anijain2305 commented on a change in pull request #4339: Added tflite frontend
support for quantized mean
URL: https://github.com/apache/incubator-tvm/pull/4339#discussion_r346978076
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File path: python/tvm/relay/frontend/tflite.py
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@@ -659,7 +659,23 @@ def _convert_reduce(self, relay_op, op):
reduce_options.Init(op_options.Bytes, op_options.Pos)
keep_dims = reduce_options.KeepDims()
+ if input_tensor.qnn_params:
+ in_expr = _qnn.op.dequantize(data=in_expr,
Review comment:
Similar concern as that of @FrozenGene
From maths size, it looks safe to me, but let me know if I am wrong
~~~~
Initial Quantized tensor - QA - shape (N, dtype='int8')
Mean would be
scale_output * (Qout - zp_out) = scale_in * [(QA[0] + QA[1] .... + QA[N-1])
- N* zp_in]/N
scale_output * (Qout - zp_out) = scale_in * (Mean(QA) - zp_in)
~~~~
So, basically, upcast the quantized tensor to int32, call Int Mean, and then
requantize if the output scale/zp are different.
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