Source code for torch.nn.qat.modules.conv

import torch.nn as nn
from torch.nn.intrinsic import ConvReLU2d

[docs]class Conv2d(nn.Conv2d): r""" A Conv2d module attached with FakeQuantize modules for weight, used for quantization aware training. We adopt the same interface as `torch.nn.Conv2d`, please see for documentation. Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to default. Attributes: weight_fake_quant: fake quant module for weight """ _FLOAT_MODULE = nn.Conv2d def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', qconfig=None): super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode) assert qconfig, 'qconfig must be provided for QAT module' self.qconfig = qconfig self.weight_fake_quant = qconfig.weight() def forward(self, input): return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
[docs] @classmethod def from_float(cls, mod): r"""Create a qat module from a float module or qparams_dict Args: `mod` a float module, either produced by torch.quantization utilities or directly from user """ assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \ cls._FLOAT_MODULE.__name__ assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined' assert mod.qconfig, 'Input float module must have a valid qconfig' if type(mod) == ConvReLU2d: mod = mod[0] qconfig = mod.qconfig qat_conv = cls(mod.in_channels, mod.out_channels, mod.kernel_size, stride=mod.stride, padding=mod.padding, dilation=mod.dilation, groups=mod.groups, bias=mod.bias is not None, padding_mode=mod.padding_mode, qconfig=qconfig) qat_conv.weight = mod.weight qat_conv.bias = mod.bias return qat_conv


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