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import argparse
import os
import numpy as np
import math
import itertools
import datetime
import time
import sys
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from models import *
from datasets import *
import torch.nn as nn
import torch.nn.functional as F
import torch
parser = argparse.ArgumentParser()
parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--dataset_name", type=str, default="edges2shoes", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=8, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_height", type=int, default=128, help="size of image height")
parser.add_argument("--img_width", type=int, default=128, help="size of image width")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--latent_dim", type=int, default=8, help="number of latent codes")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between saving generator samples")
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between model checkpoints")
parser.add_argument("--lambda_pixel", type=float, default=10, help="pixelwise loss weight")
parser.add_argument("--lambda_latent", type=float, default=0.5, help="latent loss weight")
parser.add_argument("--lambda_kl", type=float, default=0.01, help="kullback-leibler loss weight")
opt = parser.parse_args()
print(opt)
os.makedirs("images/%s" % opt.dataset_name, exist_ok=True)
os.makedirs("saved_models/%s" % opt.dataset_name, exist_ok=True)
cuda = True if torch.cuda.is_available() else False
input_shape = (opt.channels, opt.img_height, opt.img_width)
# Loss functions
mae_loss = torch.nn.L1Loss()
# Initialize generator, encoder and discriminators
generator = Generator(opt.latent_dim, input_shape)
encoder = Encoder(opt.latent_dim, input_shape)
D_VAE = MultiDiscriminator(input_shape)
D_LR = MultiDiscriminator(input_shape)
if cuda:
generator = generator.cuda()
encoder.cuda()
D_VAE = D_VAE.cuda()
D_LR = D_LR.cuda()
mae_loss.cuda()
if opt.epoch != 0:
# Load pretrained models
generator.load_state_dict(torch.load("saved_models/%s/generator_%d.pth" % (opt.dataset_name, opt.epoch)))
encoder.load_state_dict(torch.load("saved_models/%s/encoder_%d.pth" % (opt.dataset_name, opt.epoch)))
D_VAE.load_state_dict(torch.load("saved_models/%s/D_VAE_%d.pth" % (opt.dataset_name, opt.epoch)))
D_LR.load_state_dict(torch.load("saved_models/%s/D_LR_%d.pth" % (opt.dataset_name, opt.epoch)))
else:
# Initialize weights
generator.apply(weights_init_normal)
D_VAE.apply(weights_init_normal)
D_LR.apply(weights_init_normal)
# Optimizers
optimizer_E = torch.optim.Adam(encoder.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D_VAE = torch.optim.Adam(D_VAE.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D_LR = torch.optim.Adam(D_LR.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, input_shape),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
val_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, input_shape, mode="val"),
batch_size=8,
shuffle=True,
num_workers=1,
)
def sample_images(batches_done):
"""Saves a generated sample from the validation set"""
generator.eval()
imgs = next(iter(val_dataloader))
img_samples = None
for img_A, img_B in zip(imgs["A"], imgs["B"]):
# Repeat input image by number of desired columns
real_A = img_A.view(1, *img_A.shape).repeat(opt.latent_dim, 1, 1, 1)
real_A = Variable(real_A.type(Tensor))
# Sample latent representations
sampled_z = Variable(Tensor(np.random.normal(0, 1, (opt.latent_dim, opt.latent_dim))))
# Generate samples
fake_B = generator(real_A, sampled_z)
# Concatenate samples horisontally
fake_B = torch.cat([x for x in fake_B.data.cpu()], -1)
img_sample = torch.cat((img_A, fake_B), -1)
img_sample = img_sample.view(1, *img_sample.shape)
# Concatenate with previous samples vertically
img_samples = img_sample if img_samples is None else torch.cat((img_samples, img_sample), -2)
save_image(img_samples, "images/%s/%s.png" % (opt.dataset_name, batches_done), nrow=8, normalize=True)
generator.train()
def reparameterization(mu, logvar):
std = torch.exp(logvar / 2)
sampled_z = Variable(Tensor(np.random.normal(0, 1, (mu.size(0), opt.latent_dim))))
z = sampled_z * std + mu
return z
# ----------
# Training
# ----------
# Adversarial loss
valid = 1
fake = 0
prev_time = time.time()
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
# Set model input
real_A = Variable(batch["A"].type(Tensor))
real_B = Variable(batch["B"].type(Tensor))
# -------------------------------
# Train Generator and Encoder
# -------------------------------
optimizer_E.zero_grad()
optimizer_G.zero_grad()
# ----------
# cVAE-GAN
# ----------
# Produce output using encoding of B (cVAE-GAN)
mu, logvar = encoder(real_B)
encoded_z = reparameterization(mu, logvar)
fake_B = generator(real_A, encoded_z)
# Pixelwise loss of translated image by VAE
loss_pixel = mae_loss(fake_B, real_B)
# Kullback-Leibler divergence of encoded B
loss_kl = 0.5 * torch.sum(torch.exp(logvar) + mu ** 2 - logvar - 1)
# Adversarial loss
loss_VAE_GAN = D_VAE.compute_loss(fake_B, valid)
# ---------
# cLR-GAN
# ---------
# Produce output using sampled z (cLR-GAN)
sampled_z = Variable(Tensor(np.random.normal(0, 1, (real_A.size(0), opt.latent_dim))))
_fake_B = generator(real_A, sampled_z)
# cLR Loss: Adversarial loss
loss_LR_GAN = D_LR.compute_loss(_fake_B, valid)
# ----------------------------------
# Total Loss (Generator + Encoder)
# ----------------------------------
loss_GE = loss_VAE_GAN + loss_LR_GAN + opt.lambda_pixel * loss_pixel + opt.lambda_kl * loss_kl
loss_GE.backward(retain_graph=True)
optimizer_E.step()
# ---------------------
# Generator Only Loss
# ---------------------
# Latent L1 loss
_mu, _ = encoder(_fake_B)
loss_latent = opt.lambda_latent * mae_loss(_mu, sampled_z)
loss_latent.backward()
optimizer_G.step()
# ----------------------------------
# Train Discriminator (cVAE-GAN)
# ----------------------------------
optimizer_D_VAE.zero_grad()
loss_D_VAE = D_VAE.compute_loss(real_B, valid) + D_VAE.compute_loss(fake_B.detach(), fake)
loss_D_VAE.backward()
optimizer_D_VAE.step()
# ---------------------------------
# Train Discriminator (cLR-GAN)
# ---------------------------------
optimizer_D_LR.zero_grad()
loss_D_LR = D_LR.compute_loss(real_B, valid) + D_LR.compute_loss(_fake_B.detach(), fake)
loss_D_LR.backward()
optimizer_D_LR.step()
# --------------
# Log Progress
# --------------
# Determine approximate time left
batches_done = epoch * len(dataloader) + i
batches_left = opt.n_epochs * len(dataloader) - batches_done
time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
prev_time = time.time()
# Print log
sys.stdout.write(
"\r[Epoch %d/%d] [Batch %d/%d] [D VAE_loss: %f, LR_loss: %f] [G loss: %f, pixel: %f, kl: %f, latent: %f] ETA: %s"
% (
epoch,
opt.n_epochs,
i,
len(dataloader),
loss_D_VAE.item(),
loss_D_LR.item(),
loss_GE.item(),
loss_pixel.item(),
loss_kl.item(),
loss_latent.item(),
time_left,
)
)
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
torch.save(generator.state_dict(), "saved_models/%s/generator_%d.pth" % (opt.dataset_name, epoch))
torch.save(encoder.state_dict(), "saved_models/%s/encoder_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_VAE.state_dict(), "saved_models/%s/D_VAE_%d.pth" % (opt.dataset_name, epoch))
torch.save(D_LR.state_dict(), "saved_models/%s/D_LR_%d.pth" % (opt.dataset_name, epoch))
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