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attgan_5_2.py
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attgan_5_2.py
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#coding:utf-8
# Copyright (C) 2018 Elvis Yu-Jing Lin <elvisyjlin@gmail.com>
#
# This work is licensed under the MIT License. To view a copy of this license,
# visit https://opensource.org/licenses/MIT.
"""AttGAN, generator, and discriminator."""
import torch
import torch.nn as nn
from nn import LinearBlock, Conv2dBlock, ConvTranspose2dBlock
from torchsummary import summary
# This architecture is for images of 128x128
# In the original AttGAN, slim.conv2d uses padding 'same'
MAX_DIM = 64 * 16 # 1024
class Generator(nn.Module):
def __init__(self, enc_dim=64, enc_layers=5, enc_norm_fn='batchnorm', enc_acti_fn='lrelu',
dec_dim=64, dec_layers=5, dec_norm_fn='batchnorm', dec_acti_fn='relu',
n_attrs=13, shortcut_layers=0, inject_layers=0, img_size=128):
super(Generator, self).__init__()
self.shortcut_layers = min(shortcut_layers, dec_layers - 1)
self.inject_layers = min(inject_layers, dec_layers - 1)
self.f_size = img_size // 2**enc_layers # f_size = 4 for 128x128
layers = []
n_in = 3
# start
layers += [Conv2dBlock(3, 64, (4, 4), stride=2, padding=1, norm_fn=enc_norm_fn, acti_fn=enc_acti_fn)] # 0
layers += [Self_Attn(64, 'relu')] # 1
layers += [Conv2dBlock(64, 128, (4, 4), stride=2, padding=1, norm_fn=enc_norm_fn, acti_fn=enc_acti_fn)] # 2
layers += [Self_Attn(128, 'relu')] # 3
layers += [Conv2dBlock(128, 256, (4, 4), stride=2, padding=1, norm_fn=enc_norm_fn, acti_fn=enc_acti_fn)] # 4
layers += [Conv2dBlock(256, 512, (4, 4), stride=2, padding=1, norm_fn=enc_norm_fn, acti_fn=enc_acti_fn)] # 5
layers += [Conv2dBlock(512, 1024, (4, 4), stride=2, padding=1, norm_fn=enc_norm_fn, acti_fn=enc_acti_fn)] # 6
self.enc_layers = nn.ModuleList(layers)
# end
layers = []
n_in = n_in + n_attrs # 1024 + 13
k,j=0,0
if self.inject_layers == 1:
k=13
elif self.inject_layers == 2:
k=13
j=13
else:
k=0
# start
# 解码器中的 conv_block 和 ConvTranspose2dBlock 都使用了BN和relu虽然是不同人实现的,不过方法是一致的
layers += [ConvTranspose2dBlock(1037, 512, (4, 4), stride=2, padding=1, norm_fn=dec_norm_fn, acti_fn=dec_acti_fn)] # 0
layers += [Attention_block(F_g=512,F_l=512,F_int=256)] # 1
layers += [conv_block(ch_in=1024,ch_out=512)] # 2
layers += [ConvTranspose2dBlock(512+k, 256, (4, 4), stride=2, padding=1, norm_fn=dec_norm_fn, acti_fn=dec_acti_fn)] # 3
layers += [Attention_block(F_g=256,F_l=256,F_int=128)] # 4
layers += [conv_block(ch_in=512,ch_out=256)] # 5
layers += [ConvTranspose2dBlock(256+j, 128, (4, 4), stride=2, padding=1, norm_fn=dec_norm_fn, acti_fn=dec_acti_fn)] # 6
layers += [Self_Attn(128, 'relu')] # 7
layers += [ConvTranspose2dBlock(128, 64, (4, 4), stride=2, padding=1, norm_fn=dec_norm_fn, acti_fn=dec_acti_fn)] # 8
layers += [Self_Attn(64, 'relu')] # 9
layers += [ConvTranspose2dBlock(64, 3, (4, 4), stride=2, padding=1, norm_fn='none', acti_fn='tanh')] # 10
self.dec_layers = nn.ModuleList(layers)
# end
def encode(self, x):
z = x
zs = []
for layer in self.enc_layers: #5
z = layer(z)
zs.append(z)
return zs
def decode(self, zs, a):
a_tile = a.view(a.size(0), -1, 1, 1).repeat(1, 1, self.f_size, self.f_size) # 将特征向量延展为 4*4*13
z = torch.cat([zs[-1], a_tile], dim=1) # 将特征向量concat进编码器的输出,作为解码器的输入 z 8*8*1037
# start
z = self.dec_layers[0](z) # 0
x_d = z # 1
x_f = zs[5] # 编解码器的对应层数关系
decode_l = self.dec_layers[1]
att_out = decode_l(g=x_d ,x=x_f)
z = torch.cat((att_out,x_d),dim=1)
z = self.dec_layers[2](z) # 2
if self.inject_layers >= 1: # 判断是否有嵌入特征
a_tile = a.view(a.size(0), -1, 1, 1).repeat(1, 1, 8, 8)
z = torch.cat([z, a_tile], dim=1)
z = self.dec_layers[3](z) # 3
x_d = z # 4
x_f = zs[4] # 编解码器的对应层数关系
decode_l = self.dec_layers[4]
att_out = decode_l(g=x_d ,x=x_f)
z = torch.cat((att_out,x_d),dim=1)
z = self.dec_layers[5](z) # 5
if self.inject_layers >= 2: # 判断是否有嵌入特征
a_tile = a.view(a.size(0), -1, 1, 1).repeat(1, 1, 16, 16)
z = torch.cat([z, a_tile], dim=1)
z = self.dec_layers[6](z) # 6
z = self.dec_layers[7](z) # 7
z = self.dec_layers[8](z) # 8
z = self.dec_layers[9](z) # 9
z = self.dec_layers[10](z) # 10
# end
return z
def forward(self, x, a=None, mode='enc-dec'):
if mode == 'enc-dec':
assert a is not None, 'No given attribute.'
return self.decode(self.encode(x), a)
if mode == 'enc':
return self.encode(x)
if mode == 'dec':
assert a is not None, 'No given attribute.'
return self.decode(x, a)
raise Exception('Unrecognized mode: ' + mode)
class Discriminators(nn.Module):
# No instancenorm in fcs in source code, which is different from paper.
def __init__(self, dim=64, norm_fn='instancenorm', acti_fn='lrelu',
fc_dim=1024, fc_norm_fn='none', fc_acti_fn='lrelu', n_layers=5, img_size=128):
super(Discriminators, self).__init__()
self.f_size = img_size // 2**n_layers
layers = []
n_in = 3
for i in range(n_layers):
n_out = min(dim * 2**i, MAX_DIM)
layers += [Conv2dBlock(
n_in, n_out, (4, 4), stride=2, padding=1, norm_fn=norm_fn, acti_fn=acti_fn
)]
n_in = n_out
self.conv = nn.Sequential(*layers)
self.fc_adv = nn.Sequential(
LinearBlock(1024 * self.f_size * self.f_size, fc_dim, fc_norm_fn, fc_acti_fn),
LinearBlock(fc_dim, 1, 'none', 'none')
)
self.fc_cls = nn.Sequential(
LinearBlock(1024 * self.f_size * self.f_size, fc_dim, fc_norm_fn, fc_acti_fn),
LinearBlock(fc_dim, 13, 'none', 'none')
)
def forward(self, x):
h = self.conv(x)
h = h.view(h.size(0), -1)
return self.fc_adv(h), self.fc_cls(h)
# 返回值 1.真伪标签(1*1) 2.各属性概率标签(1*13)
import torch.autograd as autograd
import torch.nn.functional as F
import torch.optim as optim
# multilabel_soft_margin_loss = sigmoid + binary_cross_entropy
class AttGAN():
def __init__(self, args):
self.mode = args.mode
self.gpu = args.gpu
self.multi_gpu = args.multi_gpu if 'multi_gpu' in args else False
self.lambda_1 = args.lambda_1
self.lambda_2 = args.lambda_2
self.lambda_3 = args.lambda_3
self.lambda_gp = args.lambda_gp
self.G = Generator(
args.enc_dim, args.enc_layers, args.enc_norm, args.enc_acti,
args.dec_dim, args.dec_layers, args.dec_norm, args.dec_acti,
args.n_attrs, args.shortcut_layers, args.inject_layers, args.img_size
)
self.G.train() # modle父类自带的方法,用于做min_batch的结算
if self.gpu: self.G.cuda()
# summary(self.G, [(3, args.img_size, args.img_size), (args.n_attrs, 1, 1)], batch_size=4, device='cuda' if args.gpu else 'cpu')
self.D = Discriminators(
args.dis_dim, args.dis_norm, args.dis_acti,
args.dis_fc_dim, args.dis_fc_norm, args.dis_fc_acti, args.dis_layers, args.img_size
)
self.D.train()
if self.gpu: self.D.cuda()
# summary(self.D, [(3, args.img_size, args.img_size)], batch_size=4, device='cuda' if args.gpu else 'cpu')
if self.multi_gpu: # 并行计算
self.G = nn.DataParallel(self.G)
self.D = nn.DataParallel(self.D)
self.optim_G = optim.Adam(self.G.parameters(), lr=args.lr, betas=args.betas)
self.optim_D = optim.Adam(self.D.parameters(), lr=args.lr, betas=args.betas)
def set_lr(self, lr):
for g in self.optim_G.param_groups:
g['lr'] = lr
for g in self.optim_D.param_groups:
g['lr'] = lr
def trainG(self, img_a, att_a, att_a_, att_b, att_b_):
for p in self.D.parameters():
p.requires_grad = False
zs_a = self.G(img_a, mode='enc') # 输入真实图片
img_fake = self.G(zs_a, att_b_, mode='dec') # 真实图片高级特征 + 目标属性
img_recon = self.G(zs_a, att_a_, mode='dec') # 真实图片高级特征 + 原属性
d_fake, dc_fake = self.D(img_fake) # 输出生成图片的 真伪/类别
if self.mode == 'wgan':
gf_loss = -d_fake.mean() # 1.对抗损失 生成样本的损失
if self.mode == 'lsgan': # mean_squared_error
gf_loss = F.mse_loss(d_fake, torch.ones_like(d_fake))
if self.mode == 'dcgan': # sigmoid_cross_entropy
gf_loss = F.binary_cross_entropy_with_logits(d_fake, torch.ones_like(d_fake))
gc_loss = F.binary_cross_entropy_with_logits(dc_fake, att_b) # 2.分类损失 生产图片经过判别网络D 产生的标签的二元交叉熵损失
gr_loss = F.l1_loss(img_recon, img_a) # 3.图像重建损失
g_loss = gf_loss + self.lambda_2 * gc_loss + self.lambda_1 * gr_loss
self.optim_G.zero_grad()
g_loss.backward()
self.optim_G.step()
errG = {
'g_loss': g_loss.item(), 'gf_loss': gf_loss.item(),
'gc_loss': gc_loss.item(), 'gr_loss': gr_loss.item()
}
return errG
def trainD(self, img_a, att_a, att_a_, att_b, att_b_):
for p in self.D.parameters():
p.requires_grad = True
img_fake = self.G(img_a, att_b_).detach() # 生成图像
d_real, dc_real = self.D(img_a) # 真实图像经过鉴别器得到的标签
d_fake, dc_fake = self.D(img_fake) # 生成图像经过鉴别器得到的标签
def gradient_penalty(f, real, fake=None):
def interpolate(a, b=None):
if b is None: # interpolation in DRAGAN
beta = torch.rand_like(a)
b = a + 0.5 * a.var().sqrt() * beta
alpha = torch.rand(a.size(0), 1, 1, 1)
alpha = alpha.cuda() if self.gpu else alpha
inter = a + alpha * (b - a)
return inter
x = interpolate(real, fake).requires_grad_(True)
pred = f(x)
if isinstance(pred, tuple):
pred = pred[0]
grad = autograd.grad(
outputs=pred, inputs=x,
grad_outputs=torch.ones_like(pred),
create_graph=True, retain_graph=True, only_inputs=True
)[0]
grad = grad.view(grad.size(0), -1)
norm = grad.norm(2, dim=1)
gp = ((norm - 1.0) ** 2).mean()
return gp
if self.mode == 'wgan':
wd = d_real.mean() - d_fake.mean()
df_loss = -wd
df_gp = gradient_penalty(self.D, img_a, img_fake)
if self.mode == 'lsgan': # mean_squared_error
df_loss = F.mse_loss(d_real, torch.ones_like(d_fake)) + \
F.mse_loss(d_fake, torch.zeros_like(d_fake))
df_gp = gradient_penalty(self.D, img_a)
if self.mode == 'dcgan': # sigmoid_cross_entropy
df_loss = F.binary_cross_entropy_with_logits(d_real, torch.ones_like(d_real)) + \
F.binary_cross_entropy_with_logits(d_fake, torch.zeros_like(d_fake))
df_gp = gradient_penalty(self.D, img_a) # 梯度惩罚机制
dc_loss = F.binary_cross_entropy_with_logits(dc_real, att_a)
d_loss = df_loss + self.lambda_gp * df_gp + self.lambda_3 * dc_loss
self.optim_D.zero_grad()
d_loss.backward()
self.optim_D.step()
errD = {
'd_loss': d_loss.item(), 'df_loss': df_loss.item(),
'df_gp': df_gp.item(), 'dc_loss': dc_loss.item()
}
return errD
def train(self):
self.G.train()
self.D.train()
def eval(self):
self.G.eval()
self.D.eval()
def save(self, path):
states = {
'G': self.G.state_dict(),
'D': self.D.state_dict(),
'optim_G': self.optim_G.state_dict(),
'optim_D': self.optim_D.state_dict()
}
torch.save(states, path)
def load(self, path):
states = torch.load(path, map_location=lambda storage, loc: storage)
if 'G' in states:
self.G.load_state_dict(states['G'])
if 'D' in states:
self.D.load_state_dict(states['D'])
if 'optim_G' in states:
self.optim_G.load_state_dict(states['optim_G'])
if 'optim_D' in states:
self.optim_D.load_state_dict(states['optim_D'])
def saveG(self, path):
states = {
'G': self.G.state_dict()
}
torch.save(states, path)
# 2层不改变特征图大小的卷积 加上BN ReLU
class conv_block(nn.Module):
def __init__(self,ch_in,ch_out):
super(conv_block,self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.conv(x)
return x
# 直接上采样为原来的两倍大小
class up_conv(nn.Module):
def __init__(self,ch_in,ch_out):
super(up_conv,self).__init__()
self.up = nn.Sequential(
nn.Upsample(scale_factor=2), # 固定模式上采样
nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.up(x)
return x
# ATT_U-Net 的 Attention模块
class Attention_block(nn.Module):
# 128*128*512
# F_g,F_l 尺寸相等 都比输出大一圈, F_int通道是他们的一半(512, 512, 256)
def __init__(self,F_g,F_l,F_int): # 通道 F_g:大尺寸输入 F_l:前级输入 F_int:他们通道的一半
super(Attention_block,self).__init__()
self.W_g = nn.Sequential( # 步长为1的1*1卷积 BN
nn.Conv2d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(F_int)
) # 输出:Hg*Wg*F_int
self.W_x = nn.Sequential( # 步长为1的1*1卷积 BN
nn.Conv2d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(F_int)
) # 输出:Hg*Wg*F_int
self.psi = nn.Sequential( # 步长为1的1*1卷积 BN
nn.Conv2d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(1),
nn.Sigmoid()
)
self.relu = nn.ReLU(inplace=True)
def forward(self,g,x):
# g,x 128*128*512
g1 = self.W_g(g) # g支路输出 128*128*256
x1 = self.W_x(x) # Xl支路输出 128*128*256
psi = self.relu(g1+x1) # 2路信息相加 128*128*256
psi = self.psi(psi) # output 128*128*1
return x*psi # 与特征图相乘 128*128*512
# self-attention模块(使用标准的卷积,而不是谱归一化)
class Self_Attn(nn.Module):
""" Self attention Layer"""
def __init__(self,in_dim,activation): # 构造函数
super(Self_Attn,self).__init__()
self.chanel_in = in_dim # 输入通道数
self.activation = activation # 父类里的属性,激活函数???
self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) # Q通道输出的通道数为原来的8分之一
self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) # K通道输出的通道数为原来的8分之一
self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1) # V通道输出的通道数不变
self.gamma = nn.Parameter(torch.zeros(1)) # att图的权值参数
self.softmax = nn.Softmax(dim=-1) # softmax后形成att_map
def forward(self,x):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize,C,width ,height = x.size() # X : B*C*W*H 获取B,C,W,H的维度信息
proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B x (C/8) x (W*H) 将Q卷积后的特征拉长为2维的,B*C*(H*W),后经过转置 B*(H*W)*C
proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B x (C/8) x (W*H) 将K卷积后的特征拉长为2维的,B*C*(H*W)
energy = torch.bmm(proj_query,proj_key) # transpose check B*(H*W)*(H*W)
attention = self.softmax(energy) # B x (N) x (N)
proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B x C x N 将V卷积后的特征拉长为2维的,B*C*(H*W)
out = torch.bmm(proj_value,attention.permute(0,2,1) ) # 将V与attention_map相乘 (B*C*N)(B*N*N) = B*C*N
out = out.view(m_batchsize,C,width,height) # 再还原成原图像 B*C*H*W
out = self.gamma*out + x # 计算残差 权重参数为 self.gamma 如果残差为0那就是恒等映射
return out #attention # 返回 1.attention残差结果 2.N*N的注意力图(有什么意义)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--img_size', dest='img_size', type=int, default=128)
parser.add_argument('--shortcut_layers', dest='shortcut_layers', type=int, default=1)
parser.add_argument('--inject_layers', dest='inject_layers', type=int, default=0)
parser.add_argument('--enc_dim', dest='enc_dim', type=int, default=64)
parser.add_argument('--dec_dim', dest='dec_dim', type=int, default=64)
parser.add_argument('--dis_dim', dest='dis_dim', type=int, default=64)
parser.add_argument('--dis_fc_dim', dest='dis_fc_dim', type=int, default=1024)
parser.add_argument('--enc_layers', dest='enc_layers', type=int, default=5)
parser.add_argument('--dec_layers', dest='dec_layers', type=int, default=5)
parser.add_argument('--dis_layers', dest='dis_layers', type=int, default=5)
parser.add_argument('--enc_norm', dest='enc_norm', type=str, default='batchnorm')
parser.add_argument('--dec_norm', dest='dec_norm', type=str, default='batchnorm')
parser.add_argument('--dis_norm', dest='dis_norm', type=str, default='instancenorm')
parser.add_argument('--dis_fc_norm', dest='dis_fc_norm', type=str, default='none')
parser.add_argument('--enc_acti', dest='enc_acti', type=str, default='lrelu')
parser.add_argument('--dec_acti', dest='dec_acti', type=str, default='relu')
parser.add_argument('--dis_acti', dest='dis_acti', type=str, default='lrelu')
parser.add_argument('--dis_fc_acti', dest='dis_fc_acti', type=str, default='relu')
parser.add_argument('--lambda_1', dest='lambda_1', type=float, default=100.0)
parser.add_argument('--lambda_2', dest='lambda_2', type=float, default=10.0)
parser.add_argument('--lambda_3', dest='lambda_3', type=float, default=1.0)
parser.add_argument('--lambda_gp', dest='lambda_gp', type=float, default=10.0)
parser.add_argument('--mode', dest='mode', default='wgan', choices=['wgan', 'lsgan', 'dcgan'])
parser.add_argument('--lr', dest='lr', type=float, default=0.0002, help='learning rate')
parser.add_argument('--beta1', dest='beta1', type=float, default=0.5)
parser.add_argument('--beta2', dest='beta2', type=float, default=0.999)
parser.add_argument('--gpu', action='store_true')
args = parser.parse_args()
args.n_attrs = 13
args.betas = (args.beta1, args.beta2)
attgan = AttGAN(args)