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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="apple2orange", help="name of the dataset")
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0001, 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("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
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=256, help="size of image height")
parser.add_argument("--img_width", type=int, default=256, help="size of image width")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=100, help="interval between saving generator samples")
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between saving model checkpoints")
parser.add_argument("--n_downsample", type=int, default=2, help="number downsampling layers in encoder")
parser.add_argument("--dim", type=int, default=64, help="number of filters in first encoder layer")
opt = parser.parse_args()
print(opt)
cuda = True if torch.cuda.is_available() else False
# Create sample and checkpoint directories
os.makedirs("images/%s" % opt.dataset_name, exist_ok=True)
os.makedirs("saved_models/%s" % opt.dataset_name, exist_ok=True)
# Losses
criterion_GAN = torch.nn.MSELoss()
criterion_pixel = torch.nn.L1Loss()
input_shape = (opt.channels, opt.img_height, opt.img_width)
# Dimensionality (channel-wise) of image embedding
shared_dim = opt.dim * 2 ** opt.n_downsample
# Initialize generator and discriminator
shared_E = ResidualBlock(features=shared_dim)
E1 = Encoder(dim=opt.dim, n_downsample=opt.n_downsample, shared_block=shared_E)
E2 = Encoder(dim=opt.dim, n_downsample=opt.n_downsample, shared_block=shared_E)
shared_G = ResidualBlock(features=shared_dim)
G1 = Generator(dim=opt.dim, n_upsample=opt.n_downsample, shared_block=shared_G)
G2 = Generator(dim=opt.dim, n_upsample=opt.n_downsample, shared_block=shared_G)
D1 = Discriminator(input_shape)
D2 = Discriminator(input_shape)
if cuda:
E1 = E1.cuda()
E2 = E2.cuda()
G1 = G1.cuda()
G2 = G2.cuda()
D1 = D1.cuda()
D2 = D2.cuda()
criterion_GAN.cuda()
criterion_pixel.cuda()
if opt.epoch != 0:
# Load pretrained models
E1.load_state_dict(torch.load("saved_models/%s/E1_%d.pth" % (opt.dataset_name, opt.epoch)))
E2.load_state_dict(torch.load("saved_models/%s/E2_%d.pth" % (opt.dataset_name, opt.epoch)))
G1.load_state_dict(torch.load("saved_models/%s/G1_%d.pth" % (opt.dataset_name, opt.epoch)))
G2.load_state_dict(torch.load("saved_models/%s/G2_%d.pth" % (opt.dataset_name, opt.epoch)))
D1.load_state_dict(torch.load("saved_models/%s/D1_%d.pth" % (opt.dataset_name, opt.epoch)))
D2.load_state_dict(torch.load("saved_models/%s/D2_%d.pth" % (opt.dataset_name, opt.epoch)))
else:
# Initialize weights
E1.apply(weights_init_normal)
E2.apply(weights_init_normal)
G1.apply(weights_init_normal)
G2.apply(weights_init_normal)
D1.apply(weights_init_normal)
D2.apply(weights_init_normal)
# Loss weights
lambda_0 = 10 # GAN
lambda_1 = 0.1 # KL (encoded images)
lambda_2 = 100 # ID pixel-wise
lambda_3 = 0.1 # KL (encoded translated images)
lambda_4 = 100 # Cycle pixel-wise
# Optimizers
optimizer_G = torch.optim.Adam(
itertools.chain(E1.parameters(), E2.parameters(), G1.parameters(), G2.parameters()),
lr=opt.lr,
betas=(opt.b1, opt.b2),
)
optimizer_D1 = torch.optim.Adam(D1.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D2 = torch.optim.Adam(D2.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
# Learning rate update schedulers
lr_scheduler_G = torch.optim.lr_scheduler.LambdaLR(
optimizer_G, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
lr_scheduler_D1 = torch.optim.lr_scheduler.LambdaLR(
optimizer_D1, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
lr_scheduler_D2 = torch.optim.lr_scheduler.LambdaLR(
optimizer_D2, lr_lambda=LambdaLR(opt.n_epochs, opt.epoch, opt.decay_epoch).step
)
Tensor = torch.cuda.FloatTensor if cuda else torch.Tensor
# Image transformations
transforms_ = [
transforms.Resize(int(opt.img_height * 1.12), Image.BICUBIC),
transforms.RandomCrop((opt.img_height, opt.img_width)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
# Training data loader
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, unaligned=True),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
# Test data loader
val_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, unaligned=True, mode="test"),
batch_size=5,
shuffle=True,
num_workers=1,
)
def sample_images(batches_done):
"""Saves a generated sample from the test set"""
imgs = next(iter(val_dataloader))
X1 = Variable(imgs["A"].type(Tensor))
X2 = Variable(imgs["B"].type(Tensor))
_, Z1 = E1(X1)
_, Z2 = E2(X2)
fake_X1 = G1(Z2)
fake_X2 = G2(Z1)
img_sample = torch.cat((X1.data, fake_X2.data, X2.data, fake_X1.data), 0)
save_image(img_sample, "images/%s/%s.png" % (opt.dataset_name, batches_done), nrow=5, normalize=True)
def compute_kl(mu):
mu_2 = torch.pow(mu, 2)
loss = torch.mean(mu_2)
return loss
# ----------
# Training
# ----------
prev_time = time.time()
for epoch in range(opt.epoch, opt.n_epochs):
for i, batch in enumerate(dataloader):
# Set model input
X1 = Variable(batch["A"].type(Tensor))
X2 = Variable(batch["B"].type(Tensor))
# Adversarial ground truths
valid = Variable(Tensor(np.ones((X1.size(0), *D1.output_shape))), requires_grad=False)
fake = Variable(Tensor(np.zeros((X1.size(0), *D1.output_shape))), requires_grad=False)
# -------------------------------
# Train Encoders and Generators
# -------------------------------
optimizer_G.zero_grad()
# Get shared latent representation
mu1, Z1 = E1(X1)
mu2, Z2 = E2(X2)
# Reconstruct images
recon_X1 = G1(Z1)
recon_X2 = G2(Z2)
# Translate images
fake_X1 = G1(Z2)
fake_X2 = G2(Z1)
# Cycle translation
mu1_, Z1_ = E1(fake_X1)
mu2_, Z2_ = E2(fake_X2)
cycle_X1 = G1(Z2_)
cycle_X2 = G2(Z1_)
# Losses
loss_GAN_1 = lambda_0 * criterion_GAN(D1(fake_X1), valid)
loss_GAN_2 = lambda_0 * criterion_GAN(D2(fake_X2), valid)
loss_KL_1 = lambda_1 * compute_kl(mu1)
loss_KL_2 = lambda_1 * compute_kl(mu2)
loss_ID_1 = lambda_2 * criterion_pixel(recon_X1, X1)
loss_ID_2 = lambda_2 * criterion_pixel(recon_X2, X2)
loss_KL_1_ = lambda_3 * compute_kl(mu1_)
loss_KL_2_ = lambda_3 * compute_kl(mu2_)
loss_cyc_1 = lambda_4 * criterion_pixel(cycle_X1, X1)
loss_cyc_2 = lambda_4 * criterion_pixel(cycle_X2, X2)
# Total loss
loss_G = (
loss_KL_1
+ loss_KL_2
+ loss_ID_1
+ loss_ID_2
+ loss_GAN_1
+ loss_GAN_2
+ loss_KL_1_
+ loss_KL_2_
+ loss_cyc_1
+ loss_cyc_2
)
loss_G.backward()
optimizer_G.step()
# -----------------------
# Train Discriminator 1
# -----------------------
optimizer_D1.zero_grad()
loss_D1 = criterion_GAN(D1(X1), valid) + criterion_GAN(D1(fake_X1.detach()), fake)
loss_D1.backward()
optimizer_D1.step()
# -----------------------
# Train Discriminator 2
# -----------------------
optimizer_D2.zero_grad()
loss_D2 = criterion_GAN(D2(X2), valid) + criterion_GAN(D2(fake_X2.detach()), fake)
loss_D2.backward()
optimizer_D2.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 loss: %f] [G loss: %f] ETA: %s"
% (epoch, opt.n_epochs, i, len(dataloader), (loss_D1 + loss_D2).item(), loss_G.item(), time_left)
)
# If at sample interval save image
if batches_done % opt.sample_interval == 0:
sample_images(batches_done)
# Update learning rates
lr_scheduler_G.step()
lr_scheduler_D1.step()
lr_scheduler_D2.step()
if opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
# Save model checkpoints
torch.save(E1.state_dict(), "saved_models/%s/E1_%d.pth" % (opt.dataset_name, epoch))
torch.save(E2.state_dict(), "saved_models/%s/E2_%d.pth" % (opt.dataset_name, epoch))
torch.save(G1.state_dict(), "saved_models/%s/G1_%d.pth" % (opt.dataset_name, epoch))
torch.save(G2.state_dict(), "saved_models/%s/G2_%d.pth" % (opt.dataset_name, epoch))
torch.save(D1.state_dict(), "saved_models/%s/D1_%d.pth" % (opt.dataset_name, epoch))
torch.save(D2.state_dict(), "saved_models/%s/D2_%d.pth" % (opt.dataset_name, epoch))
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