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
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File path: tests/python/frontend/tflite/test_forward.py
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@@ -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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