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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=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=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("--sample_interval", type=int, default=400, help="interval 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("--n_residual", type=int, default=3, help="number of residual blocks in encoder / decoder")
parser.add_argument("--dim", type=int, default=64, help="number of filters in first encoder layer")
parser.add_argument("--style_dim", type=int, default=8, help="dimensionality of the style code")
opt = parser.parse_args()
print(opt)
cuda = torch.cuda.is_available()
# 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)
criterion_recon = torch.nn.L1Loss()
# Initialize encoders, generators and discriminators
Enc1 = Encoder(dim=opt.dim, n_downsample=opt.n_downsample, n_residual=opt.n_residual, style_dim=opt.style_dim)
Dec1 = Decoder(dim=opt.dim, n_upsample=opt.n_downsample, n_residual=opt.n_residual, style_dim=opt.style_dim)
Enc2 = Encoder(dim=opt.dim, n_downsample=opt.n_downsample, n_residual=opt.n_residual, style_dim=opt.style_dim)
Dec2 = Decoder(dim=opt.dim, n_upsample=opt.n_downsample, n_residual=opt.n_residual, style_dim=opt.style_dim)
D1 = MultiDiscriminator()
D2 = MultiDiscriminator()
if cuda:
Enc1 = Enc1.cuda()
Dec1 = Dec1.cuda()
Enc2 = Enc2.cuda()
Dec2 = Dec2.cuda()
D1 = D1.cuda()
D2 = D2.cuda()
criterion_recon.cuda()
if opt.epoch != 0:
# Load pretrained models
Enc1.load_state_dict(torch.load("saved_models/%s/Enc1_%d.pth" % (opt.dataset_name, opt.epoch)))
Dec1.load_state_dict(torch.load("saved_models/%s/Dec1_%d.pth" % (opt.dataset_name, opt.epoch)))
Enc2.load_state_dict(torch.load("saved_models/%s/Enc2_%d.pth" % (opt.dataset_name, opt.epoch)))
Dec2.load_state_dict(torch.load("saved_models/%s/Dec2_%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
Enc1.apply(weights_init_normal)
Dec1.apply(weights_init_normal)
Enc2.apply(weights_init_normal)
Dec2.apply(weights_init_normal)
D1.apply(weights_init_normal)
D2.apply(weights_init_normal)
# Loss weights
lambda_gan = 1
lambda_id = 10
lambda_style = 1
lambda_cont = 1
lambda_cyc = 0
# Optimizers
optimizer_G = torch.optim.Adam(
itertools.chain(Enc1.parameters(), Dec1.parameters(), Enc2.parameters(), Dec2.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
# Configure dataloaders
transforms_ = [
transforms.Resize((opt.img_height, opt.img_width), Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
val_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, mode="val"),
batch_size=5,
shuffle=True,
num_workers=1,
)
def sample_images(batches_done):
"""Saves a generated sample from the validation set"""
imgs = next(iter(val_dataloader))
img_samples = None
for img1, img2 in zip(imgs["A"], imgs["B"]):
# Create copies of image
X1 = img1.unsqueeze(0).repeat(opt.style_dim, 1, 1, 1)
X1 = Variable(X1.type(Tensor))
# Get random style codes
s_code = np.random.uniform(-1, 1, (opt.style_dim, opt.style_dim))
s_code = Variable(Tensor(s_code))
# Generate samples
c_code_1, _ = Enc1(X1)
X12 = Dec2(c_code_1, s_code)
# Concatenate samples horisontally
X12 = torch.cat([x for x in X12.data.cpu()], -1)
img_sample = torch.cat((img1, X12), -1).unsqueeze(0)
# 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=5, normalize=True)
# ----------
# Training
# ----------
# Adversarial ground truths
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
X1 = Variable(batch["A"].type(Tensor))
X2 = Variable(batch["B"].type(Tensor))
# Sampled style codes
style_1 = Variable(torch.randn(X1.size(0), opt.style_dim, 1, 1).type(Tensor))
style_2 = Variable(torch.randn(X1.size(0), opt.style_dim, 1, 1).type(Tensor))
# -------------------------------
# Train Encoders and Generators
# -------------------------------
optimizer_G.zero_grad()
# Get shared latent representation
c_code_1, s_code_1 = Enc1(X1)
c_code_2, s_code_2 = Enc2(X2)
# Reconstruct images
X11 = Dec1(c_code_1, s_code_1)
X22 = Dec2(c_code_2, s_code_2)
# Translate images
X21 = Dec1(c_code_2, style_1)
X12 = Dec2(c_code_1, style_2)
# Cycle translation
c_code_21, s_code_21 = Enc1(X21)
c_code_12, s_code_12 = Enc2(X12)
X121 = Dec1(c_code_12, s_code_1) if lambda_cyc > 0 else 0
X212 = Dec2(c_code_21, s_code_2) if lambda_cyc > 0 else 0
# Losses
loss_GAN_1 = lambda_gan * D1.compute_loss(X21, valid)
loss_GAN_2 = lambda_gan * D2.compute_loss(X12, valid)
loss_ID_1 = lambda_id * criterion_recon(X11, X1)
loss_ID_2 = lambda_id * criterion_recon(X22, X2)
loss_s_1 = lambda_style * criterion_recon(s_code_21, style_1)
loss_s_2 = lambda_style * criterion_recon(s_code_12, style_2)
loss_c_1 = lambda_cont * criterion_recon(c_code_12, c_code_1.detach())
loss_c_2 = lambda_cont * criterion_recon(c_code_21, c_code_2.detach())
loss_cyc_1 = lambda_cyc * criterion_recon(X121, X1) if lambda_cyc > 0 else 0
loss_cyc_2 = lambda_cyc * criterion_recon(X212, X2) if lambda_cyc > 0 else 0
# Total loss
loss_G = (
loss_GAN_1
+ loss_GAN_2
+ loss_ID_1
+ loss_ID_2
+ loss_s_1
+ loss_s_2
+ loss_c_1
+ loss_c_2
+ loss_cyc_1
+ loss_cyc_2
)
loss_G.backward()
optimizer_G.step()
# -----------------------
# Train Discriminator 1
# -----------------------
optimizer_D1.zero_grad()
loss_D1 = D1.compute_loss(X1, valid) + D1.compute_loss(X21.detach(), fake)
loss_D1.backward()
optimizer_D1.step()
# -----------------------
# Train Discriminator 2
# -----------------------
optimizer_D2.zero_grad()
loss_D2 = D2.compute_loss(X2, valid) + D2.compute_loss(X12.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(Enc1.state_dict(), "saved_models/%s/Enc1_%d.pth" % (opt.dataset_name, epoch))
torch.save(Dec1.state_dict(), "saved_models/%s/Dec1_%d.pth" % (opt.dataset_name, epoch))
torch.save(Enc2.state_dict(), "saved_models/%s/Enc2_%d.pth" % (opt.dataset_name, epoch))
torch.save(Dec2.state_dict(), "saved_models/%s/Dec2_%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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