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IRB.py
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IRB.py
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import torch
import torch.nn as nn
from einops import rearrange
from config import opt
class LayerNormFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, weight, bias, eps):
ctx.eps = eps
N, C, H, W = x.size()
mu = x.mean(1, keepdim=True)
var = (x - mu).pow(2).mean(1, keepdim=True)
y = (x - mu) / (var + eps).sqrt()
ctx.save_for_backward(y, var, weight)
y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
return y
@staticmethod
def backward(ctx, grad_output):
eps = ctx.eps
N, C, H, W = grad_output.size()
y, var, weight = ctx.saved_variables
g = grad_output * weight.view(1, C, 1, 1)
mean_g = g.mean(dim=1, keepdim=True)
mean_gy = (g * y).mean(dim=1, keepdim=True)
gx = 1. / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
return gx, (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), grad_output.sum(dim=3).sum(dim=2).sum(
dim=0), None
class LayerNorm2d(nn.Module):
def __init__(self, channels, eps=1e-6):
super(LayerNorm2d, self).__init__()
self.register_parameter('weight', nn.Parameter(torch.ones(channels)))
self.register_parameter('bias', nn.Parameter(torch.zeros(channels)))
self.eps = eps
def forward(self, x):
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
class V_MaskAttention(nn.Module):
def __init__(self,input_channel):
super(V_MaskAttention, self).__init__()
self.conv1 = nn.Conv2d(in_channels=input_channel, out_channels=input_channel, kernel_size=1, padding=0, bias=False)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=input_channel, out_channels=input_channel, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(input_channel),
nn.Conv2d(in_channels=input_channel, out_channels=input_channel, kernel_size=5, padding=2, bias=False)
)
self.sigmoid = nn.Sigmoid()
def forward(self,x):
x2 = self.sigmoid(self.conv2(x))
att = x+(x * x2)
return att
class AttentionMs(nn.Module):
def __init__(self, dim,num_heads,drop):
super().__init__()
self.num_heads = num_heads
self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1)
self.qkv_dwconv = nn.Conv2d(dim*3, dim*3, kernel_size=3, stride=1, padding=1, groups=dim*3)
self.project_out = nn.Conv2d(dim, dim, kernel_size=1)
self.temperature = nn.Parameter(torch.ones(self.num_heads, 1, 1))
self.dropout = nn.Dropout(drop) if drop > 0. else nn.Identity()
self.maskattention = V_MaskAttention(input_channel = dim)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.qkv_dwconv(self.qkv(x))
q,k,v = qkv.chunk(3, dim=1)
v = self.maskattention(x)
q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
q = torch.nn.functional.normalize(q, dim=-1)
k = torch.nn.functional.normalize(k, dim=-1)
attn = (q @ k.transpose(-2, -1)) * self.temperature
attn = attn.softmax(dim=-1)
out = (attn @ v)
out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)
out = self.project_out(out)
return out
class Mlp(nn.Module):
def __init__(self,in_features,expansion,drop_out_rate):
super().__init__()
hidden_feature = in_features*expansion
self.mlp = nn.Sequential(
nn.Conv2d(in_features,hidden_feature,1,1,0),
nn.GELU(),
nn.Conv2d(hidden_feature, in_features, 1, 1, 0),
)
self.dropout = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity()
def forward(self,x):
res = x
x = self.dropout(self.mlp(x))
x = x + res
return x#(B,C,H,W)
class CSTB(nn.Module):
def __init__(self,ms_stem,expansion, num_heads,drop=0.3):
super().__init__()
self.att = AttentionMs(ms_stem,num_heads,drop)
self.mlp = Mlp(in_features=ms_stem, expansion=expansion,drop_out_rate = drop)
self.norm2d = LayerNorm2d(ms_stem)
def forward(self, x):#(B,C,H,W)
residual1 = x
x = self.norm2d(x)
x = self.att(x)
x = residual1+x
residual2 = x
x = self.norm2d(x)
x = self.mlp(x)
x = residual2+x
return x#(Batch_size,64,64,64)