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FCN.py
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FCN.py
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import torch
import torch.nn as nn
import torchvision
class FCN8s(nn.Module):
def __init__(self, num_classes):
super(FCN8s, self).__init__()
vgg = torchvision.models.vgg16()
features = list(vgg.features.children())
self.padd = nn.ZeroPad2d([100,100,100,100])
self.pool3 = nn.Sequential(*features[:17])
self.pool4 = nn.Sequential(*features[17:24])
self.pool5 = nn.Sequential(*features[24:])
self.pool3_conv1x1 = nn.Conv2d(256, num_classes, kernel_size=1)
self.pool4_conv1x1 = nn.Conv2d(512, num_classes, kernel_size=1)
self.output5 = nn.Sequential(
nn.Conv2d(512, 4096, kernel_size=7),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Conv2d(4096, 4096, kernel_size=1),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Conv2d(4096, num_classes, kernel_size=1),
)
self.up_pool3_out = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=16, stride=8)
self.up_pool4_out = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2)
self.up_pool5_out = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2)
def forward(self, x):
_,_, w, h = x.size()
x = self.padd(x)
pool3 = self.pool3(x)
pool4 = self.pool4(pool3)
pool5 = self.pool5(pool4)
output5 = self.up_pool5_out(self.output5(pool5))
pool4_out = self.pool4_conv1x1(0.01 * pool4)
output4 = self.up_pool4_out(pool4_out[:,:,5:(5 + output5.size()[2]) ,5:(5 + output5.size()[3])]+output5)
pool3_out = self.pool3_conv1x1(0.0001 * pool3)
output3 = self.up_pool3_out(pool3_out[:, :, 9:(9 + output4.size()[2]), 9:(9 + output4.size()[3])] + output4)
out = self.up_pool3_out(output3)
out = out[:, :, 31: (31 + h), 31: (31 + w)].contiguous()
return out
if __name__ == '__main__':
model = FCN8s(num_classes=20)
print(model)
input = torch.randn(1,3,224,224)
output = model(input)
print(output.shape)