247 lines
8.6 KiB
Python
247 lines
8.6 KiB
Python
import argparse
|
|
import os
|
|
import numpy as np
|
|
import math
|
|
import datetime
|
|
import time
|
|
import sys
|
|
import jittor.transform as transform
|
|
from models import *
|
|
from datasets import *
|
|
import jittor as jt
|
|
|
|
jt.flags.use_cuda = 1
|
|
|
|
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="monet2photo", 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)
|
|
|
|
# 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 = nn.MSELoss()
|
|
criterion_pixel = 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)
|
|
|
|
# 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 = nn.Adam(
|
|
E1.parameters() + E2.parameters() + G1.parameters() + G2.parameters(),
|
|
lr=opt.lr,
|
|
betas=(opt.b1, opt.b2),
|
|
)
|
|
optimizer_D1 = nn.Adam(D1.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
optimizer_D2 = nn.Adam(D2.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
|
|
# Image transformations
|
|
transform_ = [
|
|
transform.Resize(int(opt.img_height * 1.12), Image.BICUBIC),
|
|
transform.RandomCrop((opt.img_height, opt.img_width)),
|
|
transform.RandomHorizontalFlip(),
|
|
transform.ImageNormalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
|
|
]
|
|
|
|
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True).set_attrs(batch_size=opt.batch_size, shuffle=True, num_workers=opt.n_cpu)
|
|
|
|
val_dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transform_, unaligned=True, mode="test").set_attrs(batch_size=5, shuffle=True, num_workers=1)
|
|
|
|
import cv2
|
|
|
|
def save_image(img, path, nrow=10, padding=5):
|
|
N,C,W,H = img.shape
|
|
if (N%nrow!=0):
|
|
print("N%nrow!=0")
|
|
return
|
|
ncol=int(N/nrow)
|
|
img_all = []
|
|
for i in range(ncol):
|
|
img_ = []
|
|
for j in range(nrow):
|
|
img_.append(img[i*nrow+j])
|
|
img_.append(np.zeros((C,W,padding)))
|
|
img_all.append(np.concatenate(img_, 2))
|
|
img_all.append(np.zeros((C,padding,img_all[0].shape[2])))
|
|
img = np.concatenate(img_all, 1)
|
|
img = np.concatenate([np.zeros((C,padding,img.shape[2])), img], 1)
|
|
img = np.concatenate([np.zeros((C,img.shape[1],padding)), img], 2)
|
|
min_=img.min()
|
|
max_=img.max()
|
|
img=(img-min_)/(max_-min_)*255
|
|
img=img.transpose((1,2,0))
|
|
if C==3:
|
|
img = img[:,:,::-1]
|
|
cv2.imwrite(path,img)
|
|
|
|
def sample_images(batches_done):
|
|
"""Saves a generated sample from the test set"""
|
|
imgs = next(iter(val_dataloader))
|
|
X1 = imgs[0].stop_grad()
|
|
X2 = imgs[1].stop_grad()
|
|
E1.eval()
|
|
E2.eval()
|
|
G1.eval()
|
|
G2.eval()
|
|
_, Z1 = E1(X1)
|
|
_, Z2 = E2(X2)
|
|
fake_X1 = G1(Z2)
|
|
fake_X2 = G2(Z1)
|
|
img_sample = jt.contrib.concat((X1, fake_X2, X2, fake_X1), 0)
|
|
save_image(img_sample.numpy(), "images/%s/%s.png" % (opt.dataset_name, batches_done), nrow=5)
|
|
|
|
def compute_kl(mu):
|
|
mu_2 = mu.sqr()
|
|
loss = jt.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 = batch[0].stop_grad()
|
|
X2 = batch[1].stop_grad()
|
|
|
|
# Adversarial ground truths
|
|
valid = jt.ones((X1.size(0), *D1.output_shape)).stop_grad()
|
|
fake = jt.zeros((X1.size(0), *D1.output_shape)).stop_grad()
|
|
# -------------------------------
|
|
# Train Encoders and Generators
|
|
# -------------------------------
|
|
|
|
E1.train()
|
|
E2.train()
|
|
G1.train()
|
|
G2.train()
|
|
|
|
# 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.sync()
|
|
optimizer_G.step(loss_G)
|
|
|
|
# -----------------------
|
|
# Train Discriminator 1
|
|
# -----------------------
|
|
|
|
|
|
loss_D1 = criterion_GAN(D1(X1), valid) + criterion_GAN(D1(fake_X1.detach()), fake)
|
|
|
|
loss_D1.sync()
|
|
optimizer_D1.step(loss_D1)
|
|
|
|
# -----------------------
|
|
# Train Discriminator 2
|
|
# -----------------------
|
|
|
|
|
|
loss_D2 = criterion_GAN(D2(X2), valid) + criterion_GAN(D2(fake_X2.detach()), fake)
|
|
|
|
loss_D2.sync()
|
|
optimizer_D2.step(loss_D2)
|
|
|
|
# --------------
|
|
# 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).data[0], loss_G.data[0], time_left)
|
|
)
|
|
|
|
# If at sample interval save image
|
|
if batches_done % opt.sample_interval == 0:
|
|
sample_images(batches_done)
|
|
|
|
if epoch >= opt.decay_epoch:
|
|
optimizer_G.lr = opt.lr * (opt.n_epochs - epoch - 1) / (opt.n_epochs - opt.decay_epoch)
|
|
optimizer_D1.lr = opt.lr * (opt.n_epochs - epoch - 1) / (opt.n_epochs - opt.decay_epoch)
|
|
optimizer_D2.lr = opt.lr * (opt.n_epochs - epoch - 1) / (opt.n_epochs - opt.decay_epoch)
|