File size: 8,416 Bytes
98feea6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
try:
from .module import *
except:
from module import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
class SuperUnet_MS(nn.Module):
def __init__(self, channels, block="INV"):
super(SuperUnet_MS, self).__init__()
# ---------ENCODE
self.layer_dowm1 = basic_block(channels, channels, block)
self.dowm1 = nn.Sequential(nn.Conv2d(channels, channels * 2, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels * 2, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.layer_dowm2 = basic_block(channels * 2, channels * 2, block)
self.dowm2 = nn.Sequential(nn.Conv2d(channels * 2, channels * 4, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels * 4, affine=True), nn.LeakyReLU(0.2, inplace=True))
# ---------DECODE
self.layer_bottom = basic_block(channels * 4, channels * 4, block)
self.up2 = nn.Sequential(nn.ConvTranspose2d(channels * 4, channels * 2, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels * 2, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.layer_up2 = basic_block(channels * 2, channels * 2, block)
self.up1 = nn.Sequential(nn.ConvTranspose2d(channels * 2, channels, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.layer_up1 = basic_block(channels, channels, block)
# ---------SKIP
self.fus2 = skip(channels * 4, channels * 2, "HIN")
self.fus1 = skip(channels * 2, channels, "HIN")
# ---------SKIP
self.skip_down1 = nn.Sequential(nn.Conv2d(channels, channels, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.skip1 = skip(channels * 3, channels * 2, "CONV")
self.skip_down2 = nn.Sequential(nn.Conv2d(channels * 2, channels, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.skip2 = skip(channels * 5, channels * 4, "CONV")
# self.skip3 = skip(channels*2, channels, "CONV")
self.skip_up4 = nn.Sequential(nn.ConvTranspose2d(channels * 4, channels, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.skip4 = skip(channels * 3, channels * 2, "CONV")
# self.skip5 = skip(channels*2, channels, "CONV")
self.skip_up6 = nn.Sequential(nn.ConvTranspose2d(channels * 2, channels, 4, 2, 1, bias=True),
nn.InstanceNorm2d(channels, affine=True), nn.LeakyReLU(0.2, inplace=True))
self.skip6 = skip(channels * 2, channels, "CONV")
def forward(self, x):
# ---------ENCODE
x_11 = self.layer_dowm1(x)
x_down1 = self.dowm1(x_11)
# x =self.skip_down1(x)
# print(x.shape, x_down1.shape)
x_down1 = self.skip1(torch.cat([self.skip_down1(x), x_down1], 1), x_down1)
x_12 = self.layer_dowm2(x_down1)
x_down2 = self.dowm2(x_12)
x_down2 = self.skip2(torch.cat([self.skip_down2(x_down1), x_down2], 1), x_down2)
x_bottom = self.layer_bottom(x_down2)
# ---------DECODE
x_up2 = self.up2(x_bottom)
x_22 = self.layer_up2(x_up2)
x_22 = self.skip4(torch.cat([self.skip_up4(x_bottom), x_22], 1), x_22)
x_22 = self.fus2(torch.cat([x_12, x_22], 1), x_22)
x_up1 = self.up1(x_22)
x_21 = self.layer_up1(x_up1)
x_21 = self.skip6(torch.cat([self.skip_up6(x_22), x_21], 1), x_21)
x_21 = self.fus1(torch.cat([x_11, x_21], 1), x_21)
return x_21, x_down2
class skip(nn.Module):
def __init__(self, channels_in, channels_out, block):
super(skip, self).__init__()
if block == "CONV":
self.body = nn.Sequential(nn.Conv2d(channels_in, channels_out, 1, 1, 0, bias=True),
nn.InstanceNorm2d(channels_out, affine=True), nn.ReLU(inplace=True), )
if block == "ID":
self.body = nn.Identity()
if block == "INV":
self.body = nn.Sequential(InvBlock(channels_in, channels_in // 2),
nn.Conv2d(channels_in, channels_out, 1, 1, 0, bias=True), )
if block == "HIN":
self.body = nn.Sequential(HinBlock(channels_in, channels_out))
# --------------------------------------
self.alpha1 = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha1.data.fill_(1.0)
self.alpha2 = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha2.data.fill_(0.5)
def forward(self, x, y):
out = self.alpha1 * self.body(x) + self.alpha2 * y
return out
def subnet(net_structure, init='xavier'):
def constructor(channel_in, channel_out):
if net_structure == 'HIN':
return HinBlock(channel_in, channel_out)
return constructor
class InvBlock(nn.Module):
def __init__(self, channel_num, channel_split_num, subnet_constructor=subnet('HIN'),
clamp=0.8): ################ split_channel一般设为channel_num的一半
super(InvBlock, self).__init__()
# channel_num: 3
# channel_split_num: 1
self.split_len1 = channel_split_num # 1
self.split_len2 = channel_num - channel_split_num # 2
self.clamp = clamp
self.F = subnet_constructor(self.split_len2, self.split_len1)
self.G = subnet_constructor(self.split_len1, self.split_len2)
self.H = subnet_constructor(self.split_len1, self.split_len2)
def forward(self, x):
x1, x2 = (x.narrow(1, 0, self.split_len1), x.narrow(1, self.split_len1, self.split_len2))
y1 = x1 + self.F(x2) # 1 channel
self.s = self.clamp * (torch.sigmoid(self.H(y1)) * 2 - 1)
y2 = x2.mul(torch.exp(self.s)) + self.G(y1) # 2 channel
out = torch.cat((y1, y2), 1)
return out + x
class sample_block(nn.Module):
def __init__(self, channels_in, channels_out, size, dil):
super(sample_block, self).__init__()
# ------------------------------------------
if size == "DOWN":
self.conv = nn.Sequential(
nn.Conv2d(channels_in, channels_out, 3, 1, dil, dilation=dil),
nn.InstanceNorm2d(channels_out, affine=True),
nn.ReLU(inplace=True),
)
if size == "UP":
self.conv = nn.Sequential(
nn.ConvTranspose2d(channels_in, channels_out, 3, 1, dil, dilation=dil),
nn.InstanceNorm2d(channels_out, affine=True),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class HinBlock(nn.Module):
def __init__(self, in_size, out_size):
super(HinBlock, self).__init__()
self.identity = nn.Conv2d(in_size, out_size, 1, 1, 0)
self.norm = nn.InstanceNorm2d(out_size // 2, affine=True)
self.conv_1 = nn.Conv2d(in_size, out_size, kernel_size=3, stride=1, padding=1, bias=True)
self.relu_1 = nn.Sequential(nn.LeakyReLU(0.2, inplace=False), )
self.conv_2 = nn.Sequential(nn.Conv2d(out_size, out_size, kernel_size=3, stride=1, padding=1, bias=True),
nn.LeakyReLU(0.2, inplace=False), )
def forward(self, x):
out = self.conv_1(x)
out_1, out_2 = torch.chunk(out, 2, dim=1)
out = torch.cat([self.norm(out_1), out_2], dim=1)
out = self.relu_1(out)
out = self.conv_2(out)
out += self.identity(x)
return out
class net(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args.model
self.hr_inc = DoubleConv(self.args["in_channel"], self.args["model_channel"] * 2)
self.hr_backbone = SuperUnet_MS(self.args["model_channel"] * 2)
self.final_out = nn.Conv2d(self.args["model_channel"] * 2, 3, kernel_size=1, bias=False)
def forward(self, x):
x = self.hr_inc(x)
x, mid_feat = self.hr_backbone(x)
out = self.final_out(x)
return out
|