import torch
import torch.nn as nn
[docs]class ReLUConvBN(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
padding: int
zero-padding added to both sides of the input
dilation: int
spacing between kernel elements
bn_affine: bool
If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
bn_momentun: float
the value used for the running_mean and running_var computation. Default: 0.1
bn_track_running_stats: bool
When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
"""
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation,
bn_affine=True, bn_momentum=0.1, bn_track_running_stats=True):
super(ReLUConvBN, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=False),
nn.BatchNorm2d(C_out, affine=bn_affine, momentum=bn_momentum,
track_running_stats=bn_track_running_stats)
)
[docs] def forward(self, x):
"""
Parameters
---
x: torch.Tensor
input tensor
"""
return self.op(x)
[docs]class Pooling(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
bn_affine: bool
If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
bn_momentun: float
the value used for the running_mean and running_var computation. Default: 0.1
bn_track_running_stats: bool
When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
"""
def __init__(self, C_in, C_out, stride, bn_affine=True, bn_momentum=0.1, bn_track_running_stats=True):
super(Pooling, self).__init__()
if C_in == C_out:
self.preprocess = None
else:
self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 0,
bn_affine, bn_momentum, bn_track_running_stats)
self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
[docs] def forward(self, x):
"""
Parameters
---
x: torch.Tensor
input tensor
"""
if self.preprocess:
x = self.preprocess(x)
return self.op(x)
[docs]class Zero(nn.Module):
"""
Parameters
---
C_in: int
the number of input channels
C_out: int
the number of output channels
stride: int
stride of the convolution
"""
def __init__(self, C_in, C_out, stride):
super(Zero, self).__init__()
self.C_in = C_in
self.C_out = C_out
self.stride = stride
self.is_zero = True
[docs] def forward(self, x):
"""
Parameters
---
x: torch.Tensor
input tensor
"""
if self.C_in == self.C_out:
if self.stride == 1:
return x.mul(0.)
else:
return x[:, :, ::self.stride, ::self.stride].mul(0.)
else:
shape = list(x.shape)
shape[1] = self.C_out
zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
return zeros
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, stride, bn_affine=True, bn_momentum=0.1,
bn_track_running_stats=True):
super(FactorizedReduce, self).__init__()
self.stride = stride
self.C_in = C_in
self.C_out = C_out
self.relu = nn.ReLU(inplace=False)
if stride == 2:
C_outs = [C_out // 2, C_out - C_out // 2]
self.convs = nn.ModuleList()
for i in range(2):
self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=False))
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
else:
raise ValueError("Invalid stride : {:}".format(stride))
self.bn = nn.BatchNorm2d(C_out, affine=bn_affine, momentum=bn_momentum,
track_running_stats=bn_track_running_stats)
def forward(self, x):
x = self.relu(x)
y = self.pad(x)
out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
out = self.bn(out)
return out