[GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range

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anijain2305 commented on a change in pull request #4789: [Frontend][TFLite] Dynamically calculate input_stats of any fake_quant range
URL: https://github.com/apache/incubator-tvm/pull/4789#discussion_r374350076
 
 

 ##########
 File path: tests/python/frontend/tflite/test_forward.py
 ##########
 @@ -143,11 +144,13 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
             converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
             input_arrays = converter.get_input_arrays()
             input_stats = {}
-            # hardcode the mean_values and std_dev_values (m,s) to be the same
-            # if all inputs are in (float_min; float_max) == (-100, 100)
+            # calculate the mean and quantization scale for every input tensor,
+            # with respect to its fp32 input range, defined in fake_quant.
             # s = 255/(fmax-fmin);  m = -fmin*s (the zero point)
             for i in input_arrays:
-                input_stats[i] = (128., 1.275)
+                quant_scale = 255 / (input_range[i][1] - input_range[i][0])
 
 Review comment:
   Minor
   * Lets add a check to ensure that denominator is not zero.
   * Typically QNN_scale = FP32_range/QNN_range, so suggest to double-check if TFLite accepts inverse scale.

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