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multiconv.py
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import os
import numpy as np
import torch
from torch import optim
from torch import nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets as dset
import torchvision.transforms as transforms
from torchvision.utils import save_image
# from layers.SNConv2d import SNConv2d
# from layers.SNLinear import SNLinear
import datasets
import gan
from logger import Logger
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class LINnet_G0(nn.Module):
def __init__(self, nc=1, ngf=64, nz=100, bias=False): # 256 ok
super(LINnet_G0,self).__init__()
self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz,ngf*8,kernel_size=(3, 5), bias=bias),
nn.BatchNorm2d(ngf*8),
nn.ReLU())
# 3 x 5
self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf*8,ngf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*4),
nn.ReLU())
# 6 x 10
self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf*4,ngf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*2),
nn.ReLU())
# 12 x 20
self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf*2,ngf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf),
nn.ReLU())
# 24 x 40
self.layer5 = nn.Sequential(nn.ConvTranspose2d(ngf,nc,kernel_size=4,stride=2,padding=1, bias=bias),
# nn.Sigmoid())
nn.Tanh())
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out
class LINnet_G(nn.Module):
def __init__(self, nc=1, ngf=64, nz=100, bias=False, n_classes=6): # 256 ok
self.n_classes = n_classes
super(LINnet_G,self).__init__()
self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz,ngf*8,kernel_size=(3, 5), bias=bias),
nn.BatchNorm2d(ngf*8),
nn.ReLU())
# 3 x 5
self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf*8,ngf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*4),
nn.ReLU())
# 6 x 10
self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf*4,ngf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*2),
nn.ReLU())
# 12 x 20
self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf*2,ngf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf),
nn.ReLU())
# 24 x 40
self.layer5 = nn.Sequential(nn.ConvTranspose2d(ngf,nc,kernel_size=4,stride=2,padding=1, bias=bias),
# nn.Sigmoid())
nn.Tanh())
# self.linear = nn.Parameter(torch.FloatTensor(41, 10, 100).normal_(0.1, 0.02))
self.apply(weights_init)
def forward(self,x):
# _, y = torch.max(x[:,-41:,:,:].sum(dim=2).sum(dim=2), dim=1)
# x = x[:,:-41,:,:].contiguous()
# matrix = Variable(torch.index_select(self.linear.data, 0, y.data)).contiguous()
# x = (matrix.view(-1, 10, 100) * x.view(-1, 10, 1)).sum(dim=1)
# x = x.view(-1, 100, 1, 1)
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out
class ListModule(nn.Module):
def __init__(self, *args):
super(ListModule, self).__init__()
idx = 0
for module in args:
self.add_module(str(idx), module)
idx += 1
def __getitem__(self, idx):
if idx < 0 or idx >= len(self._modules):
raise IndexError('index {} is out of range'.format(idx))
it = iter(self._modules.values())
for i in range(idx):
next(it)
return next(it)
def __iter__(self):
return iter(self._modules.values())
def __len__(self):
return len(self._modules)
class LINnet_D(nn.Module):
def __init__(self,nc=1,ndf=64,BN=True,bias=False,n_classes=6): # 128 ok
super(LINnet_D,self).__init__()
self.n_classes=n_classes
# 48 x 80
self.layer1 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf),
nn.LeakyReLU(0.2,inplace=True))
# 24 x 40
self.layer2 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*2),
nn.LeakyReLU(0.2,inplace=True))
# 12 x 20
self.layer3 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*4),
nn.LeakyReLU(0.2,inplace=True))
# 6 x 10
self.layer4 = nn.Sequential(nn.Conv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*8),
nn.LeakyReLU(0.2,inplace=True))
# 3 x 5
self.layer5 = ListModule(*[nn.Sequential(nn.Conv2d(ndf*8,1,kernel_size=(3, 5),stride=1,padding=0,bias=bias)).cuda() for _ in range(41)])#,
# nn.Sigmoid())
# self.linear = nn.Linear(n_classes, 3*5*ndf*8)#,
# self.embedding = nn.Linear(n_classes, ndf*8, bias=False)
# self.embedding.weight.data.normal_(0,1)
# self.linear = nn.Linear(ndf*8, 1, bias=False)
# self.linear = nn.Linear(ndf*8+n_classes, 1, bias=False)
self.apply(weights_init)
def forward(self,x,y):
# x, y = input
h = self.layer1(x)
h = self.layer2(h)
h = self.layer3(h)
h = self.layer4(h)
output = Variable(torch.FloatTensor(h.size(0))).cuda()
for i in range(h.size(0)):
output[i] = self.layer5[y.data[i]](h[i:i+1,:,:,:]).view(-1)
# h = torch.sum(h, dim=2).sum(dim=2) # Global pooling
# # output = self.linear(h)
# th = torch.cuda if h.is_cuda else torch
# y_onehot = th.FloatTensor(y.size()[0], self.n_classes)
# y_onehot.zero_()
# y_onehot.scatter_(1, y.data.view(-1,1), 1)
# y_onehot = Variable(y_onehot)
# # # h = torch.cat([h, y_onehot], dim=1)
# w_y = self.linear(y_onehot.float())
# # w_y = self.embedding(y_onehot.float())
# # # output += torch.sum(w_y * h, dim=1).view(-1, 1)
# output = torch.sum(w_y * h.view(h.size(0), -1), dim=1).view(-1, 1)
# # print(h.size())
# # output = self.linear(h)
return output.view(-1)
opt = gan.Options()
opt.cuda = True
opt.path = 'multiconvLIN41_conv/'
opt.num_iter = 100000
opt.batch_size = 64
opt.visualize_nth = 2000
opt.conditionalD = False
opt.conditional = True
opt.wgangp_lambda = 10.0
opt.n_classes = 41
opt.nz = (100,1,1)
opt.num_disc_iters = 1
opt.checkpoints = [1000, 2000, 5000, 10000, 20000, 40000, 60000, 100000, 200000, 300000, 500000]
log = Logger(base_dir=opt.path, tag='multiGAN')
# data = datasets.LINDataset(proteins=['Alp14', 'Arp3', 'Cki2', 'Mkh1', 'Sid2', 'Tea1'], transform=transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), conditional=opt.conditional)
data = datasets.LINDataset(proteins='all', transform=transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), conditional=opt.conditional)
#['Alp14', 'Arp3', 'Cki2', 'Mkh1', 'Sid2', 'Tea1', 'Act1', 'Gef1', 'For3', 'Ras1', 'Scd2', 'Tip1']
mydataloader = datasets.MyDataLoader()
data_iter = mydataloader.return_iterator(DataLoader(data, batch_size=opt.batch_size, shuffle=True, num_workers=4), is_cuda=opt.cuda, conditional=opt.conditional, pictures=True)
# netG = mnistnet.Generator(nz=100, BN=True)
# netD = mnistnet.Discriminator(nc=1, BN=True)
netG = LINnet_G0(nc=2,nz=141)
netD = LINnet_D(nc=2,BN=True,n_classes=41)
optimizerD = optim.Adam(netD.parameters(), lr=2e-4, betas=(.5, .999))
optimizerG = optim.Adam(netG.parameters(), lr=2e-4, betas=(.5, .999))
def save_samples(gan, i_iter):
gan.netG.eval()
if 'noise' not in save_samples.__dict__:
save_samples.noise = Variable(gan.gen_latent_noise(opt.n_classes*10, opt.nz))
if not os.path.exists(opt.path + 'tmp/'):
os.makedirs(opt.path + 'tmp/')
y = np.repeat(np.arange(opt.n_classes), 10)
y = torch.autograd.Variable(torch.from_numpy(y))
if gan.opt.cuda:
y = y.cuda()
noise = save_samples.noise
noise = gan.join_xy((noise, y))
fake = netG(noise)
fake = fake.view(-1, 2, 48, 80)
fake_01 = torch.FloatTensor(len(fake), 3, 48, 80).fill_(-1)
fake_01[:,:2,:,:] = (fake.data.cpu() + 1.0) * 0.5
# print(fake_01.min(), fake_01.max())
save_image(fake_01, opt.path + 'tmp/' + '{:0>5}.png'.format(i_iter), nrow=10)
# alkjfd
gan.netG.train()
def callback(gan, i_iter):
# if i_iter % 5000 == 0:
# save_inception_score(gan, i_iter)
if i_iter % 50 == 0:
save_samples(gan, i_iter)
if i_iter % 50 == 0:
log.save()
gan1 = gan.GAN(netG=netG, netD=netD, optimizerD=optimizerD, optimizerG=optimizerG, opt=opt)
gan1.train(data_iter, opt, logger=log, callback=callback)
torch.save(netG.state_dict(), opt.path + 'gen.pth')
torch.save(netD.state_dict(), opt.path + 'disc.pth')
log.close()