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import torch
import torch.nn as nn
class ConvRep5(nn.Module):
def __init__(self, in_channels, out_channels, rep_scale=4):
super(ConvRep5, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv = nn.Conv2d(in_channels, out_channels * rep_scale, 5, 1, 2)
self.conv_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 5, 1, 2),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv1 = nn.Conv2d(in_channels, out_channels * rep_scale, 1)
self.conv1_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 1),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv2 = nn.Conv2d(in_channels, out_channels * rep_scale, 3, 1, 1)
self.conv2_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 3, 1, 1),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_crossh = nn.Conv2d(in_channels, out_channels * rep_scale, (3, 1), 1, (1, 0))
self.conv_crossh_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, (3, 1), 1, (1, 0)),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_crossv = nn.Conv2d(in_channels, out_channels * rep_scale, (1, 3), 1, (0, 1))
self.conv_crossv_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, (1, 3), 1, (0, 1)),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_out = nn.Conv2d(out_channels * rep_scale * 10, out_channels, 1)
def forward(self, inp):
x = torch.cat(
[self.conv(inp),
self.conv1(inp),
self.conv2(inp),
self.conv_crossh(inp),
self.conv_crossv(inp),
self.conv_bn(inp),
self.conv1_bn(inp),
self.conv2_bn(inp),
self.conv_crossh_bn(inp),
self.conv_crossv_bn(inp)],
1
)
out = self.conv_out(x)
return out
def slim(self):
conv_weight = self.conv.weight
conv_bias = self.conv.bias
conv1_weight = self.conv1.weight
conv1_bias = self.conv1.bias
conv1_weight = nn.functional.pad(conv1_weight, (2, 2, 2, 2))
conv2_weight = self.conv2.weight
conv2_weight = nn.functional.pad(conv2_weight, (1, 1, 1, 1))
conv2_bias = self.conv2.bias
conv_crossv_weight = self.conv_crossv.weight
conv_crossv_weight = nn.functional.pad(conv_crossv_weight, (1, 1, 2, 2))
conv_crossv_bias = self.conv_crossv.bias
conv_crossh_weight = self.conv_crossh.weight
conv_crossh_weight = nn.functional.pad(conv_crossh_weight, (2, 2, 1, 1))
conv_crossh_bias = self.conv_crossh.bias
conv1_bn_weight = self.conv1_bn[0].weight
conv1_bn_weight = nn.functional.pad(conv1_bn_weight, (2, 2, 2, 2))
conv2_bn_weight = self.conv2_bn[0].weight
conv2_bn_weight = nn.functional.pad(conv2_bn_weight, (1, 1, 1, 1))
conv_crossv_bn_weight = self.conv_crossv_bn[0].weight
conv_crossv_bn_weight = nn.functional.pad(conv_crossv_bn_weight, (1, 1, 2, 2))
conv_crossh_bn_weight = self.conv_crossh_bn[0].weight
conv_crossh_bn_weight = nn.functional.pad(conv_crossh_bn_weight, (2, 2, 1, 1))
bn = self.conv_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_bn_weight = self.conv_bn[0].weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_weight = conv_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_bias = self.conv_bn[0].bias * k + b
conv_bn_bias = conv_bn_bias * bn.weight + bn.bias
bn = self.conv1_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv1_bn_weight = conv1_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv1_bn_weight = conv1_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv1_bn_bias = self.conv1_bn[0].bias * k + b
conv1_bn_bias = conv1_bn_bias * bn.weight + bn.bias
bn = self.conv2_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv2_bn_weight = conv2_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv2_bn_weight = conv2_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv2_bn_bias = self.conv2_bn[0].bias * k + b
conv2_bn_bias = conv2_bn_bias * bn.weight + bn.bias
bn = self.conv_crossv_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_crossv_bn_weight = conv_crossv_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossv_bn_weight = conv_crossv_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossv_bn_bias = self.conv_crossv_bn[0].bias * k + b
conv_crossv_bn_bias = conv_crossv_bn_bias * bn.weight + bn.bias
bn = self.conv_crossh_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_crossh_bn_weight = conv_crossh_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossh_bn_weight = conv_crossh_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossh_bn_bias = self.conv_crossh_bn[0].bias * k + b
conv_crossh_bn_bias = conv_crossh_bn_bias * bn.weight + bn.bias
weight = torch.cat(
[conv_weight, conv1_weight, conv2_weight,
conv_crossh_weight, conv_crossv_weight,
conv_bn_weight, conv1_bn_weight, conv2_bn_weight,
conv_crossh_bn_weight, conv_crossv_bn_weight],
0
)
weight_compress = self.conv_out.weight.squeeze()
weight = torch.matmul(weight_compress, weight.permute([2, 3, 0, 1])).permute([2, 3, 0, 1])
bias_ = torch.cat(
[conv_bias, conv1_bias, conv2_bias,
conv_crossh_bias, conv_crossv_bias,
conv_bn_bias, conv1_bn_bias, conv2_bn_bias,
conv_crossh_bn_bias, conv_crossv_bn_bias],
0
)
bias = torch.matmul(weight_compress, bias_)
if isinstance(self.conv_out.bias, torch.Tensor):
bias = bias + self.conv_out.bias
return weight, bias
class ConvRep3(nn.Module):
def __init__(self, in_channels, out_channels, rep_scale=4):
super(ConvRep3, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv = nn.Conv2d(in_channels, out_channels * rep_scale, 3, 1, 1)
self.conv_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 3, 1, 1),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv1 = nn.Conv2d(in_channels, out_channels * rep_scale, 1)
self.conv1_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 1),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_crossh = nn.Conv2d(in_channels, out_channels * rep_scale, (3, 1), 1, (1, 0))
self.conv_crossh_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, (3, 1), 1, (1, 0)),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_crossv = nn.Conv2d(in_channels, out_channels * rep_scale, (1, 3), 1, (0, 1))
self.conv_crossv_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, (1, 3), 1, (0, 1)),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_out = nn.Conv2d(out_channels * rep_scale * 8, out_channels, 1)
def forward(self, inp):
x = torch.cat(
[self.conv(inp),
self.conv1(inp),
self.conv_crossh(inp),
self.conv_crossv(inp),
self.conv_bn(inp),
self.conv1_bn(inp),
self.conv_crossh_bn(inp),
self.conv_crossv_bn(inp)],
1
)
out = self.conv_out(x)
return out
def slim(self):
conv_weight = self.conv.weight
conv_bias = self.conv.bias
conv1_weight = self.conv1.weight
conv1_bias = self.conv1.bias
conv1_weight = nn.functional.pad(conv1_weight, (1, 1, 1, 1))
conv_crossv_weight = self.conv_crossv.weight
conv_crossv_weight = nn.functional.pad(conv_crossv_weight, (0, 0, 1, 1))
conv_crossv_bias = self.conv_crossv.bias
conv_crossh_weight = self.conv_crossh.weight
conv_crossh_weight = nn.functional.pad(conv_crossh_weight, (1, 1, 0, 0))
conv_crossh_bias = self.conv_crossh.bias
conv1_bn_weight = self.conv1_bn[0].weight
conv1_bn_weight = nn.functional.pad(conv1_bn_weight, (1, 1, 1, 1))
conv_crossv_bn_weight = self.conv_crossv_bn[0].weight
conv_crossv_bn_weight = nn.functional.pad(conv_crossv_bn_weight, (0, 0, 1, 1))
conv_crossh_bn_weight = self.conv_crossh_bn[0].weight
conv_crossh_bn_weight = nn.functional.pad(conv_crossh_bn_weight, (1, 1, 0, 0))
bn = self.conv_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_bn_weight = self.conv_bn[0].weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_weight = conv_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_bias = self.conv_bn[0].bias * k + b
conv_bn_bias = conv_bn_bias * bn.weight + bn.bias
bn = self.conv1_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv1_bn_weight = conv1_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv1_bn_weight = conv1_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv1_bn_bias = self.conv1_bn[0].bias * k + b
conv1_bn_bias = conv1_bn_bias * bn.weight + bn.bias
bn = self.conv_crossv_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_crossv_bn_weight = conv_crossv_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossv_bn_weight = conv_crossv_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossv_bn_bias = self.conv_crossv_bn[0].bias * k + b
conv_crossv_bn_bias = conv_crossv_bn_bias * bn.weight + bn.bias
bn = self.conv_crossh_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_crossh_bn_weight = conv_crossh_bn_weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossh_bn_weight = conv_crossh_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_crossh_bn_bias = self.conv_crossh_bn[0].bias * k + b
conv_crossh_bn_bias = conv_crossh_bn_bias * bn.weight + bn.bias
weight = torch.cat(
[conv_weight, conv1_weight,
conv_crossh_weight, conv_crossv_weight,
conv_bn_weight, conv1_bn_weight,
conv_crossh_bn_weight, conv_crossv_bn_weight],
0
)
weight_compress = self.conv_out.weight.squeeze()
weight = torch.matmul(weight_compress, weight.permute([2, 3, 0, 1])).permute([2, 3, 0, 1])
bias_ = torch.cat(
[conv_bias, conv1_bias,
conv_crossh_bias, conv_crossv_bias,
conv_bn_bias, conv1_bn_bias,
conv_crossh_bn_bias, conv_crossv_bn_bias],
0
)
bias = torch.matmul(weight_compress, bias_)
if isinstance(self.conv_out.bias, torch.Tensor):
bias = bias + self.conv_out.bias
return weight, bias
class ConvRepPoint(nn.Module):
def __init__(self, in_channels, out_channels, rep_scale=4):
super(ConvRepPoint, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv = nn.Conv2d(in_channels, out_channels * rep_scale, 1)
self.conv_bn = nn.Sequential(
nn.Conv2d(in_channels, out_channels * rep_scale, 1),
nn.BatchNorm2d(out_channels * rep_scale)
)
self.conv_out = nn.Conv2d(out_channels * rep_scale * 2, out_channels, 1)
def forward(self, inp):
x = torch.cat([self.conv(inp), self.conv_bn(inp)], 1)
out = self.conv_out(x)
return out
def slim(self):
conv_weight = self.conv.weight
conv_bias = self.conv.bias
bn = self.conv_bn[1]
k = 1 / (bn.running_var + bn.eps) ** .5
b = - bn.running_mean / (bn.running_var + bn.eps) ** .5
conv_bn_weight = self.conv_bn[0].weight * k.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_weight = conv_bn_weight * bn.weight.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
conv_bn_bias = self.conv_bn[0].bias * k + b
conv_bn_bias = conv_bn_bias * bn.weight + bn.bias
weight = torch.cat([conv_weight, conv_bn_weight], 0)
weight_compress = self.conv_out.weight.squeeze()
weight = torch.matmul(weight_compress, weight.permute([2, 3, 0, 1])).permute([2, 3, 0, 1])
bias = torch.cat([conv_bias, conv_bn_bias], 0)
bias = torch.matmul(weight_compress, bias)
if isinstance(self.conv_out.bias, torch.Tensor):
bias = bias + self.conv_out.bias
return weight, bias
class QuadraticConnectionUnit(nn.Module):
def __init__(self, block1, block2, channels):
super(QuadraticConnectionUnit, self).__init__()
self.block1 = block1
self.block2 = block2
self.scale = 0.1
self.bias = nn.Parameter(torch.randn((1, channels, 1, 1)))
def forward(self, x):
return self.scale * self.block1(x) * self.block2(x) + self.bias
class QuadraticConnectionUnitS(nn.Module):
def __init__(self, block1, block2, channels):
super(QuadraticConnectionUnitS, self).__init__()
self.block1 = block1
self.block2 = block2
self.bias = nn.Parameter(torch.randn((1, channels, 1, 1)))
def forward(self, x):
return self.block1(x) * self.block2(x) + self.bias
class AdditionFusion(nn.Module):
def __init__(self, addend1, addend2, channels):
super(AdditionFusion, self).__init__()
self.addend1 = addend1
self.addend2 = addend2
self.bias = nn.Parameter(torch.randn((1, channels, 1, 1)))
def forward(self, x):
return self.addend1(x) + self.addend2(x) + self.bias
class AdditionFusionS(nn.Module):
def __init__(self, addend1, addend2, channels):
super(AdditionFusionS, self).__init__()
self.addend1 = addend1
self.addend2 = addend2
self.bias = nn.Parameter(torch.randn((1, channels, 1, 1)))
def forward(self, x):
return self.addend1(x) + self.addend2(x) + self.bias
class DropBlock(nn.Module):
def __init__(self, block_size, p=0.5):
super(DropBlock, self).__init__()
self.block_size = block_size
self.p = p / block_size / block_size
def forward(self, x):
mask = 1 - (torch.rand_like(x[:, :1]) >= self.p).float()
mask = nn.functional.max_pool2d(mask, self.block_size, 1, self.block_size // 2)
return x * (1 - mask)
class ResBlock(nn.Module):
def __init__(self, num_feat=4, rep_scale=4):
super(ResBlock, self).__init__()
self.conv1 = ConvRep3(num_feat, num_feat, rep_scale=rep_scale)
self.conv2 = ConvRep3(num_feat, num_feat, rep_scale=rep_scale)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
identity = x
out = self.conv2(self.relu(self.conv1(x)))
return identity + out
class ResBlockS(nn.Module):
def __init__(self, num_feat=4):
super(ResBlockS, self).__init__()
self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
identity = x
out = self.conv2(self.relu(self.conv1(x)))
return identity + out
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