`
import torch.nn as nn
import torch
import torch.nn.functional as F
class ModifyPPM(nn.Module):
def init(self, in_dim, reduction_dim, bins):
super(ModifyPPM, self).init()
self.features = []
for bin in bins:
self.features.append(nn.Sequential(
nn.AdaptiveAvgPool2d(bin),
nn.Conv2d(in_dim, reduction_dim, kernel_size=1),
nn.GELU(),
nn.Conv2d(reduction_dim, reduction_dim, kernel_size=3, bias=False, groups=reduction_dim),
nn.GELU()
))
self.features = nn.ModuleList(self.features)
self.local_conv = nn.Sequential(
nn.Conv2d(in_dim, in_dim, kernel_size=3, padding=1, bias=False, groups=in_dim),
nn.GELU(),
)
def forward(self, x):
x_size = x.size()
out = [self.local_conv(x)]
for f in self.features:
out.append(F.interpolate(f(x), x_size[2:], mode='bilinear', align_corners=True))
return torch.cat(out, 1)
class LMSA(nn.Module):
def init(self, in_dim, hidden_dim, patch_num):
super().init()
self.down_project = nn.Linear(in_dim,hidden_dim)
self.act = nn.GELU()
self.mppm = ModifyPPM(hidden_dim, hidden_dim //4, [3,6,9,12])
self.patch_num = patch_num
self.up_project = nn.Linear(hidden_dim, in_dim)
self.down_conv = nn.Sequential(nn.Conv2d(hidden_dim*2, hidden_dim, 1),
nn.GELU())
def forward(self, x):
down_x = self.down_project(x)
down_x = self.act(down_x)
down_x = down_x.permute(0, 3, 1, 2).contiguous()
down_x = self.mppm(down_x).contiguous()
down_x = self.down_conv(down_x)
down_x = down_x.permute(0, 2, 3, 1).contiguous()
up_x = self.up_project(down_x)
return x + up_x
if name == 'main':
in_dim=128
hidden_dim=64
patch_num=16
block = LMSA(in_dim,hidden_dim,patch_num).cuda()
input = torch.randn(1, patch_num, patch_num, in_dim).cuda() #输入 B C H W
output = block(input)
print(input.size())
print(output.size())
`
