OneRaynyDay commented on a change in pull request #11833: [MXNET-688] Fix
quantization divide by zero errors
URL: https://github.com/apache/incubator-mxnet/pull/11833#discussion_r204189323
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File path: python/mxnet/contrib/quantization.py
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@@ -303,32 +307,35 @@ def _get_optimal_threshold(arr, num_bins=8001,
num_quantized_bins=255):
right_outlier_count = np.sum(hist[p_bin_idx_stop:])
p[-1] += right_outlier_count
# is_nonzeros[k] indicates whether hist[k] is nonzero
- is_nonzeros = (sliced_nd_hist != 0).astype(np.int32)
+ is_nonzeros = (p != 0).astype(np.int32)
# calculate how many bins should be merged to generate quantized
distribution q
- num_merged_bins = p.size // num_quantized_bins
+ num_merged_bins = sliced_nd_hist.size // num_quantized_bins
# merge hist into num_quantized_bins bins
for j in range(num_quantized_bins):
start = j * num_merged_bins
stop = start + num_merged_bins
quantized_bins[j] = sliced_nd_hist[start:stop].sum()
quantized_bins[-1] += sliced_nd_hist[num_quantized_bins *
num_merged_bins:].sum()
# expand quantized_bins into p.size bins
- q = np.zeros(p.size, dtype=np.float32)
+ q = np.zeros(sliced_nd_hist.size, dtype=np.float32)
for j in range(num_quantized_bins):
start = j * num_merged_bins
if j == num_quantized_bins - 1:
- stop = -1
+ stop = len(is_nonzeros)
else:
stop = start + num_merged_bins
norm = is_nonzeros[start:stop].sum()
if norm != 0:
q[start:stop] = float(quantized_bins[j]) / float(norm)
- q[sliced_nd_hist == 0] = 0
+ q[p == 0] = 0
Review comment:
According to the slides on page 38:
http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf,
the zero'd out bins are meant to be w.r.t reference distribution `p`, rather
than the `sliced_nd_hist`.
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