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model_vgg16.py
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model_vgg16.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from utils import drop_path
class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=(3 * dilation - 1) // 2,
bias=False,
dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.downsample = downsample
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)), inplace=True)
out = F.relu(self.bn2(self.conv2(out)), inplace=True)
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
return F.relu(out + residual, inplace=True)
class vgg16(nn.Module):
def __init__(self,):
super(vgg16, self).__init__()
# conv1
self.conv1_1 = nn.Conv2d(3, 64, 3, padding=1)
self.bn1_1 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.bn1_2 = nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2 2 layers
# conv2
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.bn2_1 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.bn2_2 = nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4 2 layers
# conv3
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.bn3_1 = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.bn3_2 = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.bn3_3 = nn.BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8 4 layers
# conv4
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.bn4_1 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.bn4_2 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.bn4_3 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16 4 layers
# conv5
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.bn5_1 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.bn5_2 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.bn5_3 = nn.BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
self.relu5_3 = nn.ReLU(inplace=True) # 1/32 4 layers
out_channel = 128
self.conv5 = nn.Conv2d(512, out_channel, kernel_size=3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2d(out_channel)
self.conv4 = nn.Conv2d(512, out_channel, kernel_size=3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2d(out_channel)
self.conv3 = nn.Conv2d(256, out_channel, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(out_channel)
self.conv2 = nn.Conv2d(128, out_channel, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(out_channel)
def forward(self, x):
h = x
h = self.relu1_1(self.bn1_1(self.conv1_1(h)))
h = self.relu1_2(self.bn1_2(self.conv1_2(h)))
h_nopool1 = h
h = self.pool1(h)
# pool1 = h
h = self.relu2_1(self.bn2_1(self.conv2_1(h)))
h = self.relu2_2(self.bn2_2(self.conv2_2(h)))
h_nopool2 = h
h = self.pool2(h)
# pool2 = h
h = self.relu3_1(self.bn3_1(self.conv3_1(h)))
h = self.relu3_2(self.bn3_2(self.conv3_2(h)))
h = self.relu3_3(self.bn3_3(self.conv3_3(h)))
h_nopool3 = h
h = self.pool3(h)
# pool3 = h
h = self.relu4_1(self.bn4_1(self.conv4_1(h)))
h = self.relu4_2(self.bn4_2(self.conv4_2(h)))
h = self.relu4_3(self.bn4_3(self.conv4_3(h)))
h_nopool4 = h
h = self.pool4(h)
h = self.relu5_1(self.bn5_1(self.conv5_1(h)))
h = self.relu5_2(self.bn5_2(self.conv5_2(h)))
h = self.relu5_3(self.bn5_3(self.conv5_3(h)))
out2 = F.relu(self.bn2(self.conv2(h_nopool2)), inplace=True)
out3 = F.relu(self.bn3(self.conv3(h_nopool3)), inplace=True)
out4 = F.relu(self.bn4(self.conv4(h_nopool4)), inplace=True)
out5_ = F.relu(self.bn5(self.conv5(h)), inplace=True)
return out5_, out2, out3, out4
class Featurefusioncell43(nn.Module):
def __init__(self, standardShape, channel, op):
super(Featurefusioncell43, self).__init__()
self.standardShape = standardShape
self._ops = op
self.conv11 = nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1)
self.bn11 = nn.BatchNorm2d(channel)
# self.initialize()
def initialize(self):
weight_init(self)
def forward(self, fea, lowfeature):
f2 = fea[0]
levelfusion = fea[2]
f3 = fea[1]
f4 = fea[3]
if levelfusion.size()[2:] != self.standardShape:
levelfusion = F.interpolate(levelfusion, self.standardShape, mode='bilinear')
if lowfeature.size()[2:] != self.standardShape:
lowfeature = F.interpolate(lowfeature, self.standardShape, mode='bilinear')
if f2.size()[3] != self.standardShape and f2.size() != torch.Size([]):
f2 = F.interpolate(f2, self.standardShape, mode='bilinear')
if f3.size()[3] != self.standardShape and f3.size() != torch.Size([]):
f3 = F.interpolate(f3, self.standardShape, mode='bilinear')
if f4.size()[3] != self.standardShape:
f4 = F.interpolate(f4, self.standardShape, mode='bilinear')
z1 = f2
z2 = f3
z3 = levelfusion
z4 = f4
pre_note = [lowfeature]
states = [z1, z2, z3, z4]
offset = 0
for i in range(4):
if i == 0:
s0 = states[i]
s1 = self._ops[offset + i](pre_note[i])
add = s0 + s1
pre_note.append(add)
else:
p1 = states[i]
s0 = self._ops[offset + i](pre_note[i])
s1 = self._ops[offset + i + 1](states[i])
add = s0 + s1 + p1
pre_note.append(add)
offset += 1
out = 0
for i in range(1, 5):
out += pre_note[i]
out = F.relu(self.bn11(self.conv11(out)), inplace=True)
return out
class Featurefusioncell32(nn.Module):
def __init__(self, standardShape, channel, op):
super(Featurefusioncell32, self).__init__()
self.standardShape = standardShape
self._ops = op
self.conv11 = nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1)
self.bn11 = nn.BatchNorm2d(channel)
def initialize(self):
weight_init(self)
def forward(self, fea, lowfeature):
f2 = fea[0]
levelfusion = fea[3]
f3 = fea[1]
f4 = fea[2]
if levelfusion.size()[2:] != self.standardShape:
levelfusion = F.interpolate(levelfusion, self.standardShape, mode='bilinear')
if lowfeature.size()[2:] != self.standardShape:
lowfeature = F.interpolate(lowfeature, self.standardShape, mode='bilinear')
if f2.size()[3] != self.standardShape and f2.size() != torch.Size([]):
f2 = F.interpolate(f2, self.standardShape, mode='bilinear')
if f3.size()[3] != self.standardShape and f3.size() != torch.Size([]):
f3 = F.interpolate(f3, self.standardShape, mode='bilinear')
if f4.size()[3] != self.standardShape:
f4 = F.interpolate(f4, self.standardShape, mode='bilinear')
z1 = f2
z2 = f3
z3 = f4
z4 = levelfusion
pre_note = [lowfeature]
states = [z1, z2, z3, z4]
offset = 0
for i in range(4):
if i == 0:
s0 = states[i]
s1 = self._ops[offset + i](pre_note[i])
add = s0 + s1
pre_note.append(add)
else:
p1 = states[i]
s0 = self._ops[offset + i](pre_note[i])
s1 = self._ops[offset + i + 1](states[i])
add = s0 + s1 + p1
pre_note.append(add)
offset += 1
out = 0
for i in range(1, 5):
out += pre_note[i]
out = F.relu(self.bn11(self.conv11(out)), inplace=True)
return out
class Featurefusioncell54(nn.Module):
def __init__(self, standardShape, channel, op):
super(Featurefusioncell54, self).__init__()
self.standardShape = standardShape
self._ops = op
self.conv11 = nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1)
self.bn11 = nn.BatchNorm2d(channel)
def initialize(self):
weight_init(self)
def forward(self, fea):
lowfeature = fea[0]
levelfusion = fea[1]
f2 = fea[2]
f3 = fea[3]
if levelfusion.size()[2:] != self.standardShape:
levelfusion = F.interpolate(levelfusion, self.standardShape, mode='bilinear')
if lowfeature.size()[2:] != self.standardShape:
lowfeature = F.interpolate(lowfeature, self.standardShape, mode='bilinear')
if f2.size()[3] != self.standardShape and f2.size() != torch.Size([]):
f2 = F.interpolate(f2, self.standardShape, mode='bilinear')
if f3.size()[3] != self.standardShape and f3.size() != torch.Size([]):
f3 = F.interpolate(f3, self.standardShape, mode='bilinear')
z1 = lowfeature
z2 = levelfusion
z3 = f2
z4 = f3
pre_note = [z1]
states = [z2, z3, z4]
offset = 0
for i in range(4 - 1):
if i == 0:
s0 = states[i]
s1 = self._ops[offset + i](pre_note[i])
add = s0 + s1
pre_note.append(add)
else:
p1 = states[i]
s0 = self._ops[offset + i](pre_note[i])
s1 = self._ops[offset + i + 1](states[i])
add = s0 + s1 + p1
pre_note.append(add)
offset += 1
out = 0
for i in range(1, 4):
out += pre_note[i]
out = F.relu(self.bn11(self.conv11(out)), inplace=True)
return out
class FeatureFusion(nn.Module):
def __init__(self, genotype_fusion, node=3):
super(FeatureFusion, self).__init__()
self._ops = nn.ModuleList()
self.fnum = 4
self.node = node
# self.none_num = []
C = 128
genotype_ouside = genotype_fusion.normal
genotype_inside = genotype_fusion.inside
new_genotype_ouside = sorted(genotype_ouside, key=lambda x: (x[2], x[1]))
op_name, op_num, _ = zip(*new_genotype_ouside)
self.op_num = op_num
offset = 0
for i in range(self.node):
for j in range(self.fnum):
op = OPS[op_name[j + offset]](C, C, 1,False, True)
self._ops += [op]
offset += 4
op_name_inside, op_num_inside = zip(*genotype_inside)
k = [5, 7, 7]
noteOper = []
offset = 0
for i in range(self.node):
self._nodes = nn.ModuleList()
for j in range(k[i]):
op = OPS[op_name_inside[j + offset]](C, C, 1,False, True)
self._nodes += [op]
noteOper.append(self._nodes)
offset += k[i]
self.featurefusioncell54 = Featurefusioncell54(16, C, noteOper[0])
self.featurefusioncell43 = Featurefusioncell43(32, C, noteOper[1])
self.featurefusioncell32 = Featurefusioncell32(64, C, noteOper[2])
def forward(self, out5, out2, out3, out4):
states = [out5, out4, out3, out2]
fea = []
feaoutput = []
offset = 0
s = 0
for i in range(self.node):
for j, v in enumerate(self.op_num):
if j == 4:
break
inputFea = states[v]
x2 = self._ops[offset + j](inputFea)
fea.append(x2)
if i == 0:
new_fea = self.featurefusioncell54(fea)
feaoutput.append(new_fea)
fea.clear()
elif i == 1:
new_fea = self.featurefusioncell43(fea, feaoutput[0])
feaoutput.append(new_fea)
fea.clear()
elif i == 2:
new_fea = self.featurefusioncell32(fea, feaoutput[1])
feaoutput.append(new_fea)
fea.clear()
offset += 4
return feaoutput[2],feaoutput[1],feaoutput[0]
def _loss(self, input, target):
logits = self(input)
logits = logits.squeeze(1)
return self._criterion(logits, target)
class Network_vgg16(nn.Module):
def __init__(self, genotype_fusion):
super(Network_vgg16, self).__init__()
# self.vgg16 = Vgg16_RGB()
self.vgg16 = vgg16()
self.feafusion = FeatureFusion(genotype_fusion)
self.conv44 = nn.Conv2d(128, 1, kernel_size=3, stride=1, padding=1)
self.conv55 = nn.Conv2d(128, 1, kernel_size=3, stride=1, padding=1)
self.conv66 = nn.Conv2d(128, 1, kernel_size=3, stride=1, padding=1)
def forward(self, input):
h_, h_nopool2, h_nopool3, h_nopool4 = self.vgg16(input)
h_nopool2,h_nopool3,h_nopool4 = self.feafusion(h_, h_nopool2, h_nopool3, h_nopool4)
h_nopool2 = F.interpolate(self.conv44(h_nopool2), size=[256, 256], mode='bilinear')
h_nopool3 = F.interpolate(self.conv55(h_nopool3), size=[256, 256], mode='bilinear')
h_nopool4 = F.interpolate(self.conv66(h_nopool4), size=[256, 256], mode='bilinear')
return h_nopool2,h_nopool3,h_nopool4