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import torch
import torch.nn as nn
import torch.nn.parallel
from miscc.config import cfg
from torch.autograd import Variable
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
# Upsale the spatial size by a factor of 2
def upBlock(in_planes, out_planes):
block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='nearest'),
conv3x3(in_planes, out_planes),
nn.BatchNorm2d(out_planes),
nn.ReLU(True))
return block
class ResBlock(nn.Module):
def __init__(self, channel_num):
super(ResBlock, self).__init__()
self.block = nn.Sequential(
conv3x3(channel_num, channel_num),
nn.BatchNorm2d(channel_num),
nn.ReLU(True),
conv3x3(channel_num, channel_num),
nn.BatchNorm2d(channel_num))
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
residual = x
out = self.block(x)
out += residual
out = self.relu(out)
return out
class CA_NET(nn.Module):
# some code is modified from vae examples
# (https://github.com/pytorch/examples/blob/master/vae/main.py)
def __init__(self):
super(CA_NET, self).__init__()
self.t_dim = cfg.TEXT.DIMENSION
self.c_dim = cfg.GAN.CONDITION_DIM
self.fc = nn.Linear(self.t_dim, self.c_dim * 2, bias=True)
self.relu = nn.ReLU()
def encode(self, text_embedding):
x = self.relu(self.fc(text_embedding))
mu = x[:, :self.c_dim]
logvar = x[:, self.c_dim:]
return mu, logvar
def reparametrize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
if cfg.CUDA:
eps = torch.cuda.FloatTensor(std.size()).normal_()
else:
eps = torch.FloatTensor(std.size()).normal_()
eps = Variable(eps)
return eps.mul(std).add_(mu)
def forward(self, text_embedding):
mu, logvar = self.encode(text_embedding)
c_code = self.reparametrize(mu, logvar)
return c_code, mu, logvar
class D_GET_LOGITS(nn.Module):
def __init__(self, ndf, nef, bcondition=True):
super(D_GET_LOGITS, self).__init__()
self.df_dim = ndf
self.ef_dim = nef
self.bcondition = bcondition
if bcondition:
self.outlogits = nn.Sequential(
conv3x3(ndf * 8 + nef, ndf * 8),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=4),
nn.Sigmoid())
else:
self.outlogits = nn.Sequential(
nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=4),
nn.Sigmoid())
def forward(self, h_code, c_code=None):
# conditioning output
if self.bcondition and c_code is not None:
c_code = c_code.view(-1, self.ef_dim, 1, 1)
c_code = c_code.repeat(1, 1, 4, 4)
# state size (ngf+egf) x 4 x 4
h_c_code = torch.cat((h_code, c_code), 1)
else:
h_c_code = h_code
output = self.outlogits(h_c_code)
return output.view(-1)
# ############# Networks for stageI GAN #############
class STAGE1_G(nn.Module):
def __init__(self):
super(STAGE1_G, self).__init__()
self.gf_dim = cfg.GAN.GF_DIM * 8
self.ef_dim = cfg.GAN.CONDITION_DIM
self.z_dim = cfg.Z_DIM
self.define_module()
def define_module(self):
ninput = self.z_dim + self.ef_dim
ngf = self.gf_dim
# TEXT.DIMENSION -> GAN.CONDITION_DIM
self.ca_net = CA_NET()
# -> ngf x 4 x 4
self.fc = nn.Sequential(
nn.Linear(ninput, ngf * 4 * 4, bias=False),
nn.BatchNorm1d(ngf * 4 * 4),
nn.ReLU(True))
# ngf x 4 x 4 -> ngf/2 x 8 x 8
self.upsample1 = upBlock(ngf, ngf // 2)
# -> ngf/4 x 16 x 16
self.upsample2 = upBlock(ngf // 2, ngf // 4)
# -> ngf/8 x 32 x 32
self.upsample3 = upBlock(ngf // 4, ngf // 8)
# -> ngf/16 x 64 x 64
self.upsample4 = upBlock(ngf // 8, ngf // 16)
# -> 3 x 64 x 64
self.img = nn.Sequential(
conv3x3(ngf // 16, 3),
nn.Tanh())
def forward(self, text_embedding, noise):
c_code, mu, logvar = self.ca_net(text_embedding)
z_c_code = torch.cat((noise, c_code), 1)
h_code = self.fc(z_c_code)
h_code = h_code.view(-1, self.gf_dim, 4, 4)
h_code = self.upsample1(h_code)
h_code = self.upsample2(h_code)
h_code = self.upsample3(h_code)
h_code = self.upsample4(h_code)
# state size 3 x 64 x 64
fake_img = self.img(h_code)
return None, fake_img, mu, logvar
class STAGE1_D(nn.Module):
def __init__(self):
super(STAGE1_D, self).__init__()
self.df_dim = cfg.GAN.DF_DIM
self.ef_dim = cfg.GAN.CONDITION_DIM
self.define_module()
def define_module(self):
ndf, nef = self.df_dim, self.ef_dim
self.encode_img = nn.Sequential(
nn.Conv2d(3, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (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),
# state size (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),
# state size (ndf*4) x 8 x 8
nn.Conv2d(ndf*4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
# state size (ndf * 8) x 4 x 4)
nn.LeakyReLU(0.2, inplace=True)
)
self.get_cond_logits = D_GET_LOGITS(ndf, nef)
self.get_uncond_logits = None
def forward(self, image):
img_embedding = self.encode_img(image)
return img_embedding
# ############# Networks for stageII GAN #############
class STAGE2_G(nn.Module):
def __init__(self, STAGE1_G):
super(STAGE2_G, self).__init__()
self.gf_dim = cfg.GAN.GF_DIM
self.ef_dim = cfg.GAN.CONDITION_DIM
self.z_dim = cfg.Z_DIM
self.STAGE1_G = STAGE1_G
# fix parameters of stageI GAN
for param in self.STAGE1_G.parameters():
param.requires_grad = False
self.define_module()
def _make_layer(self, block, channel_num):
layers = []
for i in range(cfg.GAN.R_NUM):
layers.append(block(channel_num))
return nn.Sequential(*layers)
def define_module(self):
ngf = self.gf_dim
# TEXT.DIMENSION -> GAN.CONDITION_DIM
self.ca_net = CA_NET()
# --> 4ngf x 16 x 16
self.encoder = nn.Sequential(
conv3x3(3, ngf),
nn.ReLU(True),
nn.Conv2d(ngf, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.Conv2d(ngf * 2, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True))
self.hr_joint = nn.Sequential(
conv3x3(self.ef_dim + ngf * 4, ngf * 4),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True))
self.residual = self._make_layer(ResBlock, ngf * 4)
# --> 2ngf x 32 x 32
self.upsample1 = upBlock(ngf * 4, ngf * 2)
# --> ngf x 64 x 64
self.upsample2 = upBlock(ngf * 2, ngf)
# --> ngf // 2 x 128 x 128
self.upsample3 = upBlock(ngf, ngf // 2)
# --> ngf // 4 x 256 x 256
self.upsample4 = upBlock(ngf // 2, ngf // 4)
# --> 3 x 256 x 256
self.img = nn.Sequential(
conv3x3(ngf // 4, 3),
nn.Tanh())
def forward(self, text_embedding, noise):
_, stage1_img, _, _ = self.STAGE1_G(text_embedding, noise)
stage1_img = stage1_img.detach()
encoded_img = self.encoder(stage1_img)
c_code, mu, logvar = self.ca_net(text_embedding)
c_code = c_code.view(-1, self.ef_dim, 1, 1)
c_code = c_code.repeat(1, 1, 16, 16)
i_c_code = torch.cat([encoded_img, c_code], 1)
h_code = self.hr_joint(i_c_code)
h_code = self.residual(h_code)
h_code = self.upsample1(h_code)
h_code = self.upsample2(h_code)
h_code = self.upsample3(h_code)
h_code = self.upsample4(h_code)
fake_img = self.img(h_code)
return stage1_img, fake_img, mu, logvar
class STAGE2_D(nn.Module):
def __init__(self):
super(STAGE2_D, self).__init__()
self.df_dim = cfg.GAN.DF_DIM
self.ef_dim = cfg.GAN.CONDITION_DIM
self.define_module()
def define_module(self):
ndf, nef = self.df_dim, self.ef_dim
self.encode_img = nn.Sequential(
nn.Conv2d(3, ndf, 4, 2, 1, bias=False), # 128 * 128 * ndf
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True), # 64 * 64 * ndf * 2
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True), # 32 * 32 * ndf * 4
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True), # 16 * 16 * ndf * 8
nn.Conv2d(ndf * 8, ndf * 16, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 16),
nn.LeakyReLU(0.2, inplace=True), # 8 * 8 * ndf * 16
nn.Conv2d(ndf * 16, ndf * 32, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 32),
nn.LeakyReLU(0.2, inplace=True), # 4 * 4 * ndf * 32
conv3x3(ndf * 32, ndf * 16),
nn.BatchNorm2d(ndf * 16),
nn.LeakyReLU(0.2, inplace=True), # 4 * 4 * ndf * 16
conv3x3(ndf * 16, ndf * 8),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True) # 4 * 4 * ndf * 8
)
self.get_cond_logits = D_GET_LOGITS(ndf, nef, bcondition=True)
self.get_uncond_logits = D_GET_LOGITS(ndf, nef, bcondition=False)
def forward(self, image):
img_embedding = self.encode_img(image)
return img_embedding
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