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DAMDNet.py
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DAMDNet.py
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# author jiang
# -*- coding:utf-8-*-
import torch.nn as nn
import torch
# import torch.nn.parameter as Parameter
from torch.nn import Parameter
from utils.se_module import SELayer
import cv2
import math
__all__ = ['DMDNet']
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups = 64):
super(SpatialGroupEnhance, self).__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = Parameter(torch.zeros(1, groups, 1, 1))
self.bias = Parameter(torch.ones(1, groups, 1, 1))
self.sig = nn.Sigmoid()
def forward(self, x): # (b, c, h, w)
# y=torch.squeeze(x,0)
# y=torch.unsqueeze(y.permute(1,2,0)[:,:,1],2).numpy()
# y=cv2.resize(y,(520,520),cv2.INTER_LINEAR)
# cv2.imshow("test_1", y)
# cv2.waitKey(0)
b, c, h, w=x.size()
x = x.view(b * self.groups, -1, h, w)
xn = x * self.avg_pool(x)
xn = xn.sum(dim=1, keepdim=True)
t = xn.view(b * self.groups, -1)
t = t - t.mean(dim=1, keepdim=True)
std = t.std(dim=1, keepdim=True) + 1e-5
t = t / std
t = t.view(b, self.groups, h, w)
t = t * self.weight + self.bias
t = t.view(b * self.groups, 1, h, w)
x = x * self.sig(t)
# z=torch.squeeze(x, 0)
# z=torch.unsqueeze(z.permute(1, 2, 0)[:, :, 1], 2).numpy()
# z=cv2.resize(z, (520, 520), cv2.INTER_LINEAR)
# cv2.imshow("test_2", z)
# cv2.waitKey(0)
x = x.view(b, c, h, w)
return x
class Bottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, downsample=None, transition_layer=None,expansion=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, inplanes*expansion, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(inplanes*expansion)
self.conv2 = nn.Conv2d(inplanes*expansion, inplanes*expansion, kernel_size=3, stride=stride,
padding=1, bias=False, groups=inplanes*expansion)
self.bn2 = nn.BatchNorm2d(inplanes*expansion)
self.conv3 = nn.Conv2d(inplanes*expansion, planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.transition_layer=transition_layer
self.stride = stride
self.sge = SpatialGroupEnhance(int(planes/2))
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.sge(out)
if self.downsample is not None:
residual=self.downsample(x)
out=torch.cat([out,residual],1)
out=self.relu(out)
out=self.transition_layer(out)
return out
class DAMDNet(nn.Module):
def __init__(self, block, layers, num_classes=62):
self.inplanes = 32
super(DAMDNet, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(32)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, layers[0], stride=1, expansion = 1)
self.seLayer1 = SELayer(16)
self.layer2 = self._make_layer(block, 24, layers[1], stride=2, expansion = 6)
self.seLayer2 = SELayer(24)
self.layer3 = self._make_layer(block, 32, layers[2], stride=2, expansion = 6)
self.seLayer3 = SELayer(32)
self.layer4 = self._make_layer(block, 64, layers[3], stride=2, expansion = 6)
self.seLayer4 = SELayer(64)
self.layer5 = self._make_layer(block, 96, layers[4], stride=1, expansion = 6)
self.seLayer5 = SELayer(96)
self.layer6 = self._make_layer(block, 160, layers[5], stride=2, expansion = 6)
self.seLayer6 = SELayer(160)
self.layer7 = self._make_layer(block, 320, layers[6], stride=1, expansion = 6)
self.seLayer7 = SELayer(320)
self.conv8 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, bias=False)
self.avgpool = nn.AvgPool2d(4, stride=1)
self.conv9 = nn.Conv2d(1280,num_classes, kernel_size=1, stride=1, bias=False)
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))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _transition_layer(self,inplanes, planes,stride=1):
transition_layer=nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes),
nn.ReLU(inplace=True))
return transition_layer
def _downsample(self,inplanes,planes,kernel_size=1, stride=1, bias=False):
downsample=nn.Sequential(nn.Conv2d(self.inplanes + planes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes), )
return downsample
def _make_layer(self, block, planes, blocks, stride, expansion):
downsample=nn.Sequential(nn.Conv2d(self.inplanes, self.inplanes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.inplanes), )
layers = []
transition_layer=self._downsample(self.inplanes+planes, planes, kernel_size=1, stride=1, bias=False)
layers.append(block(self.inplanes, planes, stride=stride, downsample=downsample,transition_layer=transition_layer,expansion=expansion))
self.inplanes = planes
for i in range(1, blocks):
transition_layer=self._downsample(self.inplanes + planes, planes, kernel_size=1, stride=1, bias=False)
layers.append(block(self.inplanes, planes, transition_layer=transition_layer,expansion=expansion))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.seLayer1(x)
x = self.layer2(x)
x = self.seLayer2(x)
x = self.layer3(x)
x = self.seLayer3(x)
x = self.layer4(x)
x = self.seLayer4(x)
x = self.layer5(x)
x = self.seLayer5(x)
x = self.layer6(x)
x = self.seLayer6(x)
x = self.layer7(x)
x = self.seLayer7(x)
x = self.conv8(x)
x = self.avgpool(x)
x = self.conv9(x)
x = x.view(x.size(0),-1)
return x
def DAMDNet_v1(**kwargs):
"""Constructs a MobileNetV2-19 add SE-Module model.
"""
model = DAMDNet(Bottleneck, [1, 2, 3, 4, 3, 3, 1], **kwargs)
return model