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cluster_gan_train.py
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cluster_gan_train.py
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import torch
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST
import torchvision.transforms as transforms
from torchvision.utils import make_grid, save_image
from itertools import chain as ichain
from tqdm import tqdm
import numpy as np
from matplotlib import pyplot as plt
import imageio
from cluster_gan_model import Generator, Encoder, Discriminator
def sample_z(shape=100, z_dim=30, n_class=10, device="cuda", display=False):
z_n = torch.randn(shape, z_dim).to(device)
z_c_val = torch.empty(shape, dtype=torch.long).random_(n_class).to(device)
if display :
z_c_val = torch.tensor(list(range(10))*10).to(device)
z_c = torch.zeros(shape, n_class, device=device).scatter_(1, z_c_val.unsqueeze(1), 1.)
return z_n, z_c, z_c_val
def final_results(imgs, losses):
# save imgs as gif
imgs = [np.array(transforms.ToPILImage()(img)) for img in imgs]
imageio.mimsave('cluster_outputs/generator_results.gif', imgs)
# show trend of losses
plt.figure(1)
plt.plot(losses[0], label='Generator-Encoder loss')
plt.plot(losses[1], label='Discriminator Loss')
plt.legend()
plt.savefig('cluster_outputs/loss.png')
plt.figure(2)
plt.plot(losses[2], label='enc_mse_loss')
plt.legend()
plt.savefig('cluster_outputs/enc_mse_loss.png')
plt.figure(3)
plt.plot(losses[3], label='enc_cross_entropy_loss')
plt.legend()
plt.savefig('cluster_outputs/enc_cross_entropy_loss.png')
n_epochs = 200
batch_size = 100
lr = 0.0001
k = 5
img_size = 28
z_dim = 30
n_class = 10
beta_n = 10
beta_c = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
generator = Generator(z_dim, n_class).to(device)
encoder = Encoder(z_dim, n_class).to(device)
discriminator = Discriminator().to(device)
bce_loss = torch.nn.BCELoss()
ce_loss = torch.nn.CrossEntropyLoss()
mse_loss = torch.nn.MSELoss()
training_data = FashionMNIST(
root="data/",
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor()]
)
)
dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)
ge_chain = ichain(generator.parameters(), encoder.parameters())
optimizer_GE = torch.optim.Adam(
ge_chain, lr=lr, betas=(0.5, 0.9), weight_decay=2.5*1e-5)
optimizer_D = torch.optim.Adam(
discriminator.parameters(), lr=lr, betas=(0.5, 0.9))
losses = [[], [], [], []] # ge_losses, d_losses
imgs = []
sample_for_display = sample_z(shape=100, z_dim=z_dim, n_class=n_class, device=device)
epochs = tqdm(range(n_epochs))
for epoch in epochs:
epoch_ge_loss, epoch_d_loss = 0, 0
for b_i, data in enumerate(dataloader):
generator.train()
encoder.train()
image, _ = data # only need the image
real_imgs = image.to(device)
z_n, z_c, z_c_val = sample_z(shape=real_imgs.shape[0], z_dim=z_dim, n_class=n_class, device=device)
# G & E
if b_i % k == 0:
optimizer_GE.zero_grad()
fake_imgs = generator(torch.cat((z_n, z_c), 1))
D_fake = discriminator(fake_imgs)
enc_z_n, enc_z_class, enc_z_class_raw = encoder(fake_imgs)
zn_loss = mse_loss(enc_z_n, z_n)
zc_loss = ce_loss(enc_z_class_raw, z_c_val)
d_loss = bce_loss(D_fake, torch.ones(
D_fake.size(0), 1, device=device))
ge_loss = d_loss + beta_n * zn_loss + beta_c * zc_loss
ge_loss.backward(retain_graph=True)
optimizer_GE.step()
epoch_ge_loss += ge_loss.item()
# D
optimizer_D.zero_grad()
fake_imgs = generator(torch.cat((z_n, z_c), 1))
D_fake = discriminator(fake_imgs)
D_real = discriminator(real_imgs)
real_loss = bce_loss(D_real, torch.ones(
D_real.size(0), 1, device=device))
fake_loss = bce_loss(D_fake, torch.zeros(
D_real.size(0), 1, device=device))
real_loss.backward()
fake_loss.backward()
optimizer_D.step()
epoch_d_loss += (real_loss + fake_loss).item()
epoch_ge_loss = epoch_ge_loss / len(dataloader)
epoch_d_loss = epoch_d_loss / len(dataloader)
losses[0].append(epoch_ge_loss)
losses[1].append(epoch_d_loss)
epochs.set_description("epoch_ge_loss: {:.5f}, epoch_d_loss: {:.5f}".format(epoch_ge_loss, epoch_d_loss))
generator.eval()
encoder.eval()
if epoch % 10 == 0: # save imgs for every 10 epoch
generated_img = generator(torch.cat((sample_for_display[0], sample_for_display[1]), 1)).cpu().detach()
generated_img = make_grid(generated_img)
save_image(generated_img, "cluster_outputs/img_{}.png".format(epoch))
imgs.append(generated_img)
n_samp = 100
zn_samp, zc_samp, zc_samp_val = sample_z(shape=100, z_dim=z_dim, n_class=n_class, device=device)
x_samp = generator(torch.cat((zn_samp, zc_samp), 1))
zn_enc, zc_enc, zc_enc_val = encoder(x_samp)
lat_mse_loss = mse_loss(zn_enc, zn_samp)
lat_ce_loss = ce_loss(zc_enc_val, zc_samp_val)
losses[2].append(lat_mse_loss.item())
losses[3].append(lat_ce_loss.item())
final_results(imgs, losses)