liiir1985
7f62dcda9f
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| .. | ||
| README.md | ||
| dataset.txt | ||
| dog_224x224.jpg | ||
| mobilenet_v1_1.0_224_frozen.pb | ||
| test.py | ||
README.md
How to use MMSE function
Model Source
The model used in this example come from the following open source projects:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md
Script Usage
Usage:
python test.py
Description:
- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform.
- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'.
- The 'quantized_algorithm' parameter of 'rknn.config' is set to 'mmse'. and a 'MmseQuant2' progress bar can be seen during the conversion process, indicating the execution progress of MMSE.
Expected Results
This example will outputs the results of the accuracy analysis and print the TOP5 labels and corresponding scores of the test image classification results, as follows:
layer_name simulator_error
entire single
-----------------------------------------------------------------------------------------------------------
[Input] input:0 1.000000 1.000000
[exDataConvert] input:0_int8 0.999986 0.999986
[Conv] MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm:0
[Clip] MobilenetV1/MobilenetV1/Conv2d_0/Relu6:0 0.999986 0.999986
...
[Clip] MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6:0 0.858769 0.999334
[Conv] MobilenetV1/Logits/AvgPool_1a/AvgPool:0 0.948201 0.999804
[Conv] MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd:0 0.963938 0.999562
[Reshape] MobilenetV1/Logits/SpatialSqueeze:0_int8 0.963938 0.999906
[exDataConvert] MobilenetV1/Logits/SpatialSqueeze:0 0.963938 0.999906
-----TOP 5-----
[155]: 0.9931640625
[154]: 0.00266265869140625
[204]: 0.0019779205322265625
[283]: 0.0009202957153320312
[194]: 0.0001285076141357422
- Note: Different platforms, different versions of tools and drivers may have slightly different results.