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validator_models.py
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# simple WGAN-GP validator model for synthesis MNIST images
# ref: [https://github.com/caogang/wgan-gp/blob/master/gan_mnist.py]
import torch.nn as nn
import pdb
DIM = 64 # Model dimensionality
OUTPUT_DIM = 784 # Number of pixels in MNIST (28*28)
# ==================Definition Start======================
class MNIST_Generator(nn.Module):
def __init__(self):
super(MNIST_Generator, self).__init__()
preprocess = nn.Sequential(
nn.Linear(128, 4*4*4*DIM),
nn.ReLU(True),
)
block1 = nn.Sequential(
nn.ConvTranspose2d(4*DIM, 2*DIM, 5),
nn.ReLU(True),
)
block2 = nn.Sequential(
nn.ConvTranspose2d(2*DIM, DIM, 5),
nn.ReLU(True),
)
deconv_out = nn.ConvTranspose2d(DIM, 1, 8, stride=2)
self.block1 = block1
self.block2 = block2
self.deconv_out = deconv_out
self.preprocess = preprocess
self.sigmoid = nn.Sigmoid()
def forward(self, input):
output = self.preprocess(input)
output = output.view(-1, 4*DIM, 4, 4)
#print output.size()
# pdb.set_trace()
# torch.Size([50, 256, 4, 4])
output = self.block1(output)
# pdb.set_trace()
# torch.Size([50, 128, 8, 8])
#print output.size()
output = output[:, :, :7, :7]
# torch.Size([50, 128, 7, 7])
# pdb.set_trace()
#print output.size()
output = self.block2(output)
# torch.Size([50, 64, 11, 11])
# pdb.set_trace()
#print output.size()
output = self.deconv_out(output)
# torch.Size([50, 1, 28, 28])
# pdb.set_trace()
output = self.sigmoid(output)
#print output.size()
# return output.view(-1, OUTPUT_DIM)
return output.view(-1, 1, 28, 28)
class MNIST_Discriminator(nn.Module):
def __init__(self):
super(MNIST_Discriminator, self).__init__()
main = nn.Sequential(
nn.Conv2d(1, DIM, 5, stride=2, padding=2),
# nn.Linear(OUTPUT_DIM, 4*4*4*DIM),
nn.ReLU(True),
nn.Conv2d(DIM, 2*DIM, 5, stride=2, padding=2),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.ReLU(True),
nn.Conv2d(2*DIM, 4*DIM, 5, stride=2, padding=2),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.ReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
)
self.main = main
self.output = nn.Linear(4*4*4*DIM, 1)
def forward(self, input):
input = input.view(-1, 1, 28, 28)
out = self.main(input)
out = out.view(-1, 4*4*4*DIM)
out = self.output(out)
return out.view(-1)
# Cifar10
# ref: [https://github.com/caogang/wgan-gp/blob/master/gan_cifar10.py]
class CIFAR10_Generator(nn.Module):
def __init__(self):
super(CIFAR10_Generator, self).__init__()
preprocess = nn.Sequential(
nn.Linear(128, 4 * 4 * 4 * DIM),
nn.BatchNorm1d(4 * 4 * 4 * DIM),
nn.ReLU(True),
)
block1 = nn.Sequential(
nn.ConvTranspose2d(4 * DIM, 2 * DIM, 2, stride=2),
nn.BatchNorm2d(2 * DIM),
nn.ReLU(True),
)
block2 = nn.Sequential(
nn.ConvTranspose2d(2 * DIM, DIM, 2, stride=2),
nn.BatchNorm2d(DIM),
nn.ReLU(True),
)
deconv_out = nn.ConvTranspose2d(DIM, 3, 2, stride=2)
self.preprocess = preprocess
self.block1 = block1
self.block2 = block2
self.deconv_out = deconv_out
self.tanh = nn.Tanh()
def forward(self, input):
output = self.preprocess(input)
output = output.view(-1, 4 * DIM, 4, 4)
output = self.block1(output)
output = self.block2(output)
output = self.deconv_out(output)
output = self.tanh(output)
return output.view(-1, 3, 32, 32)
class CIFAR10_Discriminator(nn.Module):
def __init__(self):
super(CIFAR10_Discriminator, self).__init__()
main = nn.Sequential(
nn.Conv2d(3, DIM, 3, 2, padding=1),
nn.LeakyReLU(),
nn.Conv2d(DIM, 2 * DIM, 3, 2, padding=1),
nn.LeakyReLU(),
nn.Conv2d(2 * DIM, 4 * DIM, 3, 2, padding=1),
nn.LeakyReLU(),
)
self.main = main
self.linear = nn.Linear(4*4*4*DIM, 1)
def forward(self, input):
output = self.main(input)
output = output.view(-1, 4*4*4*DIM)
output = self.linear(output)
return output
# Anime Avatar
# ref: [https://github.com/chenyuntc/pytorch-book/blob/master/chapter7-GAN%E7%94%9F%E6%88%90%E5%8A%A8%E6%BC%AB%E5%A4%B4%E5%83%8F/model.py]
from custom_utils import dotdict
opt = {
'nz': 128,
'ngf': 64,
'ndf': 64,
}
opt = dotdict(opt)
class ANIME_Generator(nn.Module):
"""
生成器定义
"""
def __init__(self):
super(ANIME_Generator, self).__init__()
ngf = opt.ngf # 生成器feature map数
self.main = nn.Sequential(
# 输入是一个nz维度的噪声，我们可以认为它是一个1*1*nz的feature map
nn.ConvTranspose2d(opt.nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# 上一步的输出形状：(ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# 上一步的输出形状： (ngf*4) x 8 x 8
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# 上一步的输出形状： (ngf*2) x 16 x 16
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# 上一步的输出形状：(ngf) x 32 x 32
nn.ConvTranspose2d(ngf, 3, 5, 3, 1, bias=False),
nn.Tanh() # 输出范围 -1~1 故而采用Tanh
# 输出形状：3 x 96 x 96
)
def forward(self, input):
input = input.view(-1, opt.nz, 1, 1)
return self.main(input)
class ANIME_Discriminator(nn.Module):
"""
判别器定义
"""
def __init__(self):
super(ANIME_Discriminator, self).__init__()
ndf = opt.ndf
self.main = nn.Sequential(
# 输入 3 x 96 x 96
nn.Conv2d(3, ndf, 5, 3, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# 输出 (ndf) x 32 x 32
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# 输出 (ndf*2) x 16 x 16
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# 输出 (ndf*4) x 8 x 8
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# 输出 (ndf*8) x 4 x 4
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
# nn.Sigmoid() # 输出一个数(概率)
)
def forward(self, input):
return self.main(input).view(-1)
# class ANIME_Generator_v2(nn.Module):
# """
# 生成器定义
# """
# def __init__(self):
# super(ANIME_Generator, self).__init__()
# ngf = opt.ngf # 生成器feature map数
# self.main = nn.Sequential(
# # 输入是一个nz维度的噪声，我们可以认为它是一个1*1*nz的feature map
# nn.ConvTranspose2d(opt.nz, ngf * 8, 4, 1, 0, bias=False),
# nn.BatchNorm2d(ngf * 8),
# nn.ReLU(True),
# # 上一步的输出形状：(ngf*8) x 4 x 4
# nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
# nn.BatchNorm2d(ngf * 4),
# nn.ReLU(True),
# # 上一步的输出形状： (ngf*4) x 8 x 8
# nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
# nn.BatchNorm2d(ngf * 2),
# nn.ReLU(True),
# # 上一步的输出形状： (ngf*2) x 16 x 16
# nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
# nn.BatchNorm2d(ngf),
# nn.ReLU(True),
# # 上一步的输出形状：(ngf) x 32 x 32
# nn.ConvTranspose2d(ngf, 3, 5, 3, 1, bias=False),
# nn.Tanh() # 输出范围 -1~1 故而采用Tanh
# # 输出形状：3 x 96 x 96
# )
# def forward(self, input):
# # input = input.view(-1, opt.nz, 1, 1)
# return self.main(input)