import math
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch import nn
from torch.nn import Parameter
from torchsummary import summary
from config import device, num_classes
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.PReLU()(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
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=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.PReLU()
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SEBlock(nn.Module):
def __init__(self, channel, reduction=16):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.PReLU(),
nn.Linear(channel // reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class IRBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
super(IRBlock, self).__init__()
self.bn0 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, inplanes)
self.bn1 = nn.BatchNorm2d(inplanes)
self.prelu = nn.PReLU()
self.conv2 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.use_se = use_se
if self.use_se:
self.se = SEBlock(planes)
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.conv1(out)
out = self.bn1(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.use_se:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.prelu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, use_se=True):
self.inplanes = 64
self.use_se = use_se
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.prelu = nn.PReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.bn2 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout()
# self.fc = nn.Linear(512 * 7 * 6, 512) #修改尺寸
self.fc = nn.Linear(512 * 7 * 7, 512)
self.bn3 = nn.BatchNorm1d(512)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0)
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, stride, downsample, use_se=self.use_se))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, use_se=self.use_se))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
x = self.bn3(x)
# x = F.normalize(x)
return x
def resnet18(args, **kwargs):
model = ResNet(IRBlock, [2, 2, 2, 2], use_se=args.use_se, **kwargs)
if args.pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(args, **kwargs):
model = ResNet(IRBlock, [3, 4, 6, 3], use_se=args.use_se, **kwargs)
if args.pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(args, **kwargs):
model = ResNet(IRBlock, [3, 4, 6, 3], use_se=args.use_se, **kwargs)
if args.pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(args, **kwargs):
model = ResNet(IRBlock, [3, 4, 23, 3], use_se=args.use_se, **kwargs)
if args.pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(args, **kwargs):
model = ResNet(IRBlock, [3, 8, 36, 3], use_se=args.use_se, **kwargs)
if args.pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def resnet_face18(use_se=True, **kwargs):
model = ResNet(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)
return model
class MobileNet(nn.Module):
def __init__(self, alpha):
self.alpha = alpha
super(MobileNet, self).__init__()
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU(inplace=True)
)
def conv_dw(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
nn.ReLU(inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU(inplace=True),
)
self.model = nn.Sequential(
conv_bn(3, int(32 * self.alpha), 2),
conv_dw(int(32 * self.alpha), int(64 * self.alpha), 1),
conv_dw(int(64 * self.alpha), int(128 * self.alpha), 2),
conv_dw(int(128 * self.alpha), int(128 * self.alpha), 1),
conv_dw(int(128 * self.alpha), int(256 * self.alpha), 2),
conv_dw(int(256 * self.alpha), int(256 * self.alpha), 1),
conv_dw(int(256 * self.alpha), int(512 * self.alpha), 2),
conv_dw(int(512 * self.alpha), int(512 * self.alpha), 1),
conv_dw(int(512 * self.alpha), int(512 * self.alpha), 1),
conv_dw(int(512 * self.alpha), int(512 * self.alpha), 1),
conv_dw(int(512 * self.alpha), int(512 * self.alpha), 1),
conv_dw(int(512 * self.alpha), int(512 * self.alpha), 1),
conv_dw(int(512 * self.alpha), int(1024 * self.alpha), 2),
conv_dw(int(1024 * self.alpha), int(1024 * self.alpha), 1),
)
self.bn2 = nn.BatchNorm2d(1024)
self.dropout = nn.Dropout()
self.fc = nn.Linear(1024 * 4 * 4, 512)
self.bn3 = nn.BatchNorm1d(512)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.model(x)
x = self.bn2(x)
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
x = self.bn3(x)
return x
class ArcMarginModel(nn.Module):
def __init__(self, args):
super(ArcMarginModel, self).__init__()
self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
nn.init.xavier_uniform_(self.weight)
self.easy_margin = args.easy_margin
self.m = args.margin_m
self.s = args.margin_s
self.cos_m = math.cos(self.m)
self.sin_m = math.sin(self.m)
self.th = math.cos(math.pi - self.m)
self.mm = math.sin(math.pi - self.m) * self.m
def forward(self, input, label):
x = F.normalize(input)
W = F.normalize(self.weight)
cosine = F.linear(x, W)
sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
phi = cosine * self.cos_m - sine * self.sin_m # cos(theta + m)
if self.easy_margin:
phi = torch.where(cosine > 0, phi, cosine)
else:
phi = torch.where(cosine > self.th, phi, cosine - self.mm)
one_hot = torch.zeros(cosine.size(), device=device)
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
return output
if __name__ == "__main__":
# args = parse_args()
# model = resnet152(args).to(device)
model = MobileNet(1.0).to(device)
summary(model, (3, 112, 112))
