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infoGAN.py
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import utils, torch, time, os, pickle, itertools
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
from dataloader import dataloader
import sys
from torch.autograd import Variable
from mnist_train import Net
from nt_xent import NTXentLoss
import imageio
class HLoss(nn.Module):
def __init__(self):
super(HLoss, self).__init__()
def forward(self, x):
b = F.softmax(x) * F.log_softmax(x)
b = -1 * b.sum(dim = 1)
return b.mean()
class Generator(nn.Module):
def __init__(self, input_dim=100, output_dim=1, input_size=32, len_discrete_code=10):
super(Generator, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.input_size = input_size
self.len_discrete_code = len_discrete_code # categorical distribution (i.e. label)
self.fc = nn.Sequential(
nn.Linear(self.input_dim + self.len_discrete_code, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(),
nn.Linear(1024, 128 * (self.input_size // 4) * (self.input_size // 4)),
nn.BatchNorm1d(128 * (self.input_size // 4) * (self.input_size // 4)),
nn.ReLU(),
)
self.deconv = nn.Sequential(
nn.ConvTranspose2d(128, 64, 4, 2, 1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.ConvTranspose2d(64, self.output_dim, 4, 2, 1),
nn.Tanh(),
)
utils.initialize_weights(self)
def forward(self, z, dist_code):
x = torch.cat([z, dist_code], 1)
x = self.fc(x)
x = x.view(-1, 128, (self.input_size // 4), (self.input_size // 4))
x = self.deconv(x)
return x
class Front_end(nn.Module):
def __init__(self, input_dim=1, input_size=32):
super(Front_end, self).__init__()
self.input_dim = input_dim
self.input_size = input_size
self.conv = nn.Sequential(
nn.Conv2d(self.input_dim, 64, 4, 2, 1),
nn.LeakyReLU(0.2),
nn.Conv2d(64, 128, 4, 2, 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
)
self.fc = nn.Sequential(
nn.Linear(128 * (self.input_size // 4) * (self.input_size // 4), 1024),
nn.BatchNorm1d(1024),
nn.LeakyReLU(0.2)
)
utils.initialize_weights(self)
def forward(self, input):
x = self.conv(input)
x = x.view(-1, 128 * (self.input_size // 4) * (self.input_size // 4))
a = self.fc(x)
return a
class Discriminator(nn.Module):
# Module which predicts real/fake for the image
def __init__(self, output_dim=1):
super(Discriminator, self).__init__()
self.output_dim = output_dim
self.fc = nn.Sequential(
nn.Linear(1024, self.output_dim),
nn.Sigmoid()
)
utils.initialize_weights(self)
def forward(self, input):
x = self.fc(input)
return x
class Latent_predictor(nn.Module):
# Module which reconstructs the latent codes from the fake images
def __init__(self, len_discrete_code = 10):
super(Latent_predictor, self).__init__()
self.len_discrete_code = len_discrete_code # categorical distribution (i.e. label)
self.fc = nn.Sequential(
nn.Linear(1024, 128),
nn.BatchNorm1d(128),
nn.LeakyReLU(0.2),
#nn.Linear(128, self.len_discrete_code)
)
self.fc1 =nn.Linear(128, self.len_discrete_code)
utils.initialize_weights(self)
def forward(self, input):
a = self.fc(input)
b = self.fc1(a)
return a,b
class infoGAN(object):
def __init__(self, args):
# parameters
self.epoch = args.epoch
self.mytemp = args.mytemp
self.klwt = args.klwt
self.batch_size = args.batch_size
self.save_dir = args.save_dir
self.result_dir = args.result_dir
self.dataset = args.dataset
self.log_dir = args.log_dir
self.gpu_mode = args.gpu_mode
self.model_name = args.gan_type
self.input_size = args.input_size
self.save_ind = args.ind
self.z_dim = 62
self.len_discrete_code = 10 # categorical distribution (i.e. label)
self.exp_id = args.ind - 1
self.sample_num = 100
temp = torch.tensor(self.len_discrete_code * [float(1)/ self.len_discrete_code]).cuda()
self.prior_parameters = Variable(temp, requires_grad = True)
self.repeat_checks = 100
self.mnist_net = Net().cuda()
state_dict = torch.load('mnist_cnn.pth')
self.mnist_net.load_state_dict(state_dict)
# load dataset
self.data_loader = dataloader(self.dataset, self.input_size, self.batch_size, exp_ind=self.exp_id)
print ("Length of dataloader", len(self.data_loader))
data = self.data_loader.__iter__().__next__()[0]
# networks init
self.G = Generator(input_dim = self.z_dim, output_dim = data.shape[1], input_size = self.input_size, len_discrete_code = self.len_discrete_code)
self.FE = Front_end(input_dim = data.shape[1], input_size = self.input_size)
self.D = Discriminator(output_dim=1)
self.Q = Latent_predictor(len_discrete_code = self.len_discrete_code)
self.G_optimizer = optim.Adam([{'params':self.G.parameters()}, {'params':self.Q.parameters()}, {'params':self.prior_parameters}], lr=args.lrG, betas=(args.beta1, args.beta2))
self.D_optimizer = optim.Adam([{'params':self.FE.parameters()}, {'params':self.D.parameters()}], lr=args.lrD, betas=(args.beta1, args.beta2))
self.nt_xent_criterion = NTXentLoss('cuda', self.batch_size, self.mytemp, True)
if self.gpu_mode:
self.G.cuda()
self.D.cuda()
self.FE.cuda()
self.Q.cuda()
self.BCE_loss = nn.BCELoss().cuda()
self.CE_loss = nn.CrossEntropyLoss().cuda()
self.MSE_loss = nn.MSELoss().cuda()
self.entropy_loss = HLoss().cuda()
else:
ghj = 1
print('---------- Networks architecture -------------')
utils.print_network(self.G)
utils.print_network(self.D)
print('-----------------------------------------------')
# Fixed noise
self.sample_z_ = torch.randn((self.sample_num, self.z_dim))
temp = torch.zeros((self.len_discrete_code, 1))
for i in range(self.len_discrete_code):
temp[i, 0] = i
temp_y = torch.zeros((self.sample_num, 1))
for i in range(self.sample_num):
temp_y[i] = temp_y[i] + (i / (self.sample_num/self.len_discrete_code))
self.sample_y_ = torch.zeros((self.sample_num, self.len_discrete_code)).scatter_(1, temp_y.type(torch.LongTensor), 1)
if self.gpu_mode:
self.sample_z_, self.sample_y_ = \
self.sample_z_.cuda(), self.sample_y_.cuda()
def sample_gumbel(self, shape, eps = 1e-20):
u = torch.FloatTensor(shape, self.len_discrete_code).cuda().uniform_(0, 1)
return -torch.log(-torch.log(u + eps) + eps)
def gumbel_softmax_sample(self, logits, temp, batch_size):
y = logits + self.sample_gumbel(batch_size)
return torch.nn.functional.softmax( y / temp)
def approx_latent(self, params):
params = F.softmax(params)
log_params = torch.log(params)
c = self.gumbel_softmax_sample(log_params, temp = 0.1, batch_size = self.batch_size)
return c
def train(self):
self.train_hist = {}
self.train_hist['D_loss'] = []
self.train_hist['G_loss'] = []
self.train_hist['info_loss'] = []
self.train_hist['per_epoch_time'] = []
self.train_hist['total_time'] = []
self.prior_denominator = 3
self.y_real_, self.y_fake_ = torch.ones(self.batch_size, 1, device = "cuda"), torch.zeros(self.batch_size, 1, device = "cuda")
self.D.train()
print('training start!!')
start_time = time.time()
for epoch in range(self.epoch):
self.G.train()
epoch_start_time = time.time()
for iter, (x_, xa_, y_) in enumerate(self.data_loader):
if iter == self.data_loader.dataset.__len__() // self.batch_size:
break
z_ = torch.randn((self.batch_size, self.z_dim), device = "cuda")
y_disc_= self.approx_latent(self.prior_parameters)
if self.gpu_mode:
x_ = x_.cuda()
xa_ = xa_.cuda()
# update D network
self.D_optimizer.zero_grad()
# real part
real_intm = self.FE(x_)
real_intm_aux = self.FE(xa_)
real_logits = self.D(real_intm)
D_real_loss = self.BCE_loss(real_logits, self.y_real_)
# fake part
fx = self.G(z_, y_disc_)
fake_intm_tmp = self.FE(fx.detach())
fake_logits_tmp = self.D(fake_intm_tmp)
D_fake_loss = self.BCE_loss(fake_logits_tmp, self.y_fake_)
D_loss = D_real_loss + D_fake_loss
D_loss.backward(retain_graph=True)
self.D_optimizer.step()
# update G network
self.G_optimizer.zero_grad()
fake_intm = self.FE(fx)
fake_logits = self.D(fake_intm)
G_fake_loss = self.BCE_loss(fake_logits, self.y_real_)
# information loss
_,c_pred = self.Q(fake_intm)
info_loss = self.CE_loss(c_pred, torch.max(y_disc_, 1)[1])
# Augmentation similarity loss
real_c_pred,_ = self.Q(real_intm)
real_aux_c_pred,_ = self.Q(real_intm_aux)
real_c_pred = F.normalize(real_c_pred,dim=1)
real_aux_c_pred = F.normalize(real_aux_c_pred,dim=1)
kl_loss = self.nt_xent_criterion(real_c_pred, real_aux_c_pred)
G_loss = G_fake_loss + info_loss + self.klwt*kl_loss
G_loss.backward(retain_graph=True)
self.G_optimizer.step()
if ((iter + 1) % 100) == 0:
print("Epoch: [%2d] [%4d/%4d] D_loss: %.8f, G_loss: %.8f, Info_loss: %.8f, KL_loss: %.8f" %
((epoch + 1), (iter + 1), self.data_loader.dataset.__len__() // self.batch_size, D_loss.item(), G_fake_loss.item(), info_loss.item(), kl_loss.item()))
self.train_hist['per_epoch_time'].append(time.time() - epoch_start_time)
with torch.no_grad():
self.visualize_results((epoch+1))
if epoch % 10 == 0:
if not os.path.exists('./saved_models'):
os.makedirs('./saved_models')
torch.save(self.G.state_dict(), os.path.join('./saved_models', 'netG%d.pth' %(self.exp_id)))
torch.save(self.D.state_dict(), os.path.join('./saved_models', 'netD%d.pth' %(self.exp_id)))
torch.save(self.FE.state_dict(), os.path.join('./saved_models', 'netFE%d.pth' %(self.exp_id)))
torch.save(self.Q.state_dict(), os.path.join('./saved_models', 'netQ%d.pth' %(self.exp_id)))
self.train_hist['total_time'].append(time.time() - start_time)
print("Avg one epoch time: %.2f, total %d epochs time: %.2f" % (np.mean(self.train_hist['per_epoch_time']),
self.epoch, self.train_hist['total_time'][0]))
print("Training finish!... save training results")
self.save()
self.loss_plot(self.train_hist, os.path.join(self.save_dir, self.dataset, self.model_name), self.model_name)
def visualize_results(self, epoch):
self.G.eval()
if not os.path.exists(self.result_dir + '/' + self.dataset + '/' + self.model_name):
os.makedirs(self.result_dir + '/' + self.dataset + '/' + self.model_name)
image_frame_dim = int(np.floor(np.sqrt(self.sample_num)))
""" style by class """
samples = self.G(self.sample_z_, self.sample_y_)
if self.gpu_mode:
samples = samples.cpu().data.numpy().transpose(0, 2, 3, 1)
else:
samples = samples.data.numpy().transpose(0, 2, 3, 1)
samples = (samples + 1) / 2
utils.save_images(samples[:image_frame_dim * image_frame_dim, :, :, :], [image_frame_dim, image_frame_dim],
self.result_dir + '/disc_interpolation%d.png' %(self.save_ind))
def save(self):
save_dir = os.path.join(self.save_dir, self.dataset, self.model_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(self.G.state_dict(), os.path.join(save_dir, self.model_name + '_G.pkl'))
torch.save(self.D.state_dict(), os.path.join(save_dir, self.model_name + '_D.pkl'))
with open(os.path.join(save_dir, self.model_name + '_history.pkl'), 'wb') as f:
pickle.dump(self.train_hist, f)
def load(self):
save_dir = os.path.join(self.save_dir, self.dataset, self.model_name)
self.G.load_state_dict(torch.load(os.path.join(save_dir, self.model_name + '_G.pkl')))
self.D.load_state_dict(torch.load(os.path.join(save_dir, self.model_name + '_D.pkl')))
def loss_plot(self, hist, path='Train_hist.png', model_name=''):
x = range(len(hist['D_loss']))
y1 = hist['D_loss']
y2 = hist['G_loss']
y3 = hist['info_loss']
plt.plot(x, y1, label='D_loss')
plt.plot(x, y2, label='G_loss')
plt.plot(x, y3, label='info_loss')
plt.xlabel('Iter')
plt.ylabel('Loss')
plt.legend(loc=4)
plt.grid(True)
plt.tight_layout()
path = os.path.join(path, model_name + '_loss.png')
plt.savefig(path)