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XHChen0528
2022-03-19 22:47:08 +08:00
parent 25a65ff60b
commit 9efbe03d3f
8 changed files with 1503 additions and 137 deletions
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components/Generator_LSTU_config.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Generator_Invobn_config1.py
# Created Date: Saturday February 26th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 27th February 2022 7:50:18 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from LSTU import LSTU
# from components.DeConv_Invo import DeConv
class InstanceNorm(nn.Module):
def __init__(self, epsilon=1e-8):
"""
@notice: avoid in-place ops.
https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
"""
super(InstanceNorm, self).__init__()
self.epsilon = epsilon
def forward(self, x):
x = x - torch.mean(x, (2, 3), True)
tmp = torch.mul(x, x) # or x ** 2
tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
return x * tmp
class ApplyStyle(nn.Module):
"""
@ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
"""
def __init__(self, latent_size, channels):
super(ApplyStyle, self).__init__()
self.linear = nn.Linear(latent_size, channels * 2)
def forward(self, x, latent):
style = self.linear(latent) # style => [batch_size, n_channels*2]
shape = [-1, 2, x.size(1), 1, 1]
style = style.view(shape) # [batch_size, 2, n_channels, ...]
#x = x * (style[:, 0] + 1.) + style[:, 1]
x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
return x
class ResnetBlock_Adain(nn.Module):
def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
super(ResnetBlock_Adain, self).__init__()
p = 0
conv1 = []
if padding_type == 'reflect':
conv1 += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv1 += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
self.conv1 = nn.Sequential(*conv1)
self.style1 = ApplyStyle(latent_size, dim)
self.act1 = activation
p = 0
conv2 = []
if padding_type == 'reflect':
conv2 += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv2 += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
self.conv2 = nn.Sequential(*conv2)
self.style2 = ApplyStyle(latent_size, dim)
def forward(self, x, dlatents_in_slice):
y = self.conv1(x)
y = self.style1(y, dlatents_in_slice)
y = self.act1(y)
y = self.conv2(y)
y = self.style2(y, dlatents_in_slice)
out = x + y
return out
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
id_dim = kwargs["id_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
in_channel = kwargs["in_channel"]
up_mode = kwargs["up_mode"]
aggregator = kwargs["aggregator"]
res_mode = aggregator
padding_size= int((k_size -1)/2)
padding_type= 'reflect'
activation = nn.ReLU(True)
from components.DeConv_Depthwise import DeConv
# self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
# nn.BatchNorm2d(64), activation)
self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
nn.BatchNorm2d(in_channel),
activation)
# self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
# nn.BatchNorm2d(64), activation)
### downsample
self.down1 = nn.Sequential(
nn.Conv2d(in_channel, in_channel, kernel_size=3, padding=1, groups=in_channel),
nn.Conv2d(in_channel, in_channel*2, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*2),
activation)
self.down2 = nn.Sequential(
nn.Conv2d(in_channel*2, in_channel*2, kernel_size=3, padding=1, groups=in_channel*2),
nn.Conv2d(in_channel*2, in_channel*4, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*4),
activation)
self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4)
self.down3 = nn.Sequential(
nn.Conv2d(in_channel*4, in_channel*4, kernel_size=3, padding=1, groups=in_channel*4),
nn.Conv2d(in_channel*4, in_channel*8, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*8),
activation)
self.down4 = nn.Sequential(
nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, padding=1, groups=in_channel*8),
nn.Conv2d(in_channel*8, in_channel*8, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel*8),
activation)
### resnet blocks
BN = []
for i in range(res_num):
BN += [
ResnetBlock_Adain(in_channel*8, latent_size=id_dim,
padding_type=padding_type, activation=activation, res_mode=res_mode)]
self.BottleNeck = nn.Sequential(*BN)
self.up4 = nn.Sequential(
DeConv(in_channel*8,in_channel*8,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*8),
activation
)
self.up3 = nn.Sequential(
DeConv(in_channel*8,in_channel*4,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*4),
activation
)
self.up2 = nn.Sequential(
DeConv(in_channel*4,in_channel*2,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel*2),
activation
)
self.up1 = nn.Sequential(
DeConv(in_channel*2,in_channel,3,up_mode=up_mode),
nn.BatchNorm2d(in_channel),
activation
)
# self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
nn.Conv2d(in_channel, 3, kernel_size=3, padding=0))
# self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
# nn.Conv2d(64, 3, kernel_size=7, padding=0))
# self.__weights_init__()
# def __weights_init__(self):
# for layer in self.encoder:
# if isinstance(layer,nn.Conv2d):
# nn.init.xavier_uniform_(layer.weight)
# for layer in self.encoder2:
# if isinstance(layer,nn.Conv2d):
# nn.init.xavier_uniform_(layer.weight)
def forward(self, img, id):
res = self.first_layer(img)
res = self.down1(res)
res1 = self.down2(res)
res = self.down3(res1)
res = self.down4(res)
for i in range(len(self.BottleNeck)):
res = self.BottleNeck[i](res, id)
res = self.up4(res)
res = self.up3(res)
skip = self.lstu(res1)
res = self.up2(res + skip)
res = self.up1(res)
res = self.last_layer(res)
return res
components/LSTU.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Generator.py
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 13th February 2022 2:03:21 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
class LSTU(nn.Module):
def __init__(
self,
in_channel,
out_channel,
latent_channel,
scale = 4
):
super().__init__()
sig = nn.Sigmoid()
self.relu = nn.Relu()
self.up_sample = nn.Sequential(nn.ConvTranspose2d(latent_channel, out_channel, kernel_size=4, stride=scale, padding=0, bias=False),
nn.BatchNorm2d(out_channel), sig)
self.forget_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channel), sig)
self.reset_gate = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(out_channel), sig)
self.conv11 = nn.Sequential(nn.Conv2d(out_channel, out_channel, kernel_size=1, bias=True))
def forward(self, encoder_in, bottleneck_in):
h_hat_l_1 = self.up_sample(bottleneck_in) # upsample and make `channel` identical to `out_channel`
h_bar_l = self.conv11(h_hat_l_1)
f_l = self.forget_gate(h_hat_l_1)
r_l = self.reset_gate (h_hat_l_1)
h_hat_l = (1-f_l)*h_bar_l + f_l* encoder_in
x_hat_l = r_l* self.relu(h_hat_l) + (1-r_l)* h_hat_l_1
return x_hat_l