file
@ -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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|
Before Width: | Height: | Size: 23 KiB After Width: | Height: | Size: 23 KiB |
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@ -0,0 +1,2 @@
|
||||
4dff57052f94a9c080774ab0e6af1d56 rknn_toolkit_lite2-1.3.0-cp37-cp37m-linux_aarch64.whl
|
||||
1085cd7ee4bb18ef17b03d66e5428d69 rknn_toolkit_lite2-1.3.0-cp39-cp39-linux_aarch64.whl
|
||||
Loading…
x
Reference in New Issue
Block a user