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vae.py
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vae.py
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import numpy as np
import time
import torch
import torch.nn.functional as F
from torch.distributions.normal import Normal
from torchvision import datasets, transforms
from torchvision.utils import save_image
from modules import VAE
BATCH_SIZE = 32
N_EPOCHS = 100
PRINT_INTERVAL = 500
DATASET = 'FashionMNIST' # CIFAR10 | MNIST | FashionMNIST
NUM_WORKERS = 4
INPUT_DIM = 1
DIM = 256
Z_DIM = 128
LR = 1e-3
preproc_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_loader = torch.utils.data.DataLoader(
eval('datasets.'+DATASET)(
'../data/{}/'.format(DATASET), train=True, download=True,
transform=preproc_transform,
), batch_size=BATCH_SIZE, shuffle=False,
num_workers=NUM_WORKERS, pin_memory=True
)
test_loader = torch.utils.data.DataLoader(
eval('datasets.'+DATASET)(
'../data/{}/'.format(DATASET), train=False,
transform=preproc_transform
), batch_size=BATCH_SIZE, shuffle=False,
num_workers=NUM_WORKERS, pin_memory=True
)
model = VAE(INPUT_DIM, DIM, Z_DIM).cuda()
print(model)
opt = torch.optim.Adam(model.parameters(), lr=LR, amsgrad=True)
def train():
train_loss = []
model.train()
for batch_idx, (x, _) in enumerate(train_loader):
start_time = time.time()
x = x.cuda()
x_tilde, kl_d = model(x)
loss_recons = F.mse_loss(x_tilde, x, size_average=False) / x.size(0)
loss = loss_recons + kl_d
nll = -Normal(x_tilde, torch.ones_like(x_tilde)).log_prob(x)
log_px = nll.mean().item() - np.log(128) + kl_d.item()
log_px /= np.log(2)
opt.zero_grad()
loss.backward()
opt.step()
train_loss.append([log_px, loss.item()])
if (batch_idx + 1) % PRINT_INTERVAL == 0:
print('\tIter [{}/{} ({:.0f}%)]\tLoss: {} Time: {:5.3f} ms/batch'.format(
batch_idx * len(x), len(train_loader.dataset),
PRINT_INTERVAL * batch_idx / len(train_loader),
np.asarray(train_loss)[-PRINT_INTERVAL:].mean(0),
1000 * (time.time() - start_time)
))
def test():
start_time = time.time()
val_loss = []
model.eval()
with torch.no_grad():
for batch_idx, (x, _) in enumerate(test_loader):
x = x.cuda()
x_tilde, kl_d = model(x)
loss_recons = F.mse_loss(x_tilde, x, size_average=False) / x.size(0)
loss = loss_recons + kl_d
val_loss.append(loss.item())
print('\nValidation Completed!\tLoss: {:5.4f} Time: {:5.3f} s'.format(
np.asarray(val_loss).mean(0),
time.time() - start_time
))
return np.asarray(val_loss).mean(0)
def generate_reconstructions():
model.eval()
x, _ = test_loader.__iter__().next()
x = x[:32].cuda()
x_tilde, kl_div = model(x)
x_cat = torch.cat([x, x_tilde], 0)
images = (x_cat.cpu().data + 1) / 2
save_image(
images,
'samples/vae_reconstructions_{}.png'.format(DATASET),
nrow=8
)
def generate_samples():
model.eval()
z_e_x = torch.randn(64, Z_DIM, 1, 1).cuda()
x_tilde = model.decoder(z_e_x)
images = (x_tilde.cpu().data + 1) / 2
save_image(
images,
'samples/vae_samples_{}.png'.format(DATASET),
nrow=8
)
BEST_LOSS = 99999
LAST_SAVED = -1
for epoch in range(1, N_EPOCHS):
print("Epoch {}:".format(epoch))
train()
cur_loss = test()
if cur_loss <= BEST_LOSS:
BEST_LOSS = cur_loss
LAST_SAVED = epoch
print("Saving model!")
torch.save(model.state_dict(), 'models/{}_vae.pt'.format(DATASET))
else:
print("Not saving model! Last saved: {}".format(LAST_SAVED))
generate_reconstructions()
generate_samples()