FM / modules

/usr/local/lib64/python3.6/site-packages/torch/nn/quantized/modules/

/usr/local/lib64/python3.6/site-packages/torch/nn/quantized/modules

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation.py	4683	0644	edit dl rm
batchnorm.py	2682	0644	edit dl rm
conv.py	34625	0644	edit dl rm
embedding_ops.py	11130	0644	edit dl rm
functional_modules.py	8269	0644	edit dl rm
linear.py	11930	0644	edit dl rm
normalization.py	7351	0644	edit dl rm
utils.py	2370	0644	edit dl rm
__init__.py	3908	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/quantized/modules/utils.py (2370B)

import torch
from itertools import repeat
import collections
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, collections.abc.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)

import torch from itertools import repeat import collections 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, collections.abc.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)