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MSFCN2D.py
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MSFCN2D.py
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from torch import nn
import torch
def conv3otherRelu(in_planes, out_planes, kernel_size=None, stride=None, padding=None):
if kernel_size is None:
kernel_size = 3
if stride is None:
stride = 1
if padding is None:
padding = 1
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=True),
nn.ReLU(inplace=True)
)
class DualConvLayer2D(nn.Module):
def __init__(self, in_planes, out_planes):
super(DualConvLayer2D, self).__init__()
self.conv1a = nn.Sequential(
nn.Conv2d(in_channels=in_planes, out_channels=out_planes, padding=0,
kernel_size=1, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
self.conv1b = nn.Sequential(
nn.Conv2d(in_channels=out_planes, out_channels=out_planes, padding=1,
kernel_size=3, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
self.conv2a = nn.Sequential(
nn.Conv2d(in_channels=in_planes, out_channels=out_planes, padding=0,
kernel_size=1, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
self.conv2b = nn.Sequential(
nn.Conv2d(in_channels=out_planes, out_channels=out_planes, padding=1,
kernel_size=3, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
self.conv2c = nn.Sequential(
nn.Conv2d(in_channels=out_planes, out_channels=out_planes, padding=1,
kernel_size=3, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=out_planes, out_channels=out_planes, padding=0,
kernel_size=1, stride=1),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True)
)
def forward(self, X):
x2l = self.conv1a(X)
x2l = self.conv1b(x2l)
x2r = self.conv2a(X)
x2r = self.conv2b(x2r)
x2r = self.conv2c(x2r)
x10 = torch.add(x2l, x2r)
return self.conv3(x10)
class CMA2D(nn.Module):
def __init__(self, in_channels):
super(CMA2D, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv1_1 = nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1)
def forward(self, x):
x1 = self.avg_pool(x)
x1 = self.conv1_1(x1)
x1 = self.relu(x1)
x1 = self.conv1_2(x1)
x1 = self.sigmoid(x1)
x2 = x * x1
x2 = x + x2
x2 = self.conv2(x2)
return x2
class MSFCN2D(nn.Module):
def __init__(self, time_num, band_num, class_num):
super(MSFCN2D, self).__init__()
self.band_num = time_num * band_num
self.class_num = class_num
self.name = 'MSFCN2D'
channels = [64, 128, 256, 512]
self.conv1 = nn.Sequential(
DualConvLayer2D(self.band_num, channels[0])
)
self.conv2 = nn.Sequential(
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),
DualConvLayer2D(channels[0], channels[1])
)
self.conv3 = nn.Sequential(
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),
DualConvLayer2D(channels[1], channels[2])
)
self.conv4 = nn.Sequential(
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),
DualConvLayer2D(channels[2], channels[3])
)
self.down_channel1 = nn.Conv2d(
channels[3], channels[3], kernel_size=1, stride=1)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.down_channel2 = nn.Conv2d(
channels[3], channels[3], kernel_size=1, stride=1)
self.sigmoid = nn.Sigmoid()
self.down_channel3 = nn.Conv2d(channels[3], channels[3], kernel_size=1, stride=1)
self.deconv3 = nn.ConvTranspose2d(channels[3], channels[2], kernel_size=(2, 2), stride=(2, 2))
self.conv5 = nn.Sequential(
CMA2D(channels[3]),
conv3otherRelu(channels[3], channels[2]),
)
self.deconv2 = nn.ConvTranspose2d(channels[2], channels[1], kernel_size=(2, 2), stride=(2, 2))
self.conv6 = nn.Sequential(
CMA2D(channels[2]),
conv3otherRelu(channels[2], channels[1]),
)
self.deconv1 = nn.ConvTranspose2d(channels[1], channels[0], kernel_size=(2, 2), stride=(2, 2))
self.conv7 = nn.Sequential(
CMA2D(channels[1]),
conv3otherRelu(channels[1], channels[0], kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
)
self.conv8 = nn.Conv2d(channels[0], self.class_num, kernel_size=1, stride=1)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2)
conv4 = self.conv4(conv3)
x1 = self.down_channel1(conv4)
x2 = self.avg_pool(x1)
x2 = self.down_channel2(x2)
x2 = self.sigmoid(x2)
x2 = x2 * x1 + x1
x2 = self.down_channel3(x2)
deconv3 = self.deconv3(x2)
conv5 = torch.cat((deconv3, conv3), 1)
conv5 = self.conv5(conv5)
deconv2 = self.deconv2(conv5)
conv6 = torch.cat((deconv2, conv2), 1)
conv6 = self.conv6(conv6)
deconv1 = self.deconv1(conv6)
conv7 = torch.cat((deconv1, conv1), 1)
conv7 = self.conv7(conv7)
output = self.conv8(conv7)
return output
if __name__ == '__main__':
batch_size = 8
height = 64
weight = 64
time_num = 4
band_num = 4
class_num = 4
x = torch.randn(batch_size, time_num * band_num, height, weight).cuda()
model = MSFCN2D(time_num, band_num, class_num).cuda()
y = model(x)