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import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True):
super(BasicConv, self).__init__()
self.out_channels = out_planes
if bn:
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)
self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True)
self.relu = nn.ReLU(inplace=True) if relu else None
else:
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
self.bn = None
self.relu = nn.ReLU(inplace=True) if relu else None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class BasicRFB(nn.Module):
def __init__(self, in_planes, out_planes, stride=1, scale=0.1, map_reduce=8, vision=1, groups=1):
super(BasicRFB, self).__init__()
self.scale = scale
self.out_channels = out_planes
inter_planes = in_planes // map_reduce
self.branch0 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 1, dilation=vision + 1, relu=False, groups=groups)
)
self.branch1 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
BasicConv(inter_planes, 2 * inter_planes, kernel_size=(3, 3), stride=stride, padding=(1, 1), groups=groups),
BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 2, dilation=vision + 2, relu=False, groups=groups)
)
self.branch2 = nn.Sequential(
BasicConv(in_planes, inter_planes, kernel_size=1, stride=1, groups=groups, relu=False),
BasicConv(inter_planes, (inter_planes // 2) * 3, kernel_size=3, stride=1, padding=1, groups=groups),
BasicConv((inter_planes // 2) * 3, 2 * inter_planes, kernel_size=3, stride=stride, padding=1, groups=groups),
BasicConv(2 * inter_planes, 2 * inter_planes, kernel_size=3, stride=1, padding=vision + 4, dilation=vision + 4, relu=False, groups=groups)
)
self.ConvLinear = BasicConv(6 * inter_planes, out_planes, kernel_size=1, stride=1, relu=False)
self.shortcut = BasicConv(in_planes, out_planes, kernel_size=1, stride=stride, relu=False)
self.relu = nn.ReLU(inplace=False)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
out = self.ConvLinear(out)
short = self.shortcut(x)
out = out * self.scale + short
out = self.relu(out)
return out
class Mb_Tiny_RFB(nn.Module):
def __init__(self, num_classes=2):
super(Mb_Tiny_RFB, self).__init__()
self.base_channel = 8 * 2
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, self.base_channel, 2), # 160*120
conv_dw(self.base_channel, self.base_channel * 2, 1),
conv_dw(self.base_channel * 2, self.base_channel * 2, 2), # 80*60
conv_dw(self.base_channel * 2, self.base_channel * 2, 1),
conv_dw(self.base_channel * 2, self.base_channel * 4, 2), # 40*30
conv_dw(self.base_channel * 4, self.base_channel * 4, 1),
conv_dw(self.base_channel * 4, self.base_channel * 4, 1),
BasicRFB(self.base_channel * 4, self.base_channel * 4, stride=1, scale=1.0),
conv_dw(self.base_channel * 4, self.base_channel * 8, 2), # 20*15
conv_dw(self.base_channel * 8, self.base_channel * 8, 1),
conv_dw(self.base_channel * 8, self.base_channel * 8, 1),
conv_dw(self.base_channel * 8, self.base_channel * 16, 2), # 10*8
conv_dw(self.base_channel * 16, self.base_channel * 16, 1)
)
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.model(x)
x = F.avg_pool2d(x, 7)
x = x.view(-1, 1024)
x = self.fc(x)
return x
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