def __init__(self, C_in, C_out, affine=True):
super(FactorizedReduce, self).__init__()
assert C_out % 2 == 0
self.conv_1 = Layer.Conv2d_1w1a(C_in,
C_out // 2,
1,
stride=2,
padding=0,
bias=False)
self.conv_2 = Layer.Conv2d_1w1a(C_in,
C_out // 2,
1,
stride=2,
padding=0,
bias=False)
self.bn = nn.BatchNorm2d(C_out, affine=affine)
def __init__(self,
C_in,
C_out,
kernel_size,
stride,
padding,
dilation,
group,
affine=True,
options="B"):
super(BinaryDilGroupConv, self).__init__()
self.conv_1 = Layer.Conv2d_1w1a(C_in,
C_in,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=group,
bias=False)
self.bn_1 = nn.BatchNorm2d(C_in, affine=affine)
self.conv_2 = Layer.Conv2d_1w1a(C_in,
C_out,
kernel_size=1,
padding=0,
bias=False)
self.bn_2 = nn.BatchNorm2d(C_out, affine=affine)
self.shortcut = nn.Sequential()
if stride != 1:
if options == "A":
self.shortcut = LambdaLayer(lambda x: F.pad(
x[:, :, ::2, ::2],
(0, 0, 0, 0, C_in // 4, C_in // 4), "constant", 0))
elif options == "B":
self.shortcut = nn.Sequential(
Layer.Conv2d_1w1a(C_in,
C_in,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(C_in, affine=affine))
else:
self.shortcut = nn.Sequential(
nn.MaxPool2d(kernel_size=kernel_size,
stride=stride,
padding=padding), )
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(BinaryConvBN, self).__init__()
self.op = nn.Sequential(
Layer.Conv2d_1w1a(C_in,
C_out,
kernel_size,
stride=stride,
padding=padding,
bias=False), nn.BatchNorm2d(C_out,
affine=affine))
C, C, 3, stride, 1, group=group, affine=affine),
'group_conv_5x5':
lambda C, stride, group, affine: BinaryGroupConv(
C, C, 5, stride, 2, group=group, affine=affine),
'group_conv_7x7':
lambda C, stride, group, affine: BinaryGroupConv(
C, C, 7, stride, 3, group=group, affine=affine),
'dil_group_conv_3x3':
lambda C, stride, group, affine: BinaryDilGroupConv(
C, C, 3, stride, 2, 2, group=group, affine=affine),
'dil_group_conv_5x5':
lambda C, stride, group, affine: BinaryDilGroupConv(
C, C, 5, stride, 4, 2, group=group, affine=affine),
'group_conv_7x1_1x7':
lambda C, stride, group, affine: nn.Sequential(
Layer.Conv2d_1w1a(
C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False),
nn.BatchNorm2d(C, affine=affine),
Layer.Conv2d_1w1a(
C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False),
nn.BatchNorm2d(C, affine=affine)),
}
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)