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