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gcn.py
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gcn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.utils.model_zoo as model_zoo
from torchvision import models
import math
class GCN(nn.Module):
def __init__(self, inplanes, planes, ks=7):
super(GCN, self).__init__()
self.conv_l1 = nn.Conv2d(inplanes, planes, kernel_size=(ks, 1),
padding=(ks/2, 0))
self.conv_l2 = nn.Conv2d(planes, planes, kernel_size=(1, ks),
padding=(0, ks/2))
self.conv_r1 = nn.Conv2d(inplanes, planes, kernel_size=(1, ks),
padding=(0, ks/2))
self.conv_r2 = nn.Conv2d(planes, planes, kernel_size=(ks, 1),
padding=(ks/2, 0))
def forward(self, x):
x_l = self.conv_l1(x)
x_l = self.conv_l2(x_l)
x_r = self.conv_r1(x)
x_r = self.conv_r2(x_r)
x = x_l + x_r
return x
class Refine(nn.Module):
def __init__(self, planes):
super(Refine, self).__init__()
self.bn = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
def forward(self, x):
residual = x
x = self.bn(x)
x = self.relu(x)
x = self.conv1(x)
x = self.bn(x)
x = self.relu(x)
x = self.conv2(x)
out = residual + x
return out
class FCN(nn.Module):
def __init__(self, num_classes):
super(FCN, self).__init__()
self.num_classes = num_classes
resnet = models.resnet50(pretrained=True)
self.conv1 = resnet.conv1
self.bn0 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
self.gcn1 = GCN(2048, self.num_classes)
self.gcn2 = GCN(1024, self.num_classes)
self.gcn3 = GCN(512, self.num_classes)
self.gcn4 = GCN(64, self.num_classes)
self.gcn5 = GCN(64, self.num_classes)
self.refine1 = Refine(self.num_classes)
self.refine2 = Refine(self.num_classes)
self.refine3 = Refine(self.num_classes)
self.refine4 = Refine(self.num_classes)
self.refine5 = Refine(self.num_classes)
self.refine6 = Refine(self.num_classes)
self.refine7 = Refine(self.num_classes)
self.refine8 = Refine(self.num_classes)
self.refine9 = Refine(self.num_classes)
self.refine10 = Refine(self.num_classes)
self.out0 = self._classifier(2048)
self.out1 = self._classifier(1024)
self.out2 = self._classifier(512)
self.out_e = self._classifier(256)
self.out3 = self._classifier(64)
self.out4 = self._classifier(64)
self.out5 = self._classifier(32)
self.transformer = nn.Conv2d(256, 64, kernel_size=1)
def _classifier(self, inplanes):
return nn.Sequential(
nn.Conv2d(inplanes, inplanes, 3, padding=1, bias=False),
nn.BatchNorm2d(inplanes/2),
nn.ReLU(inplace=True),
nn.Dropout(.1),
nn.Conv2d(inplanes/2, self.num_classes, 1),
)
def forward(self, x):
input = x
x = self.conv1(x)
x = self.bn0(x)
x = self.relu(x)
conv_x = x
x = self.maxpool(x)
pool_x = x
fm1 = self.layer1(x)
fm2 = self.layer2(fm1)
fm3 = self.layer3(fm2)
fm4 = self.layer4(fm3)
gcfm1 = self.refine1(self.gcn1(fm4))
gcfm2 = self.refine2(self.gcn2(fm3))
gcfm3 = self.refine3(self.gcn3(fm2))
gcfm4 = self.refine4(self.gcn4(pool_x))
gcfm5 = self.refine5(self.gcn5(conv_x))
fs1 = self.refine6(F.upsample_bilinear(gcfm1, fm3.size()[2:]) + gcfm2)
fs2 = self.refine7(F.upsample_bilinear(fs1, fm2.size()[2:]) + gcfm3)
fs3 = self.refine8(F.upsample_bilinear(fs2, pool_x.size()[2:]) + gcfm4)
fs4 = self.refine9(F.upsample_bilinear(fs3, conv_x.size()[2:]) + gcfm5)
out = self.refine10(F.upsample_bilinear(fs4, input.size()[2:]))
return out, fs4, fs3, fs2, fs1, gcfm1