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base_net_snow.py
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base_net_snow.py
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import torch
import torch.nn as nn
from einops import rearrange
import numbers
from timm.models.layers import DropPath,to_2tuple,trunc_normal_
import math
import torch.nn.functional as F
from condconv import *
from distutils.version import LooseVersion
import torchvision
def to_3d(x):
return rearrange(x, 'b c h w -> b (h w) c')
def to_4d(x,h,w):
return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)
class BiasFree_LayerNorm(nn.Module):
def __init__(self, normalized_shape):
super(BiasFree_LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
normalized_shape = torch.Size(normalized_shape)
assert len(normalized_shape) == 1
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.normalized_shape = normalized_shape
def forward(self, x):
sigma = x.var(-1, keepdim=True, unbiased=False)
return x / torch.sqrt(sigma+1e-5) * self.weight
class WithBias_LayerNorm(nn.Module):
def __init__(self, normalized_shape):
super(WithBias_LayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
normalized_shape = torch.Size(normalized_shape)
assert len(normalized_shape) == 1
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.normalized_shape = normalized_shape
def forward(self, x):
mu = x.mean(-1, keepdim=True)
sigma = x.var(-1, keepdim=True, unbiased=False)
return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias
class LayerNorm(nn.Module):
def __init__(self, dim, LayerNorm_type):
super(LayerNorm, self).__init__()
if LayerNorm_type =='BiasFree':
self.body = BiasFree_LayerNorm(dim)
else:
self.body = WithBias_LayerNorm(dim)
def forward(self, x):
h, w = x.shape[-2:]
return to_4d(self.body(to_3d(x)), h, w)
class Down(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,stride,padding):
super(Down,self).__init__()
self.down = nn.Sequential(
nn.Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding,bias=False),
)
def forward(self,x):
x = self.down(x)
return x
class Up(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,stride,padding):
super(Up,self).__init__()
self.up = nn.Sequential(
nn.Sequential(nn.ConvTranspose2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding))
)
def forward(self,x):
x = self.up(x)
return x
class UpSample(nn.Module):
def __init__(self, in_channels,out_channels,s_factor):
super(UpSample, self).__init__()
self.up = nn.Sequential(nn.Upsample(scale_factor=s_factor, mode='bilinear', align_corners=False),
nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False))
def forward(self, x):
x = self.up(x)
return x
class DWconv(nn.Module):
def __init__(self,in_channels,out_channels):
super(DWconv,self).__init__()
self.dwconv = nn.Conv2d(in_channels,out_channels,3,1,1,bias=False,groups=in_channels)
def forward(self,x):
x = self.dwconv(x)
return x
class ChannelAttention(nn.Module):
def __init__(self,chns,factor,dynamic=False):
super().__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
if dynamic == False:
self.channel_map = nn.Sequential(
nn.Conv2d(chns,chns//factor,1,1,0),
nn.LeakyReLU(),
nn.Conv2d(chns//factor,chns,1,1,0),
nn.Sigmoid()
)
else:
self.channel_map = nn.Sequential(
CondConv2D(chns,chns//factor,1,1,0),
nn.LeakyReLU(),
CondConv2D(chns//factor,chns,1,1,0),
nn.Sigmoid()
)
def forward(self,x):
avg_pool = self.avg_pool(x)
map = self.channel_map(avg_pool)
return x*map
class LKA(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
self.act1 = nn.GELU()
self.conv_spatial = nn.Conv2d(dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)
self.act2 = nn.GELU()
self.conv1 = nn.Conv2d(dim, dim, 1)
def forward(self, x):
u = x.clone()
attn = self.conv0(x)
attn = self.act1(attn)
attn = self.conv_spatial(attn)
attn = self.act2(attn)
attn = self.conv1(attn)
return u * attn
class LKA_dynamic(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv0 = CondConv2D(dim,dim,5,1,2,1,dim)
self.act1 = nn.GELU()
self.conv_spatial = CondConv2D(dim,dim,7,1,9,3,dim)
self.act2 = nn.GELU()
self.conv1 = nn.Conv2d(dim, dim, 1)
def forward(self, x):
u = x.clone()
attn = self.conv0(x)
attn = self.act1(attn)
attn = self.conv_spatial(attn)
attn = self.act2(attn)
attn = self.conv1(attn)
return u * attn
class Attention(nn.Module):
def __init__(self, d_model,dynamic=True):
super().__init__()
self.conv11 = nn.Conv2d(d_model,d_model,kernel_size=3,stride=1,padding=1,groups=d_model)
#self.activation = nn.GELU()
if dynamic == True:
self.spatial_gating_unit = LKA_dynamic(d_model)
else:
self.spatial_gating_unit = LKA(d_model)
self.conv22 = nn.Conv2d(d_model,d_model,kernel_size=3,stride=1,padding=1,groups=d_model)
def forward(self, x):
x = self.conv11(x)
x = self.spatial_gating_unit(x)
x = self.conv22(x)
return x
class ConvBlock(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio,VAN=False,dynamic=False):
super(ConvBlock, self).__init__()
self.VAN = VAN
hidden_dim = round(inp * expand_ratio)
self.identity = stride == 1 and inp == oup
self.apply(self._init_weight)
if self.VAN == True:
if expand_ratio == 1:
self.conv = nn.Sequential(
LayerNorm(hidden_dim, 'BiasFree'),
Attention(hidden_dim,dynamic=dynamic),
)
else:
self.conv = nn.Sequential(
nn.Conv2d(inp, hidden_dim, 1, 1, 0 ),
LayerNorm(hidden_dim, 'BiasFree'),
Attention(hidden_dim,dynamic=dynamic),
nn.Conv2d(hidden_dim, oup, 1, 1, 0),
)
else:
if dynamic == False:
if expand_ratio == 1:
self.conv = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
LayerNorm(hidden_dim, 'BiasFree'),
nn.GELU(),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
ChannelAttention(hidden_dim,4,dynamic=dynamic),
nn.Conv2d(hidden_dim, oup, 1, 1, 0),
)
else:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0 ),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
LayerNorm(hidden_dim, 'BiasFree'),
nn.GELU(),
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
ChannelAttention(hidden_dim,4,dynamic=dynamic),
nn.Conv2d(hidden_dim, oup, 1, 1, 0),
)
else:
if expand_ratio == 1:
self.conv = nn.Sequential(
CondConv2D(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
LayerNorm(hidden_dim, 'BiasFree'),
nn.GELU(),
CondConv2D(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
ChannelAttention(hidden_dim,4,dynamic=dynamic),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0),
)
else:
self.conv = nn.Sequential(
nn.Conv2d(inp, hidden_dim, 1, 1, 0 ),
CondConv2D(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
LayerNorm(hidden_dim, 'BiasFree'),
nn.GELU(),
CondConv2D(hidden_dim, hidden_dim, 3, stride, 1,dilation=1, groups=hidden_dim),
ChannelAttention(hidden_dim,4,dynamic=dynamic),
nn.Conv2d(hidden_dim, oup, 1, 1, 0),
)
def _init_weight(self,m):
if isinstance(m,nn.Linear):
trunc_normal_(m.weight,std=0.02)
if isinstance(m,nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias,0)
elif isinstance(m,nn.LayerNorm):
nn.init.constant_(m.bias,0)
nn.init.constant_(m.weight,1.0)
elif isinstance(m,nn.Conv2d):
fan_out = m.kernel_size[0]*m.kernel_size[1]*m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0,math.sqrt(2.0/fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
if self.identity:
return x + self.conv(x)
else:
return self.conv(x)
class Conv_block(nn.Module):
def __init__(self,n,in_channel,out_channele,expand_ratio,VAN=False,dynamic=False):
super(Conv_block,self).__init__()
layers=[]
for i in range(n):
layers.append(ConvBlock(in_channel,out_channele,1 if i==0 else 1,expand_ratio,VAN=VAN,dynamic=dynamic))
in_channel = out_channele
self.model = nn.Sequential(*layers)
def forward(self,x):
x = self.model(x)
return x