def process_group(G, group, threshold):
total_precision = 0
cycles = 0
# Keep going until we converge
while True:
precisions = []
cycles += 1
if cycles > 50:
raise QuantizationError("Weight scaling has failed to converge")
for step in group:
prec_0, prec_1 = calculate_precisions(G, step)
precisions.append(np.sum(prec_0 * prec_1))
new_total_precision = sum(precisions)
# end when the precision change drops below threshold
if abs(new_total_precision - total_precision) < threshold:
LOG.info("group has converged under %f after %d cycles", threshold, cycles)
break
total_precision = new_total_precision
# note: traversing in reverse order. Not sure that it makes any difference.
for step in reversed(group):
nn_0 = step[0]
nn_1 = step[1]
# get the ranges of the output channels of layer 0 and input channels of layer 2
ranges_0, _ = Ranges.range_output(G, nn_0['node'], weights=nn_0['weights'])
ranges_1, _ = Ranges.range_input(G, nn_1['node'], weights=nn_1['weights'])
scale = calculate_s(ranges_0, ranges_1)
# now apply the scale to the output and input channels
nn_0['weights'], nn_0['biases'] =\
Scales.scale_output(G, nn_0['node'], scale, nn_0['weights'], nn_0['biases'])
nn_1['weights'] = Scales.scale_input(G, nn_1['node'], scale, nn_1['weights'])
def calculate_precisions(G, step):
nn_0 = step[0]
nn_1 = step[1]
ranges_0, max_0 = Ranges.range_output(G, nn_0['node'], weights=nn_0['weights'])
ranges_1, max_1 = Ranges.range_input(G, nn_1['node'], weights=nn_1['weights'])
prec_0 = ranges_0/max_0
prec_1 = ranges_1/max_1
return prec_0, prec_1
def process_group(group, threshold):
total_precision = 0
cycles = 0
# Keep going until we converge
while True:
precisions = []
cycles += 1
if cycles > 50:
raise QuantizationError("Weight scaling has failed to converge")
for step in group:
prec_0, prec_1 = calculate_precisions(step)
precisions.append(np.sum(prec_0 * prec_1))
new_total_precision = sum(precisions)
# end when the precision change drops below threshold
if abs(new_total_precision - total_precision) < threshold:
LOG.info("group has converged under %f after %d cycles", threshold,
cycles)
break
total_precision = new_total_precision
# note: traversing in reverse order. Not sure that it makes any difference.
for step in reversed(group):
nn_0 = step[0]
nn_1 = step[1]
# get the ranges of the output channels of layer 0 and input channels of layer 2
ranges_0, _ = Ranges.range_output(nn_0['node'],
weights=nn_0['weights'])
ranges_1, _ = Ranges.range_input(nn_1['node'],
weights=nn_1['weights'])
scale = calculate_s(ranges_0, ranges_1)
if 'activation' in nn_0:
if 'relun' not in nn_0:
if nn_0['activation'].activation == "relu6":
nn_0['relun'] = [6.0] * len(scale)
elif nn_0['activation'].activation == "relun":
if isinstance(nn_0['activation'].activation_params,
list):
nn_0['relun'] = copy(
nn_0['activation'].activation_params)
else:
nn_0['relun'] = [
nn_0['activation'].activation_params
] * len(scale)
nn_0['relun'] = [
relun / s for relun, s in zip(nn_0['relun'], scale)
]
# now apply the scale to the output and input channels
nn_0['weights'], nn_0['biases'] =\
Scales.scale_output(nn_0['node'], scale, nn_0['weights'], nn_0['biases'])
nn_1['weights'] = Scales.scale_input(nn_1['node'], scale,
nn_1['weights'])