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import torch
import torch.nn as nn
import math
'''https://github.com/blandocs/Tag2Pix/blob/master/model/pretrained.py'''
# Pretrained version
class Selayer(nn.Module):
def __init__(self, inplanes):
super(Selayer, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(inplanes, inplanes // 16, kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(inplanes // 16, inplanes, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.global_avgpool(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.sigmoid(out)
return x * out
class BottleneckX_Origin(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None):
super(BottleneckX_Origin, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes * 2)
self.conv2 = nn.Conv2d(planes * 2, planes * 2, kernel_size=3, stride=stride,
padding=1, groups=cardinality, bias=False)
self.bn2 = nn.BatchNorm2d(planes * 2)
self.conv3 = nn.Conv2d(planes * 2, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.selayer = Selayer(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.selayer(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SEResNeXt_extractor(nn.Module):
def __init__(self, block, layers, input_channels=3, cardinality=32):
super(SEResNeXt_extractor, self).__init__()
self.cardinality = cardinality
self.inplanes = 64
self.input_channels = input_channels
self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, self.cardinality, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, self.cardinality))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
return x
def get_seresnext_extractor():
return SEResNeXt_extractor(BottleneckX_Origin, [3, 4, 6, 3], 1) |