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quantizationissues related to quantizationissues related to quantizationstaleissues that have not been addressed in a while; categorized by a botissues that have not been addressed in a while; categorized by a bot
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How can we quantize FP32 pretrained ONNX models to INT8 (static)?
the current method as mentioned for static quantization follows the below method:
quantize_static(
input_model_path,
output_model_path,
dr,
quant_format=args.quant_format,
per_channel=args.per_channel,
weight_type=QuantType.QInt8,
)
where dr is the data_reader class directory.
seems like it needs to be defined in the model class.
But for models which are not trained by me at all, how can i quantize them to INT8?
can i use a dummy dataset based on input shape like we do fro TFLite?
is there any other way to quantize the model to INT8 just for the purposes of inferencing with dummy data?
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quantizationissues related to quantizationissues related to quantizationstaleissues that have not been addressed in a while; categorized by a botissues that have not been addressed in a while; categorized by a bot