[GitHub] [incubator-mxnet] sfraczek removed a comment on issue #19235: question about function _get_optimal_threshold

[GitBox <gi...@apache.org>]

sfraczek removed a comment on issue #19235:
URL: https://github.com/apache/incubator-mxnet/issues/19235#issuecomment-700688990


   I'm not experienced with Mxnet yet but I made an experiment comparing both functions and the **old version code** seems to not work properly or I am misunderstanding something. The new code seems to return results that look like they make sense.
   For array arr containing `[ 0.990018   -0.92912527  0.88925551  0.29950806  0.5652269   0.91779029 0.70329186  0.93344053  0.26962166  0.23452311]`
   the old returns:
   min: 0.0,
   max: 1.0,
   divergence: 0.012564257718622684
   threshold: 1.0
   
   the new  returns:
   min: -0.92912527
   max: 0.990018
   divergence: [0.] 
   threshold: [0.9338415]
   
   This is my test code:
   ```python
   @with_seed()
   def test_optimal_threshold():
       arr = np.array([0.990018, -0.92912527, 0.88925551, 0.29950806, 0.5652269, 0.91779029, 0.70329186, 0.93344053, 0.26962166, 0.23452311])
       num_bins = 8001
       num_quantized_bins=255
       min_val = np.min(arr)
       max_val = np.max(arr)
       th = max(abs(min_val), abs(max_val))
       hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))
       print("\narr", arr)
       # print("hist", np.nonzero(hist))
   
       min_val1, max_val1, min_divergence, opt_th = mx.contrib.quant._get_optimal_threshold_old(mx.nd.array(hist), num_bins=num_bins, num_quantized_bins=num_quantized_bins)
       print("old", min_val1, max_val1, min_divergence, opt_th)
   
       quantized_dtype = 'int8'
       hist_data = (hist, hist_edges, min_val, max_val, max_val)
       min_val2, max_val2, threshold, divergence = mx.contrib.quant._get_optimal_threshold(hist_data, quantized_dtype, num_quantized_bins=num_quantized_bins)
       print("new", min_val2, max_val2, divergence, threshold)
   ```
   
   I hope this helps?


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