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cnn_vae_module_mnist.py
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cnn_vae_module_mnist.py
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from __future__ import print_function
import torch
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
h_dim = 1024
image_channels=1
image_size = 28
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1) # [10,1024]
class UnFlatten(nn.Module):
def forward(self, input, size=h_dim):
return input.view(input.size(0), size, 1, 1)
class VAE(nn.Module):
def __init__(self, image_channels=image_channels, h_dim=h_dim, x_dim=x_dim):
super(VAE, self).__init__()
self.encoder = nn.Sequential(
nn.Conv2d(in_channels=image_channels, out_channels=64, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(in_channels=256, out_channels=1024, kernel_size=4, stride=1, padding=0),
nn.ReLU(),
Flatten()
)
self.fc1 = nn.Linear(h_dim, x_dim)
self.fc2 = nn.Linear(h_dim, x_dim)
self.fc3 = nn.Linear(x_dim, h_dim)
self.decoder = nn.Sequential(
UnFlatten(),
nn.ConvTranspose2d(in_channels=h_dim, out_channels=512, kernel_size=4, stride=1, padding=0),
nn.ReLU(),
nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=3, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(in_channels=128, out_channels=image_channels, kernel_size=4, stride=2, padding=1),
#nn.ReLU(),
#nn.ConvTranspose2d(in_channels=16, out_channels=image_channels, kernel_size=2, stride=2),
#nn.ReLU(),
#nn.ConvTranspose2d(in_channels=8, out_channels=image_channels, kernel_size=2, stride=2),
#nn.ReLU(),
#nn.ConvTranspose2d(in_channels=16, out_channels=image_channels, kernel_size=3, stride=2),
nn.Sigmoid(),
)
self.fc1 = nn.Linear(h_dim, x_dim)
self.fc2 = nn.Linear(h_dim, x_dim)
self.fc3 = nn.Linear(x_dim, h_dim)
self.softplus= nn.Softplus()
# prior (0,I)
self.prior_var = nn.Parameter(torch.Tensor(1, x_dim).float().fill_(1.0))
self.prior_logvar = nn.Parameter(self.prior_var.log())
self.prior_var.requires_grad = False
self.prior_logvar.requires_grad = False
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return mu + eps*std
def encode(self, o_d):
h = self.encoder(o_d)
z, mu, logvar = self.bottleneck(h)
return z, mu, logvar
def decode(self, z):
z = self.fc3(z)
z = self.decoder(z)
return z
def bottleneck(self, h):
mu, logvar = self.fc1(h), self.fc2(h)
z = self.reparameterize(mu, logvar)
return z, mu, logvar
def forward(self, o_d):
z, mu, logvar = self.encode(o_d)
return self.decode(z), mu, logvar, z
# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(self, recon_o, o_d, en_mu, en_logvar, gmm_mu, gmm_var, iteration):
BCE = F.binary_cross_entropy(recon_o, o_d, reduction='sum')
beta = 1.0
# 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, batch_size, all_loader, model_dir, agent):
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(f"VAE_Agent {agent} Training Start({iteration}): Epoch:{epoch}, Batch_size:{batch_size}")
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:
loss = model.loss_function(recon_batch, data, mu, logvar, gmm_mu=None, gmm_var=None, iteration=iteration)
else:
loss = model.loss_function(recon_batch, data, mu, logvar, gmm_mu[batch_idx*batch_size:(batch_idx+1)*batch_size], gmm_var[batch_idx*batch_size:(batch_idx+1)*batch_size], iteration=iteration)
loss = loss.mean()
loss.backward()
train_loss += loss.item()
optimizer.step()
if i == 0 or (i+1) % 25 == 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
plt.figure()
plt.plot(range(0,epoch), loss_list, color="blue", label="ELBO")
if iteration!=0:
loss_0 = np.load(model_dir+'/npy/loss'+agent+'_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'+agent+'/vae_loss_'+str(iteration)+'.png')
plt.close()
np.save(model_dir+'/npy/loss'+agent+'_'+str(iteration)+'.npy', np.array(loss_list))
torch.save(model.state_dict(), model_dir+"/pth/vae"+agent+"_"+str(iteration)+".pth")
x_d, label = send_all_x(iteration=iteration, all_loader=all_loader, model_dir=model_dir, agent=agent)
# Send latent variable x_d to GMM
return x_d, label, loss_list
def decode(iteration, decode_k, sample_num, sample_d, manual, model_dir, agent):
print(f"Reconstruct image on Agent: {agent}, category: {decode_k}")
model = VAE().to("cpu")
model.load_state_dict(torch.load(str(model_dir)+"/pth/vae"+agent+"_"+str(iteration)+".pth",map_location=torch.device('cpu'))); model.eval()
mu_gmm_kd, lambda_gmm_kdd, pi_gmm_k = get_param(iteration, model_dir=model_dir, agent=agent)
sample_d = torch.from_numpy(sample_d.astype(np.float32)).clone()
with torch.no_grad():
sample_d = sample_d.to("cpu")
sample_d = model.decode(sample_d).cpu()
save_image(sample_d.view(sample_num, image_channels, image_size, image_size),model_dir+'/recon'+agent+'/random_'+str(decode_k)+'.png') if manual != True else save_image(sample_d.view(sample_num, image_channels, image_size, image_size),model_dir+'/recon'+agent+'/manual_'+str(decode_k)+'.png')
def plot_latent(iteration, all_loader, model_dir, agent):
print(f"Plot latent space on Agent: {agent}")
model = VAE().to("cpu")
model.load_state_dict(torch.load(model_dir+"/pth/vae"+agent+"_"+str(iteration)+".pth", map_location=torch.device('cpu')))
model.eval()
for batch_idx, (data, label) in enumerate(all_loader):
data = data.to("cpu")
recon_batch, mu, logvar, x_d = model(data)
x_d = x_d.cpu()
visualize_ls(iteration, x_d.detach().numpy(), label, model_dir,agent=agent)
break
def send_all_x(iteration, all_loader, model_dir, agent):
model = VAE().to(device)
model.load_state_dict(torch.load(model_dir+"/pth/vae"+agent+"_"+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()