Source code for torch.nn.intrinsic.quantized.modules.conv_relu
from __future__ import absolute_import, division, print_function, unicode_literals
import torch.nn.quantized as nnq
import torch.nn.intrinsic
import torch.nn.intrinsic.qat
from torch.nn.utils import fuse_conv_bn_weights
import torch
[docs]class ConvReLU2d(nnq.Conv2d):
r"""
A ConvReLU2d module is a fused module of Conv2d and ReLU
We adopt the same interface as :class:`torch.nn.quantized.Conv2d`.
Attributes:
Same as torch.nn.quantized.Conv2d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1,
bias=True, padding_mode='zeros'):
super(ConvReLU2d, self).__init__(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias, padding_mode=padding_mode)
def forward(self, input):
# Temporarily using len(shape) instead of ndim due to JIT issue
# https://github.com/pytorch/pytorch/issues/23890
if len(input.shape) != 4:
raise ValueError("Input shape must be `(N, C, H, W)`!")
return torch.ops.quantized.conv2d_relu(input,
self._packed_params,
self.stride, self.padding,
self.dilation, self.groups,
self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedConvReLU2d'
@classmethod
def from_float(cls, mod):
if type(mod) == torch.nn.intrinsic.qat.ConvBnReLU2d:
mod.weight, mod.bias = \
fuse_conv_bn_weights(mod.weight, mod.bias, mod.running_mean,
mod.running_var, mod.eps, mod.gamma, mod.beta)
return super(ConvReLU2d, cls).from_float(mod)
