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vqvae.py
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import numpy as np
import torch, torchvision, os
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import transforms
from utils.visdom_utils import VisFunc
from utils.data import data_loader
from utils.model_mnist import MODEL_MNIST
from utils.model_cifar10 import MODEL_CIFAR10
from utils.model_pixelcnn import PIXELCNN
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
init_seed = 1
torch.manual_seed(init_seed)
torch.cuda.manual_seed(init_seed)
np.random.seed(init_seed)
np.set_printoptions(precision= 4)
torch.set_printoptions(precision = 4)
class Solver(object):
def __init__(self, args):
self.args = args
self.epoch = args.epoch
self.batch_size = args.batch_size
self.lr = args.lr
self.z_dim = args.z_dim
self.k_dim = args.k_dim
self.beta = args.beta
self.env_name = args.env_name
self.ckpt_dir = os.path.join('checkpoints',args.env_name)
self.global_iter = 0
self.dataset = args.dataset
self.fixed_x_num = args.fixed_x_num
self.output_dir = os.path.join(args.output_dir,args.env_name)
self.ckpt_load = args.ckpt_load
self.ckpt_save = args.ckpt_save
# Toy Network init
if self.dataset == 'MNIST':
self.model = MODEL_MNIST(k_dim=self.k_dim,z_dim=self.z_dim).cuda()
elif self.dataset == 'CIFAR10':
self.model = MODEL_CIFAR10(k_dim=self.k_dim,z_dim=self.z_dim).cuda()
# Visdom Sample Visualization
self.vf = VisFunc(enval=self.env_name,port=55558)
# Criterions
self.MSE_Loss = nn.MSELoss().cuda()
# Dataset init
self.train_data, self.train_loader = data_loader(args)
self.fixed_x = iter(self.train_loader).next()[0][:self.fixed_x_num]
# Optimizer
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.5, 0.999))
# Resume training
if self.ckpt_load : self.load_checkpoint()
def set_mode(self, mode='train'):
if mode == 'train' :
self.model.train()
elif mode == 'eval' :
self.model.eval()
else : raise('mode error. It should be either train or eval')
def save_checkpoint(self, state, filename='checkpoint.pth.tar'):
if not os.path.exists(self.ckpt_dir) : os.makedirs(self.ckpt_dir)
file_path = os.path.join(self.ckpt_dir,filename)
torch.save(state,file_path)
print("=> saved checkpoint '{}' (iter {})".format(file_path,self.global_iter))
def load_checkpoint(self):
filename = 'checkpoint.pth.tar'
file_path = os.path.join(self.ckpt_dir,filename)
if os.path.isfile(file_path):
print("=> loading checkpoint '{}'".format(file_path))
checkpoint = torch.load(file_path)
self.global_iter = checkpoint['iter']
self.model.load_state_dict(checkpoint['state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (iter {})"
.format(filename, checkpoint['iter']))
else:
print("=> no checkpoint found at '{}'".format(file_path))
def image_save(self, imgs, name='fixed', **kwargs):
# required imgs shape : batch_size x channels x width x height
if not os.path.exists(self.output_dir) : os.makedirs(self.output_dir)
filename = os.path.join(self.output_dir,name+'_'+str(self.global_iter)+'.jpg')
torchvision.utils.save_image(imgs,filename,**kwargs)
def train(self):
self.set_mode('train')
for e in range(self.epoch) :
recon_losses = []
z_and_sg_embd_losses = []
sg_z_and_embd_losses = []
for idx, (images,labels) in enumerate(self.train_loader):
self.global_iter += 1
X = Variable(images.cuda(),requires_grad=False)
X_recon, Z_enc, Z_dec, Z_enc_for_embd = self.model(X)
recon_loss = self.MSE_Loss(X_recon,X)
z_and_sg_embd_loss = self.MSE_Loss(Z_enc,Z_dec.detach())
sg_z_and_embd_loss = self.MSE_Loss(self.model._modules['embd'].weight,
Z_enc_for_embd.detach())
total_loss = recon_loss + sg_z_and_embd_loss + self.beta*z_and_sg_embd_loss
self.optimizer.zero_grad()
total_loss.backward(retain_graph=True)
Z_enc.backward(self.model.grad_for_encoder)
self.optimizer.step()
recon_losses.append(recon_loss.data)
z_and_sg_embd_losses.append(z_and_sg_embd_loss.data)
sg_z_and_embd_losses.append(sg_z_and_embd_loss.data)
# Sample Visualization
self.vf.imshow_multi(X_recon.data.cpu(),
title='random:{:d}'.format(e+1))
self.image_save(X_recon.data,name='random')
self.test()
# AVG Losses
recon_losses = torch.cat(recon_losses,0).mean()
z_and_sg_embd_losses = torch.cat(z_and_sg_embd_losses,0).mean()
sg_z_and_embd_losses = torch.cat(sg_z_and_embd_losses,0).mean()
print('[{:02d}/{:d}] recon_loss:{:.2f} z_sg_embd:{:.2f} sg_z_embd:{:.2f}'.format(
e+1,self.epoch,recon_losses,z_and_sg_embd_losses,sg_z_and_embd_losses))
print("[*] Training Finished!")
def test(self):
self.set_mode('eval')
X = Variable(self.fixed_x,requires_grad=False).cuda()
X_recon = self.model(X)[0]
X_cat = torch.cat([X,X_recon],0)
self.vf.imshow_multi(X_cat.data.cpu(),
nrow=self.fixed_x_num,
title='fixed_x_test:'+str(self.global_iter))
self.image_save(X_cat.data,name='fixed',nrow=self.fixed_x_num)
if self.ckpt_save :
self.save_checkpoint({
'iter':self.global_iter,
'args': self.args,
'state_dict': self.model.state_dict(),
'optimizer' : self.optimizer.state_dict(),
})
self.set_mode('train')