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operations.py
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operations.py
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import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect' : lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
'sep_conv_3x3' : lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
'sep_conv_5x5' : lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
'dil_conv_3x3' : lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
'dil_conv_5x5' : lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
}
class ReLUConvBN(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=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, bias=False),
nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class DilConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
super(DilConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class SepConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(SepConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class Zero(nn.Module):
def __init__(self, stride):
super(Zero, self).__init__()
self.stride = stride
def forward(self, x):
if self.stride == 1:
return x.mul(0.)
return x[:,:,::self.stride,::self.stride].mul(0.)
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, affine=True):
super(FactorizedReduce, self).__init__()
assert C_out % 2 == 0
self.relu = nn.ReLU(inplace=False)
self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.bn = nn.BatchNorm2d(C_out, affine=affine)
def forward(self, x):
x = self.relu(x)
out = torch.cat([self.conv_1(x), self.conv_2(x[:,:,1:,1:])], dim=1)
out = self.bn(out)
return out
class FactorizedIncrease (nn.Module) :
def __init__ (self, in_channel, out_channel) :
super(FactorizedIncrease, self).__init__()
self._in_channel = in_channel
self.op = nn.Sequential (
nn.Upsample(scale_factor=2, mode="bilinear"),
nn.ReLU(inplace = False),
nn.Conv2d(self._in_channel, out_channel, 1, stride=1, padding=0),
nn.BatchNorm2d(out_channel)
)
def forward (self, x) :
return self.op (x)
class ASPP(nn.Module):
def __init__(self, in_channels, paddings, dilations, num_classes):
# todo depthwise separable conv
super(ASPP, self).__init__()
self._num_classes =num_classes
self.conv11 = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, bias=False),
nn.BatchNorm2d(in_channels))
self.conv33 = nn.Sequential(nn.Conv2d(in_channels, in_channels, 3,
padding=paddings, dilation=dilations, bias=False),
nn.BatchNorm2d(in_channels))
self.conv_p = nn.Sequential(nn.Conv2d(in_channels, in_channels, 1, bias=False),
nn.BatchNorm2d(in_channels))
self.concate_conv = nn.Sequential(nn.Conv2d(in_channels * 3, self._num_classes, 1, stride=1, padding=0))
def forward(self, x):
conv11 = self.conv11(x)
conv33 = self.conv33(x)
# image pool and upsample
image_pool = nn.AvgPool2d(kernel_size=x.size()[2:])
image_pool = image_pool(x)
upsample = nn.Upsample(size=x.size()[2:], mode='bilinear', align_corners=True)
upsample = upsample(image_pool)
upsample = self.conv_p(upsample)
# concate
concate = torch.cat([conv11, conv33, upsample], dim=1)
return self.concate_conv(concate)