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Inv_model_wLPin.py
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Inv_model_wLPin.py
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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 Inv_modules import InvertibleConv1x1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def upsample(x, h, w):
return F.interpolate(x, size=[h, w], mode='bicubic', align_corners=True)
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 ResBlock(nn.Module):
def __init__(self, channel_in, channel_out):
super(ResBlock, self).__init__()
feature = 64
self.conv1 = nn.Conv2d(channel_in, feature, kernel_size=3, padding=1)
self.relu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv2 = nn.Conv2d(feature, feature, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d((feature+channel_in), channel_out, kernel_size=3, padding=1)
def forward(self, x):
residual = self.relu1(self.conv1(x))
residual = self.relu1(self.conv2(residual))
input = torch.cat((x, residual), dim=1)
out = self.conv3(input)
return out
class HinResBlock(nn.Module):
def __init__(self, channel_in, channel_out):
super(HinResBlock, self).__init__()
feature = 64
self.conv1 = nn.Conv2d(channel_in, feature, kernel_size=3, padding=1)
self.relu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv2 = nn.Conv2d(feature, feature, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d((feature+channel_in), channel_out, kernel_size=3, padding=1)
self.norm = nn.InstanceNorm2d(feature // 2, affine=True)
def forward(self, x):
residual = self.relu1(self.conv1(x))
out_1, out_2 = torch.chunk(residual, 2, dim=1)
residual = torch.cat([self.norm(out_1), out_2], dim=1)
residual = self.relu1(self.conv2(residual))
input = torch.cat((x, residual), dim=1)
out = self.conv3(input)
return out
class INSG(nn.Module):
def __init__(self, channel_in, channel_out):
super(INSG, self).__init__()
feature = 64
self.conv1 = nn.Conv2d(channel_in, feature, kernel_size=3, padding=1)
self.relu1 = nn.LeakyReLU(negative_slope=0.2, inplace=True)
self.conv2 = nn.Conv2d(feature*2, feature, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(feature, channel_out, kernel_size=3, padding=1)
#self.conv3 = nn.Conv2d(feature, channel_out, kernel_size=1)
self.norm = nn.InstanceNorm2d(feature, affine=True)
self.convf = nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1)
def forward(self, x):
residual = self.relu1(self.conv1(x))
out_1 = self.norm(residual)
out_2 = residual - self.norm(residual)
#out_1, out_2 = torch.chunk(residual, 2, dim=1)
residual = torch.cat([out_1, out_2], dim=1)
residual = self.relu1(self.conv2(residual))
out = self.conv3(residual)+self.convf(x)
return out
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)
elif net_structure == 'Resnet':
return ResBlock(channel_in, channel_out)
elif net_structure == 'HinResnet':
return HinResBlock(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(channel_num, 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=4, channel_split_num=4, subnet_constructor=subnet('HinResnet'), block_num=8):
super(InvISPNet, self).__init__()
operations = []
level = 3
channel_num = channel_in + (level +1) #total channels at input stage
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()
self.pyin = Lap_Pyramid_Conv(num_high=level)
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight)
m.weight.data *= 1. # 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.
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, ms, pan, rev=False):
_, _, m, n = ms.shape
_, _, M, N = pan.shape
mHR = upsample(ms, M, N)
x = mHR
out = self.pyin.pyramid_decom(pan)
for i in range(len(out)):
x = torch.cat([x, out[i]], dim=1)
if not rev:
for op in self.operations:
out = op.forward(x, rev)
else:
for op in reversed(self.operations):
out = op.forward(x, rev)
return out, out[:, :4, :, :]
class Lap_Pyramid_Conv(nn.Module):
def __init__(self, num_high=3):
super(Lap_Pyramid_Conv, self).__init__()
self.num_high = num_high
self.kernel = self.gauss_kernel()
def gauss_kernel(self, device=device, channels=3): #cuda
kernel = torch.tensor([[1., 4., 6., 4., 1],
[4., 16., 24., 16., 4.],
[6., 24., 36., 24., 6.],
[4., 16., 24., 16., 4.],
[1., 4., 6., 4., 1.]])
kernel /= 256.
kernel = kernel.repeat(channels, 1, 1, 1)
kernel = kernel.to(device)
return kernel
def downsample(self, x):
return x[:, :, ::2, ::2]
def upsample(self, x):
cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3], device=x.device)], dim=3)
cc = cc.view(x.shape[0], x.shape[1], x.shape[2] * 2, x.shape[3])
cc = cc.permute(0, 1, 3, 2)
cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2] * 2, device=x.device)], dim=3)
cc = cc.view(x.shape[0], x.shape[1], x.shape[3] * 2, x.shape[2] * 2)
x_up = cc.permute(0, 1, 3, 2)
return self.conv_gauss(x_up, 4 * self.kernel)
def conv_gauss(self, img, kernel):
img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode='reflect')
out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1])
return out
def pyramid_decom(self, img):
current = img
pyr = []
for _ in range(self.num_high):
filtered = self.conv_gauss(current, self.kernel)
down = self.downsample(filtered)
up = self.upsample(down)
if up.shape[2] != current.shape[2] or up.shape[3] != current.shape[3]:
up = nn.functional.interpolate(up, size=(current.shape[2], current.shape[3]))
diff = current - up
diff = nn.functional.interpolate(diff, size=(img.shape[2], img.shape[3])) #
pyr.append(diff)
current = down
current = nn.functional.interpolate(current, size=(img.shape[2], img.shape[3]))
pyr.append(current)
return pyr
def pyramid_recons(self, pyr):
image = pyr[-1]
for level in reversed(pyr[:-1]):
up = self.upsample(image)
if up.shape[2] != level.shape[2] or up.shape[3] != level.shape[3]:
up = nn.functional.interpolate(up, size=(level.shape[2], level.shape[3]))
image = up + level
return image
if __name__ == '__main__':
level =3
pyin = Lap_Pyramid_Conv(num_high=level)
net = InvISPNet(channel_in=3, level=level,block_num=8)
print('#generator parameters:',sum(param.numel() for param in net.parameters()))
x = torch.randn(2, 3, 128, 128)
out = pyin.pyramid_decom(x)
for i in range(len(out)):
x =torch.cat([x,out[i]],dim=1)
print(x.size())
out = net(x)
print(out.shape)