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vae_module.py
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vae_module.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision.utils import save_image
import numpy as np
import matplotlib.pyplot as plt
from tool import visualize_ls, sample, get_param
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
x_dim=12 # dimention of latent variable
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 256)
self.fc21 = nn.Linear(256, x_dim)
self.fc22 = nn.Linear(256, x_dim)
self.fc3 = nn.Linear(x_dim, 256)
self.fc4 = nn.Linear(256, 784)
def encode(self, o_d):
h1 = F.relu(self.fc1(o_d))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return mu + eps*std
def decode(self, x_d):
h3 = F.relu(self.fc3(x_d))
return torch.sigmoid(self.fc4(h3))
def forward(self, o_d):
mu, logvar = self.encode(o_d.view(-1, 784))
x_d = self.reparameterize(mu, logvar)
return self.decode(x_d), mu, logvar, x_d
# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(self, recon_x, o_d, en_mu, en_logvar, gmm_mu, gmm_var, iteration):
BCE = F.binary_cross_entropy(recon_x, o_d.view(-1, 784), reduction='sum')
# see Appendix B from VAE paper:
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
# https://arxiv.org/abs/1312.6114
if iteration != 0:
gmm_mu = nn.Parameter(gmm_mu)
prior_mu = gmm_mu
prior_mu.requires_grad = False
prior_mu = prior_mu.expand_as(en_mu).to(device)
gmm_var = nn.Parameter(gmm_var)
prior_var = gmm_var
prior_var.requires_grad = False
prior_var = prior_var.expand_as(en_logvar).to(device)
prior_logvar = nn.Parameter(prior_var.log())
prior_logvar.requires_grad = False
prior_logvar = prior_logvar.expand_as(en_logvar).to(device)
var_division = en_logvar.exp() / prior_var # Σ_0 / Σ_1
diff = en_mu - prior_mu # μ_１ - μ_0
diff_term = diff *diff / prior_var # (μ_1 - μ_0)(μ_1 - μ_0)/Σ_1
logvar_division = prior_logvar - en_logvar # log|Σ_1| - log|Σ_0| = log(|Σ_1|/|Σ_2|)
KLD = 0.5 * ((var_division + diff_term + logvar_division).sum(1) - x_dim)
else:
KLD = -0.5 * torch.sum(1 + en_logvar - en_mu.pow(2) - en_logvar.exp())
return BCE + KLD
def train(iteration, gmm_mu, gmm_var, epoch, train_loader, all_loader, model_dir="./vae_gmm"):
prior_mean = torch.Tensor(len(train_loader), x_dim).float().fill_(0.0) # 最初のVAEの事前分布の\mu
model = VAE().to(device)
print("VAE Training Start")
#loss_list = []
#epoch_list = np.arange(epoch)
#model.load_state_dict(torch.load(save_dir+"/vae.pth"))
#if first!=True:
#print("前回の学習パラメータの読み込み")
#model.load_state_dict(torch.load(save_dir+"/vae.pth"))
#model.load_state_dict(torch.load(save_dir))
optimizer = optim.Adam(model.parameters(), lr=1e-3)
loss_list = np.zeros((epoch))
for i in range(epoch):
model.train()
train_loss = 0
for batch_idx, (data, _) in enumerate(train_loader):
data = data.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar, x_d = model(data)
if iteration==0: # when mutual learning first iteration prior is N(0,I)
loss = model.loss_function(recon_batch, data, mu, logvar, gmm_mu=None, gmm_var=None, iteration=iteration)
else: # when mutual learning first iteration prior is N(gmm_mu,gmm_var)
loss = model.loss_function(recon_batch, data, mu, logvar, gmm_mu[batch_idx], gmm_var[batch_idx], iteration=iteration)
loss = loss.mean()
loss.backward()
train_loss += loss.item()
optimizer.step()
if i == 0 or (i+1) % 50 == 0 or i == (epoch-1):
print('====> Epoch: {} Average loss: {:.4f}'.format(
i+1, train_loss / len(train_loader.dataset)))
loss_list[i] = -(train_loss / len(train_loader.dataset))
# plot loss
plt.figure()
plt.plot(range(0,epoch), loss_list, color="blue", label="ELBO")
if iteration!=0:
loss_0 = np.load(model_dir+'/npy/loss_0.npy')
plt.plot(range(0,epoch), loss_0, color="red", label="ELBO_I0")
plt.xlabel('epoch'); plt.ylabel('ELBO'); plt.legend(loc='lower right')
plt.savefig(model_dir+'/graph/vae_loss_'+str(iteration)+'.png')
plt.close()
np.save(model_dir+'/npy/loss_'+str(iteration)+'.npy', np.array(loss_list)) # save loss as a .npy
torch.save(model.state_dict(), model_dir+"/pth/vae_"+str(iteration)+".pth") # save model as a .pth
# inference of latent variables for all data points
x_d, label = send_all_z(iteration=iteration, all_loader=all_loader, model_dir=model_dir)
return x_d, label, loss_list
def decode(iteration, decode_k, sample_num, model_dir="./vae_gmm"):
model = VAE().to(device)
model.load_state_dict(torch.load(str(model_dir)+"/pth/vae_"+str(iteration)+".pth"))
model.eval()
mu_gmm_kd, lambda_gmm_kdd, pi_gmm_k = get_param(iteration, model_dir=model_dir)
manual_sample, random_sample = sample(iteration=iteration, x_dim=x_dim,
mu_gmm=mu_gmm_kd, lambda_gmm=lambda_gmm_kdd,
sample_num=sample_num, sample_k=decode_k, model_dir=model_dir
)
sample_d = manual_sample
sample_d = torch.from_numpy(sample_d.astype(np.float32)).clone()
with torch.no_grad():
sample_d = sample_d.to(device)
#sample_d = torch.from_numpy(sample_d).to(device)
sample_d = model.decode(sample_d).cpu()
save_image(sample_d.view(sample_num, 1, 28, 28),model_dir+'/recon/manual_'+str(decode_k)+'.png')
def plot_latent(iteration, all_loader, model_dir="./vae_gmm"): # VAEの潜在空間を可視化するメソッド
print("Plot latent space")
model = VAE().to(device)
model.load_state_dict(torch.load(model_dir+"/pth/vae_"+str(iteration)+".pth"))
model.eval()
for batch_idx, (data, label) in enumerate(all_loader):
data = data.to(device)
recon_batch, mu, logvar, x_d = model(data)
x_d = x_d.cpu()
visualize_ls(iteration, x_d.detach().numpy(), label, model_dir)
break
def test(epoch):
model = VAE().to(device)
model.load_state_dict(torch.load(file_name+"/vae.pth"))
model.eval()
test_loss = 0
with torch.no_grad():
for i, (data, _) in enumerate(test_loader):
data = data.to(device)
recon_batch, mu, logvar = model(data)
loss = model.loss_function(recon_batch, data, mu, logvar, args.category)
test_loss += loss.mean()
test_loss.item()
if i == 0:
n = min(data.size(0), 18)
comparison = torch.cat([data[:n],
recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
save_image(comparison.cpu(),
'image/recon_' + str(epoch) + '.png', nrow=n)
def send_all_z(iteration, all_loader, model_dir="./vae_gmm"): # method to send vae latent variable:x_d to gmm
model = VAE().to(device)
model.load_state_dict(torch.load(model_dir+"/pth/vae_"+str(iteration)+".pth"))
model.eval()
for batch_idx, (data, label) in enumerate(all_loader):
data = data.to(device)
recon_batch, mu, logvar, x_d = model(data)
x_d = x_d.cpu()
label = label.cpu()
return x_d.detach().numpy(), label.detach().numpy()