import torch
from torch._six import container_abcs
from itertools import repeat
from torch.nn.modules.module import _addindent
def _quantize_weight(float_wt, observer):
wt_scale, wt_zp = observer.calculate_qparams()
if observer.qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]:
qweight = torch.quantize_per_tensor(
float_wt,
float(wt_scale), int(wt_zp), torch.qint8)
elif observer.qscheme in [torch.per_channel_symmetric, torch.per_channel_affine]:
wt_axis = observer.ch_axis
qweight = torch.quantize_per_channel(
float_wt,
wt_scale.to(torch.double), wt_zp.to(torch.int64), wt_axis, torch.qint8)
elif observer.qscheme in [torch.per_channel_affine_float_qparams]:
qweight = torch.quantize_per_channel(
float_wt,
wt_scale.to(torch.float), wt_zp.to(torch.float), observer.ch_axis, observer.dtype)
else:
raise ValueError("Unexpected qscheme " + observer.qscheme)
return qweight
def _ntuple_from_first(n):
"""Converts the argument to a tuple of size n
with the first element repeated."""
def parse(x):
while isinstance(x, container_abcs.Sequence):
if len(x) == n:
break
x = x[0]
return tuple(repeat(x, n))
return parse
def hide_packed_params_repr(self, params):
# We don't want to show `PackedParams` children, hence custom
# `__repr__`. This is the same as nn.Module.__repr__, except the check
# for the `params module`.
extra_lines = []
extra_repr = self.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
for key, module in self._modules.items():
if isinstance(module, params):
continue
mod_str = repr(module)
mod_str = _addindent(mod_str, 2)
child_lines.append('(' + key + '): ' + mod_str)
lines = extra_lines + child_lines
main_str = self._get_name() + '('
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
main_str += '\n ' + '\n '.join(lines) + '\n'
main_str += ')'
return main_str
_pair_from_first = _ntuple_from_first(2)