File size: 6,725 Bytes
404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee 404d2af 8b973ee |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torch.nn.init as init
from .modules import InvertibleConv1x1
def initialize_weights(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode="fan_in")
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.kaiming_normal_(m.weight, a=0, mode="fan_in")
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def initialize_weights_xavier(net_l, scale=1):
if not isinstance(net_l, list):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight)
m.weight.data *= scale # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.xavier_normal_(m.weight)
m.weight.data *= scale
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
class DenseBlock(nn.Module):
def __init__(self, channel_in, channel_out, init="xavier", gc=32, bias=True):
super(DenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias)
self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias)
self.conv5 = nn.Conv2d(channel_in + 4 * gc, channel_out, 3, 1, 1, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
if init == "xavier":
initialize_weights_xavier(
[self.conv1, self.conv2, self.conv3, self.conv4], 0.1
)
else:
initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4], 0.1)
initialize_weights(self.conv5, 0)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5
def subnet(net_structure, init="xavier"):
def constructor(channel_in, channel_out):
if net_structure == "DBNet":
if init == "xavier":
return DenseBlock(channel_in, channel_out, init)
else:
return DenseBlock(channel_in, channel_out)
# return UNetBlock(channel_in, channel_out)
else:
return None
return constructor
class InvBlock(nn.Module):
def __init__(self, subnet_constructor, channel_num, channel_split_num, clamp=0.8):
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)
in_channels = 3
self.invconv = InvertibleConv1x1(in_channels, LU_decomposed=True)
self.flow_permutation = lambda z, logdet, rev: self.invconv(z, logdet, rev)
def forward(self, x, rev=False):
if not rev:
# invert1x1conv
x, logdet = self.flow_permutation(x, logdet=0, rev=False)
# split to 1 channel and 2 channel.
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)
else:
# split.
x1, x2 = (
x.narrow(1, 0, self.split_len1),
x.narrow(1, self.split_len1, self.split_len2),
)
self.s = self.clamp * (torch.sigmoid(self.H(x1)) * 2 - 1)
y2 = (x2 - self.G(x1)).div(torch.exp(self.s))
y1 = x1 - self.F(y2)
x = torch.cat((y1, y2), 1)
# inv permutation
out, logdet = self.flow_permutation(x, logdet=0, rev=True)
return out
class InvISPNet(nn.Module):
def __init__(
self,
channel_in=3,
channel_out=3,
subnet_constructor=subnet("DBNet"),
block_num=8,
):
super(InvISPNet, self).__init__()
operations = []
current_channel = channel_in
channel_num = channel_in
channel_split_num = 1
for j in range(block_num):
b = InvBlock(
subnet_constructor, channel_num, channel_split_num
) # one block is one flow step.
operations.append(b)
self.operations = nn.ModuleList(operations)
self.initialize()
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight)
m.weight.data *= 1.0 # for residual block
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
init.xavier_normal_(m.weight)
m.weight.data *= 1.0
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias.data, 0.0)
def forward(self, x, rev=False):
out = x # x: [N,3,H,W]
if not rev:
for op in self.operations:
out = op.forward(out, rev)
else:
for op in reversed(self.operations):
out = op.forward(out, rev)
return out
|