[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4339: Added tflite frontend support for quantized mean

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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
 
 

 ##########
 File path: python/tvm/relay/frontend/tflite.py
 ##########
 @@ -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 it 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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