77 lines
2.7 KiB
Markdown
77 lines
2.7 KiB
Markdown
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# How to debug invalid output
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The TFLM debugging output tools allow TFLM users to easily debug their models
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by providing a tool that will compare the intermediate values(output of each OP/Kernel)
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from a model post invoke between the TFLM and TfLite. As well as a way to
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compare intermediate values between TFLM x86 implementations and Optimized
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Implementations.
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## How to debug TFLM Interpreter output on embedded targets
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First you call a C++ binary that takes a TfLite model and returns a file that has
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random inputs and their corresponding output values for each layer of the model
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it was provided.
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The second is you provide a TfLite model and file outputted by C++ binary above
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to a python script. The script runs TFLM x86 inference comparison to the
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expected output.
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## How to debug TFLM Python Interpreter output
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Using a python script mentioned in the section above when only a TfLite model is
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provided as input, the script generates random input and compares TFLM vs TfLite
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inference outputs for each layer of the model.
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## C++ Expected Layer by Layer Output Tool on TFLite Micro
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This C++ binary allows you to pass in a TfLite model and returns a flatbuffer
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file with input and the corresponding output values appended into it that can be
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passed into a python debugging tool which can compare those golden values vs
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the x86 TFLM reference kernel implementation.
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The C++ Tool/binary will write a debugging file to the path provide in
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2nd arg using the tflite_model provided in the 1st arg.
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##### Command bazel/blaze:
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```
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bazel run tensorflow/lite/micro/tools:layer_cc -- \
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</path/to/input_model.tflite>
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</path/to/output.file_name>
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```
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##### How to Build using Makefile :
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```
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make -f tensorflow/lite/micro/tools/make/Makefile layer_by_layer_output_tool -j24
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```
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## Python Layer by Layer Debugging Tool
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The Python Tool/Script can first be used to compare TFLM vs Tflite outputs for
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random inputs by only providing a TfLite file.
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#### TfLite vs TFLM command:
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```
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bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
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--input_tflite_file=</path/to/my_model.tflite>
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```
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The Python Tool/Script can also be used to compare TFLM's python x86 output
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vs expected output provided by the C++ Tool/binary.
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#### TFLM vs Expected Command:
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```
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bazel run tensorflow/lite/micro/tools:layer_by_layer_debugger -- \
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--input_tflite_file=</path/to/my_model.tflite> \
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--layer_by_layer_data_file=</path/to/my_debug_flatbuffer_file>
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```
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#### Optional Flags:
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` --print_dump `
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When this flag is set, it will print the TFLM output for each layer that is
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compared.
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` --rng`
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Integer random number seed for generating input data for comparisons against TFLite. (Default: 42)
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