39 lines
2.3 KiB
Markdown
39 lines
2.3 KiB
Markdown
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# How to use accuracy-analysis function
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## Model Source
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The model used in this example come from:
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https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx
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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.accuracy_analysis'.
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## Expected Results
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This example will outputs the results of the accuracy analysis and store all the results in the snapshot directory, as follows:
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```
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# simulator_error: calculate the output error of each layer of the simulator (compared to the 'golden' value).
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# entire: output error of each layer between 'golden' and 'simulator', these errors will accumulate layer by layer.
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# single: single-layer output error between 'golden' and 'simulator', can better reflect the single-layer accuracy of the simulator.
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layer_name simulator_error
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entire single
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cos euc cos euc
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--------------------------------------------------------------------------------------------------
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[Input] data 1.00000 | 0.0 1.00000 | 0.0
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[exDataConvert] data_int8 0.99997 | 2.3275 0.99997 | 2.3275
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[BatchNormalization] resnetv24_batchnorm0_fwd 0.99995 | 2.4514 0.99995 | 2.4514
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...
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[Relu] resnetv24_relu1_fwd 0.98352 | 41.498 0.99989 | 3.3598
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[Conv] resnetv24_pool1_fwd 0.99545 | 2.2519 0.99999 | 0.1243
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[Conv] resnetv24_dense0_fwd_conv 0.99450 | 6.8046 0.99993 | 0.7382
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[Reshape] resnetv24_dense0_fwd_int8 0.99450 | 6.8046 0.99994 | 0.6719
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[exDataConvert] resnetv24_dense0_fwd 0.99450 | 6.8046 0.99994 | 0.6719
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```
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- Note: Different platforms, different versions of tools and drivers may have slightly different results.
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