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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class SqeezeExcite(nn.Module):
def __init__(self, channel, reduction_ratio = 16):
super(SqeezeExcite,self).__init__()
self.GAP = nn.AdaptiveAvgPool2d(1)
self.mlp = nn.Sequential(
nn.Linear(channel, channel//reduction_ratio, bias = False),
nn.ReLU(inplace=True),
nn.Linear(channel//reduction_ratio,channel,bias = False),
nn.Sigmoid()
)
def forward(self,x):
b,c,_,_ = x.size()
out = self.GAP(x).view(b,c)
out = self.mlp(out).view(b,c,1,1)
return x * out.expand_as(x)
class ECA(nn.Module):
# https://wandb.ai/diganta/ECANet-sweep/reports/Efficient-Channel-Attention--VmlldzozNzgwOTE
def __init__(self,channels, b = 1, gamma = 2):
super(ECA, self).__init__()
self.GAP = nn.AdaptiveAvgPool2d(1)
self.channels = channels
self.b = b
self.gamma = gamma
self.conv = nn.Conv1d(1, 1, kernel_size=self.adaptive_kernel(),padding = (self.adaptive_kernel()-1)//2, bias = False)
self.sigmoid = nn.Sigmoid()
def forward(self,x):
attn = self.GAP(x)
attn = self.conv(attn.squeeze(-1).transpose(-1,-2)).transpose(-1,-2).unsqueeze(-1)
attn = self.sigmoid(attn)
return x * attn.expand_as(x)
def adaptive_kernel(self):
k = int(abs(math.log2(self.channels)/self.gamma) + self.b)
ksize = k if k%2 else k+1
return ksize
class UNetConvBlock(nn.Module):
def __init__(self, in_channel, out_channel, ca_layer):
super(UNetConvBlock, self).__init__()
block = []
block.append(nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=1, padding=1))
block.append(nn.PReLU())
block.append(nn.Conv2d(out_channel, out_channel, kernel_size=3, padding=1, stride=1))
block.append(nn.PReLU())
if ca_layer:
block.append(ECA(out_channel))
self.block = nn.Sequential(*block)
def forward(self, x):
out = self.block(x)
return out
class AttentionGate(nn.Module):
def __init__(self, F_g, F_l, dimensions):
super(AttentionGate, self).__init__()
self.W_gate = nn.Sequential(
nn.Conv2d(F_g, dimensions, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(dimensions)
)
self.W_x = nn.Sequential(
nn.Conv2d(F_l, dimensions, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(dimensions)
)
self.psi = nn.Sequential(
nn.Conv2d(dimensions, 1, kernel_size=1, stride=1, padding=0, bias=True),
nn.BatchNorm2d(1),
nn.Sigmoid()
)
self.relu = nn.PReLU()
def forward(self, g, x):
g1 = self.W_gate(g)
x1 = self.W_x(x)
psi = self.relu(g1 + x1)
psi = self.psi(psi)
out = x * psi
return out
class UNetUpConvBlock(nn.Module):
def __init__(self, in_channel, out_channel, upmode, ca_layer, up_factor = 2, att_mode = "standard"):
super(UNetUpConvBlock, self).__init__()
self.att_mode = att_mode
self.ca_layer = ca_layer
if upmode == 'upsample':
self.Upsize = nn.Sequential(
nn.Upsample(scale_factor=up_factor, mode='bilinear', align_corners=False),
nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0),
)
elif upmode == 'upconv':
self.Upsize = nn.ConvTranspose2d(in_channel,out_channel,kernel_size=2,stride = 2)
elif upmode == 'shuffle':
self.Upsize = nn.Sequential(
nn.Conv2d(in_channel,out_channel*4,kernel_size=3,stride=1,padding=1),
nn.PReLU(),
nn.PixelShuffle(2),
nn.Conv2d(out_channel,out_channel,kernel_size=3,stride = 1,padding=1)
)
# self.conv = UNetConvBlock(in_channel, out_channel)
if self.att_mode == 'standard':
self.attention_gate = AttentionGate(out_channel, out_channel, out_channel)
self.conv = UNetConvBlock(in_channel, out_channel, ca_layer=self.ca_layer)
elif self.att_mode == 'modified':
self.attention_gate = AttentionGate(out_channel, out_channel, out_channel )
self.conv = UNetConvBlock(3*out_channel, out_channel, ca_layer = self.ca_layer)
elif self.att_mode == 'None':
self.conv = UNetConvBlock(in_channel, out_channel, ca_layer=self.ca_layer)
def forward(self, x, residue):
x = self.Upsize(x)
x = F.interpolate(x, size=residue.shape[2:], mode='bilinear')
if self.att_mode == "standard":
attn = self.attention_gate(g = x, x=residue)
out = torch.cat([x, attn],dim = 1)
out = self.conv(out)
elif self.att_mode == 'modified':
attn = self.attention_gate(g = x, x = residue)
out = torch.cat([x,residue,attn],dim = 1)
out = self.conv(out)
elif self.att_mode == 'None':
out = torch.cat([x,residue], dim = 1)
out = self.conv(out)
return out
class AUNet(nn.Module):
def __init__(self,in_channels = 6,out_channels = 6,depth = 3,growth_factor = 6,
interp_mode = 'bicubic', up_mode = 'upconv',spatial_attention = "standard", ca_layer = True):
super(AUNet,self).__init__()
if not spatial_attention in ['None', 'modified', 'standard']:
raise AssertionError("spatial_attention options : \'None\'- no spatial attention, \'standard\'-spatial attention as in attention unet paper, \'modified\'-modified attention unet")
self.in_channels = in_channels
self.out_channels = out_channels
self.depth = depth
self.growth_factor = growth_factor
self.interp_mode = interp_mode
prev_channels = self.in_channels
self.up_mode = up_mode
self.att_mode = spatial_attention
self.ca_layer = ca_layer
self.encoding_module = nn.ModuleList()
for i in range(self.depth):
self.encoding_module.append(UNetConvBlock(in_channel=prev_channels,out_channel=2**(self.growth_factor + i), ca_layer=self.ca_layer))
prev_channels = 2**(self.growth_factor+i)
self.decoding_module = nn.ModuleList()
for i in reversed(range(self.depth-1)):
self.decoding_module.append(UNetUpConvBlock(prev_channels,2**(self.growth_factor+i),upmode = self.up_mode, att_mode = self.att_mode, ca_layer = self.ca_layer))
prev_channels = 2**(self.growth_factor+i)
self.final = nn.Conv2d(prev_channels,out_channels,1,1,0)
def forward(self,MS,PAN = None):
if PAN == None:
x = MS
else:
x = torch.cat([MS,PAN],dim = 1)
blocks = []
for i,down in enumerate(self.encoding_module):
x = down(x)
if i != len(self.encoding_module)-1:
blocks.append(x)
x = F.avg_pool2d(x,2)
for i,up in enumerate(self.decoding_module):
x = up(x,blocks[-i-1])
x = self.final(x)
return x
if __name__ == '__main__':
x = torch.rand([9,7,256,256]).cuda()
model = AUNet(in_channels=7, out_channels=6, depth=5, spatial_attention="modified", growth_factor=6,
interp_mode='bilinear', up_mode='upconv', ca_layer=True).cuda()
x = model(x)
# print(model)
activation = {}
for layer in model:
print(layer) |