241 lines
8.6 KiB
Python
241 lines
8.6 KiB
Python
import argparse
|
|
import os
|
|
import numpy as np
|
|
import math
|
|
import datetime
|
|
import time
|
|
|
|
from models import *
|
|
from datasets import *
|
|
from utils import *
|
|
|
|
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=0.0002, 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("--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 outputs")
|
|
parser.add_argument("--checkpoint_interval", type=int, default=-1, help="interval between saving model checkpoints")
|
|
parser.add_argument("--n_residual_blocks", type=int, default=9, help="number of residual blocks in generator")
|
|
parser.add_argument("--lambda_cyc", type=float, default=10.0, help="cycle loss weight")
|
|
parser.add_argument("--lambda_id", type=float, default=5.0, help="identity loss weight")
|
|
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_cycle = nn.L1Loss()
|
|
criterion_identity = nn.L1Loss()
|
|
|
|
input_shape = (opt.channels, opt.img_height, opt.img_width)
|
|
|
|
# Initialize generator and discriminator
|
|
G_AB = GeneratorResNet(input_shape, opt.n_residual_blocks)
|
|
G_BA = GeneratorResNet(input_shape, opt.n_residual_blocks)
|
|
D_A = Discriminator(input_shape)
|
|
D_B = Discriminator(input_shape)
|
|
|
|
# Optimizers
|
|
optimizer_G = nn.Adam(
|
|
G_AB.parameters() + G_BA.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)
|
|
)
|
|
optimizer_D_A = nn.Adam(D_A.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
optimizer_D_B = nn.Adam(D_B.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
|
|
|
|
# Buffers of previously generated samples
|
|
fake_A_buffer = ReplayBuffer()
|
|
fake_B_buffer = ReplayBuffer()
|
|
|
|
# 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]),
|
|
]
|
|
|
|
# Training data loader
|
|
dataloader = ImageDataset("../../data/%s" % opt.dataset_name, transform_=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, transform_=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))
|
|
G_AB.eval()
|
|
G_BA.eval()
|
|
|
|
|
|
real_A = imgs[0].stop_grad()
|
|
fake_B = G_AB(real_A)
|
|
real_B = imgs[1].stop_grad()
|
|
fake_A = G_BA(real_B)
|
|
# Arange images along x-axis
|
|
real_A_ = []
|
|
for i in range(5): real_A_.append(real_A.numpy()[i])
|
|
real_A = np.concatenate(real_A_, -1)[np.newaxis,:,:,:]
|
|
real_B_ = []
|
|
for i in range(5): real_B_.append(real_B.numpy()[i])
|
|
real_B = np.concatenate(real_B_, -1)[np.newaxis,:,:,:]
|
|
fake_A_ = []
|
|
for i in range(5): fake_A_.append(fake_A.numpy()[i])
|
|
fake_A = np.concatenate(fake_A_, -1)[np.newaxis,:,:,:]
|
|
fake_B_ = []
|
|
for i in range(5): fake_B_.append(fake_B.numpy()[i])
|
|
fake_B = np.concatenate(fake_B_, -1)[np.newaxis,:,:,:]
|
|
# Arange images along y-axis
|
|
image_grid = np.concatenate((real_A, fake_B, real_B, fake_A), 0)
|
|
save_image(image_grid, "images/%s/%s.png" % (opt.dataset_name, batches_done), 1)
|
|
|
|
|
|
# ----------
|
|
# Training
|
|
# ----------
|
|
|
|
prev_time = time.time()
|
|
for epoch in range(opt.epoch, opt.n_epochs):
|
|
for i, batch in enumerate(dataloader):
|
|
|
|
# Set model input
|
|
real_A = batch[0]
|
|
real_B = batch[1]
|
|
|
|
# Adversarial ground truths
|
|
valid = jt.array(np.ones((real_A.size(0), *D_A.output_shape))).float32().stop_grad()
|
|
fake = jt.array(np.zeros((real_A.size(0), *D_A.output_shape))).float32().stop_grad()
|
|
|
|
# ------------------
|
|
# Train Generators
|
|
# ------------------
|
|
|
|
G_AB.train()
|
|
G_BA.train()
|
|
|
|
# Identity loss
|
|
loss_id_A = criterion_identity(G_BA(real_A), real_A)
|
|
loss_id_B = criterion_identity(G_AB(real_B), real_B)
|
|
|
|
loss_identity = (loss_id_A + loss_id_B) / 2
|
|
|
|
# GAN loss
|
|
fake_B = G_AB(real_A)
|
|
loss_GAN_AB = criterion_GAN(D_B(fake_B), valid)
|
|
fake_A = G_BA(real_B)
|
|
loss_GAN_BA = criterion_GAN(D_A(fake_A), valid)
|
|
|
|
loss_GAN = (loss_GAN_AB + loss_GAN_BA) / 2
|
|
|
|
# Cycle loss
|
|
recov_A = G_BA(fake_B)
|
|
loss_cycle_A = criterion_cycle(recov_A, real_A)
|
|
recov_B = G_AB(fake_A)
|
|
loss_cycle_B = criterion_cycle(recov_B, real_B)
|
|
|
|
loss_cycle = (loss_cycle_A + loss_cycle_B) / 2
|
|
|
|
# Total loss
|
|
loss_G = loss_GAN + opt.lambda_cyc * loss_cycle + opt.lambda_id * loss_identity
|
|
|
|
optimizer_G.step(loss_G)
|
|
|
|
# -----------------------
|
|
# Train Discriminator A
|
|
# -----------------------
|
|
|
|
# Real loss
|
|
loss_real = criterion_GAN(D_A(real_A), valid)
|
|
# Fake loss (on batch of previously generated samples)
|
|
fake_A_ = fake_A_buffer.push_and_pop(fake_A)
|
|
loss_fake = criterion_GAN(D_A(fake_A_.stop_grad()), fake)
|
|
# Total loss
|
|
loss_D_A = (loss_real + loss_fake) / 2
|
|
|
|
optimizer_D_A.step(loss_D_A)
|
|
|
|
# -----------------------
|
|
# Train Discriminator B
|
|
# -----------------------
|
|
|
|
# Real loss
|
|
loss_real = criterion_GAN(D_B(real_B), valid)
|
|
# Fake loss (on batch of previously generated samples)
|
|
fake_B_ = fake_B_buffer.push_and_pop(fake_B)
|
|
fake_B_.sync()
|
|
loss_fake = criterion_GAN(D_B(fake_B_.stop_grad()), fake)
|
|
# Total loss
|
|
loss_D_B = (loss_real + loss_fake) / 2
|
|
|
|
optimizer_D_B.step(loss_D_B)
|
|
|
|
loss_D = (loss_D_A + loss_D_B) / 2
|
|
|
|
# --------------
|
|
# 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()
|
|
|
|
if i % 50 == 0:
|
|
# Print log
|
|
sys.stdout.write(
|
|
"\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, adv: %f, cycle: %f, identity: %f] ETA: %s"
|
|
% (
|
|
epoch,
|
|
opt.n_epochs,
|
|
i,
|
|
len(dataloader),
|
|
loss_D.data[0],
|
|
loss_G.data[0],
|
|
loss_GAN.data[0],
|
|
loss_cycle.data[0],
|
|
loss_identity.data[0],
|
|
time_left,
|
|
)
|
|
)
|
|
|
|
# If at sample interval save image
|
|
if batches_done % opt.sample_interval == 0:
|
|
sample_images(batches_done) |