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After calibration, the quantized model is transformed into a DPU deployable model (named as deploy_model.pb for vai_q_tensorflow) which follows the data format of a DPU. This model can then be compiled by the Vitis AI compiler and deployed to the DPU. This quantized model cannot be used by the standard TensorFlow framework to evaluate the loss of accuracy; hence in order to do so, a second file is produced (named as quantize_eval_model.pb for vai_q_tensorflow).
<pre>
Vai_q_tensorflow v1.2.0 build for Tensorflow 1.15.2
2020-10-08 13:26:59.752125: W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Not found: ./bin/ptxas not found
Relying on driver to perform ptx compilation. This message will be only logged once.
100% (100 of 100) |######################| Elapsed Time: 0:00:33 Time: 0:00:33
INFO: Checking Float Graph...
INFO: Float Graph Check Done.
INFO: Calibrating for 100 iterations...
INFO: Calibration Done.
INFO: Generating Deploy Model...
INFO: Deploy Model Generated.
********************* Quantization Summary *********************
INFO: Output:
quantize_eval_model: ./build/quantize/baseline/quantize_eval_model.pb
deploy_model: ./build/quantize/baseline/deploy_model.pb
</pre>
The accuracy of the '''baseline model''' over the test dataset after applying quantization:
<pre>
graph accuracy with test dataset: 0.70836667
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