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import torch
import torch.nn as nn
# basic
class Conv(nn.Module):
# basic convlution
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, act=nn.ReLU(),
bias=False, auto_padding=True, padding_mode='zeros'):
super().__init__()
# not yet handle the case when dilation is a tuple
if auto_padding:
if isinstance(kernel_size, int):
padding = (dilation * (kernel_size - 1) + 1) // 2
else:
padding = [(dilation * (k - 1) + 1) // 2 for k in kernel_size]
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, dilation=dilation, bias=bias)
self.bn = nn.BatchNorm2d(out_channels)
self.act = act if isinstance(act, nn.Module) else nn.Identity()
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def forward_fuse(self, x):
return self.act(self.conv(x))
# to be implement
# def fuse_conv_bn(self):
# RepVGG
class RepConv(nn.Module):
# https://github.com/DingXiaoH/RepVGG
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, groups=1, act=nn.ReLU()):
super().__init__()
self.conv1 = Conv(in_channels, out_channels, kernel_size, stride, groups=groups, act=False)
self.conv2 = Conv(in_channels, out_channels, 1, stride, groups=groups, act=False)
self.act = act if isinstance(act, nn.Module) else nn.Identity()
def forward(self, x):
return self.act(self.conv1(x) + self.conv2(x))
def forward_fuse(self, x):
return self.act(self.conv(x))
# to be implement
# def fuse_convs(self):
# ResNet
class Res(nn.Module):
# ResNet bottleneck
def __init__(self, in_channels, out_channels,
groups=1, act=nn.ReLU(), ratio=0.25):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = Conv(h_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv3 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv3(self.cv2(self.cv1(x)))
class RepRes(nn.Module):
# RepResNet bottleneck
def __init__(self, in_channels, out_channels,
groups=1, act=nn.ReLU(), ratio=0.25):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = RepConv(h_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv3 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv3(self.cv2(self.cv1(x)))
class ConvBlock(nn.Module):
# ConvBlock
def __init__(self, in_channels,
repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, in_channels, 3, 1, act=act) if repeat == 1 else Conv(in_channels, h_channels, 3, 1, act=act)
self.cb = nn.Sequential(*(Conv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat-2))) if repeat > 2 else nn.Identity()
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return self.cv2(self.cb(self.cv1(x)))
class RepConvBlock(nn.Module):
# ConvBlock
def __init__(self, in_channels,
repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, in_channels, 3, 1, act=act) if repeat == 1 else RepConv(in_channels, h_channels, 3, 1, act=act)
self.cb = nn.Sequential(*(RepConv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat-2))) if repeat > 2 else nn.Identity()
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return self.cv2(self.cb(self.cv1(x)))
class ResConvBlock(nn.Module):
# ResConvBlock
def __init__(self, in_channels,
repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, in_channels, 3, 1, act=act) if repeat == 1 else Conv(in_channels, h_channels, 3, 1, act=act)
self.cb = nn.Sequential(*(Conv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat-2))) if repeat > 2 else nn.Identity()
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cb(self.cv1(x)))
class ResRepConvBlock(nn.Module):
# ResConvBlock
def __init__(self, in_channels,
repeat=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, in_channels, 3, 1, act=act) if repeat == 1 else RepConv(in_channels, h_channels, 3, 1, act=act)
self.cb = nn.Sequential(*(RepConv(in_channels, in_channels, 3, 1, act=act) for _ in range(repeat-2))) if repeat > 2 else nn.Identity()
self.cv2 = nn.Identity() if repeat == 1 else Conv(h_channels, in_channels, 3, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cb(self.cv1(x)))
# Darknet
class Dark(nn.Module):
# DarkNet bottleneck
def __init__(self, in_channels, out_channels,
groups=1, act=nn.ReLU(), ratio=0.5):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = Conv(in_channels, h_channels, 1, 1, act=act)
self.cv2 = Conv(h_channels, out_channels, 3, 1, groups=groups, act=act)
def forward(self, x):
return x + self.cv2(self.cv1(x))
class RepDark(nn.Module):
# RepDarkNet bottleneck
def __init__(self, in_channels, out_channels,
groups=1, act=nn.ReLU(), ratio=0.5):
super().__init__()
h_channels = int(in_channels * ratio)
self.cv1 = RepConv(in_channels, h_channels, 3, 1, groups=groups, act=act)
self.cv2 = Conv(h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
return x + self.cv2(self.cv1(x))
# CSPNet
class CSP(nn.Module):
# CSPNet
def __init__(self, in_channels, out_channels,
repeat=1, cb_repeat=2, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = in_channels // 2
self.cv1 = Conv(in_channels, in_channels, 1, 1, act=act)
self.cb = nn.Sequential(*(ResConvBlock(h_channels, act=act, repeat=cb_repeat) for _ in range(repeat)))
self.cv2 = Conv(2 * h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
return self.cv2(torch.cat((self.cb(y[0]), y[1]), 1))
class CSPDark(nn.Module):
# CSPNet
def __init__(self, in_channels, out_channels,
repeat=1, groups=1, act=nn.ReLU(), ratio=1.0):
super().__init__()
h_channels = in_channels // 2
self.cv1 = Conv(in_channels, in_channels, 1, 1, act=act)
self.cb = nn.Sequential(*(Dark(h_channels, h_channels, groups=groups, act=act, ratio=ratio) for _ in range(repeat)))
self.cv2 = Conv(2 * h_channels, out_channels, 1, 1, act=act)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
return self.cv2(torch.cat((self.cb(y[0]), y[1]), 1))
# ELAN
class ELAN(nn.Module):
# ELAN
def __init__(self, in_channels, out_channels, med_channels,
elan_repeat=2, cb_repeat=2, ratio=1.0):
super().__init__()
h_channels = med_channels // 2
self.cv1 = Conv(in_channels, med_channels, 1, 1)
self.cb = nn.ModuleList(ConvBlock(h_channels, repeat=cb_repeat, ratio=ratio) for _ in range(elan_repeat))
self.cv2 = Conv((2+elan_repeat) * h_channels, out_channels, 1, 1)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
y.extend((m(y[-1])) for m in self.cb)
return self.cv2(torch.cat(y, 1))
class CSPELAN(nn.Module):
# ELAN
def __init__(self, in_channels, out_channels, med_channels,
elan_repeat=2, cb_repeat=2, ratio=1.0):
super().__init__()
h_channels = med_channels // 2
self.cv1 = Conv(in_channels, med_channels, 1, 1)
self.cb = nn.ModuleList(CSP(h_channels, h_channels, repeat=cb_repeat, ratio=ratio) for _ in range(elan_repeat))
self.cv2 = Conv((2+elan_repeat) * h_channels, out_channels, 1, 1)
def forward(self, x):
y = list(self.cv1(x).chunk(2, 1))
y.extend((m(y[-1])) for m in self.cb)
return self.cv2(torch.cat(y, 1))
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