45 lines
2.4 KiB
Markdown
45 lines
2.4 KiB
Markdown
# How to use MMSE function
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## Model Source
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The model used in this example come from the following open source projects:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md
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## Script Usage
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*Usage:*
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```
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python test.py
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```
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*Description:*
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- The default target platform in script is 'rk3566', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform.
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- If connecting board is required, please add the 'target' parameter in 'rknn.init_runtime'.
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- 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.
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## Expected Results
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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:
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```
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layer_name simulator_error
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entire single
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-----------------------------------------------------------------------------------------------------------
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[Input] input:0 1.000000 1.000000
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[exDataConvert] input:0_int8 0.999986 0.999986
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[Conv] MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/FusedBatchNorm:0
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[Clip] MobilenetV1/MobilenetV1/Conv2d_0/Relu6:0 0.999986 0.999986
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...
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[Clip] MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6:0 0.858769 0.999334
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[Conv] MobilenetV1/Logits/AvgPool_1a/AvgPool:0 0.948201 0.999804
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[Conv] MobilenetV1/Logits/Conv2d_1c_1x1/BiasAdd:0 0.963938 0.999562
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[Reshape] MobilenetV1/Logits/SpatialSqueeze:0_int8 0.963938 0.999906
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[exDataConvert] MobilenetV1/Logits/SpatialSqueeze:0 0.963938 0.999906
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```
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```
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-----TOP 5-----
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[155]: 0.9931640625
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[154]: 0.00266265869140625
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[204]: 0.0019779205322265625
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[283]: 0.0009202957153320312
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[194]: 0.0001285076141357422
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```
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- Note: Different platforms, different versions of tools and drivers may have slightly different results. |