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train.py
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#!/usr/bin/env python
import argparse
import os
import chainer
from chainer import training
from chainer import cuda, serializers
from chainer.training import extension
from chainer.training import extensions
import sys
import common.net as net
import datasets
from updater import *
from evaluation import *
def main():
parser = argparse.ArgumentParser(
description='Train CycleGAN')
parser.add_argument('--batch_size', '-b', type=int, default=1)
parser.add_argument('--max_iter', '-m', type=int, default=120000)
parser.add_argument('--gpu', '-g', type=int, default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--eval_folder', '-e', default='test',
help='Directory to output the evaluation result')
parser.add_argument('--eval_interval', type=int, default=100,
help='Interval of evaluating generator')
parser.add_argument("--learning_rate_g", type=float, default=0.0002, help="Learning rate for generator")
parser.add_argument("--learning_rate_d", type=float, default=0.0002, help="Learning rate for discriminator")
parser.add_argument("--load_gen_f_model", default='', help='load generator model')
parser.add_argument("--load_gen_g_model", default='', help='load generator model')
parser.add_argument("--load_dis_x_model", default='', help='load discriminator model')
parser.add_argument("--load_dis_y_model", default='', help='load discriminator model')
parser.add_argument('--gen_class', default='Generator_ResBlock_9', help='Default generator class')
parser.add_argument('--dis_class', default='Discriminator', help='Default discriminator class')
parser.add_argument("--lambda1", type=float, default=10.0, help='lambda for reconstruction loss')
parser.add_argument("--lambda2", type=float, default=3.0, help='lambda for adversarial loss')
parser.add_argument("--flip", type=int, default=1, help='flip images for data augmentation')
parser.add_argument("--resize_to", type=int, default=256, help='resize the image to')
parser.add_argument("--crop_to", type=int, default=256, help='crop the resized image to')
parser.add_argument("--load_dataset", default='silverhair_train', help='load dataset')
parser.add_argument("--discriminator_layer_n", type=int, default=5, help='number of discriminator layers')
parser.add_argument("--learning_rate_anneal", type=float, default=0, help='anneal the learning rate')
parser.add_argument("--learning_rate_anneal_interval", type=int, default=1000, help='time to anneal the learning')
args = parser.parse_args()
print(args)
max_iter = args.max_iter
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
gen_g = getattr(net, args.gen_class)()
dis_x = getattr(net, args.dis_class)()
gen_f = getattr(net, args.gen_class)()
dis_y = getattr(net, args.dis_class)()
if args.load_gen_g_model != '':
serializers.load_npz(args.load_gen_g_model, gen_g)
print("Generator G(X->Y) model loaded")
if args.load_gen_f_model != '':
serializers.load_npz(args.load_gen_f_model, gen_f)
print("Generator F(Y->X) model loaded")
if args.load_dis_x_model != '':
serializers.load_npz(args.load_dis_x_model, dis_x)
print("Discriminator X model loaded")
if args.load_dis_y_model != '':
serializers.load_npz(args.load_dis_y_model, dis_y)
print("Discriminator Y model loaded")
if not os.path.exists(args.eval_folder):
os.makedirs(args.eval_folder)
# select GPU
if args.gpu >= 0:
gen_g.to_gpu()
gen_f.to_gpu()
dis_x.to_gpu()
dis_y.to_gpu()
print("use gpu {}".format(args.gpu))
# Setup an optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
return optimizer
opt_g=make_optimizer(gen_g, alpha=args.learning_rate_g)
opt_f=make_optimizer(gen_f, alpha=args.learning_rate_g)
opt_x=make_optimizer(dis_x, alpha=args.learning_rate_d)
opt_y=make_optimizer(dis_y, alpha=args.learning_rate_d)
train_dataset = getattr(datasets, args.load_dataset)(flip=args.flip, resize_to=args.resize_to, crop_to=args.crop_to)
train_iter = chainer.iterators.MultiprocessIterator(
train_dataset, args.batch_size, n_processes=4)
#train2_iter = chainer.iterators.MultiprocessIterator(
# train_dataset, args.batchsize, n_processes=4)
#test_dataset = horse2zebra_Dataset_train(flip=args.flip, resize_to=args.resize_to, crop_to=args.crop_to)
test_iter = chainer.iterators.SerialIterator(train_dataset, 4)
# Set up a trainer
updater = Updater(
models=(gen_g, gen_f, dis_x, dis_y),
iterator={
'main': train_iter,
#'dis' : train2_iter,
'test': test_iter
},
optimizer={
'gen_g': opt_g,
'gen_f': opt_f,
'dis_x': opt_x,
'dis_y': opt_y
},
device=args.gpu,
params={
'lambda1': args.lambda1,
'lambda2': args.lambda2,
'image_size' : args.crop_to,
'eval_folder' : args.eval_folder,
'learning_rate_anneal' : args.learning_rate_anneal,
'learning_rate_anneal_interval' : args.learning_rate_anneal_interval,
'dataset' : train_dataset
})
model_save_interval = (4000, 'iteration')
trainer = training.Trainer(updater, (max_iter, 'iteration'), out=args.out)
trainer.extend(extensions.snapshot_object(
gen_g, 'gen_g{.updater.iteration}.npz'), trigger=model_save_interval)
trainer.extend(extensions.snapshot_object(
gen_f, 'gen_f{.updater.iteration}.npz'), trigger=model_save_interval)
trainer.extend(extensions.snapshot_object(
dis_x, 'dis_x{.updater.iteration}.npz'), trigger=model_save_interval)
trainer.extend(extensions.snapshot_object(
dis_y, 'dis_y{.updater.iteration}.npz'), trigger=model_save_interval)
log_keys = ['epoch', 'iteration', 'gen_g/loss_rec', 'gen_f/loss_rec', 'gen_g/loss_adv', 'gen_f/loss_adv', 'dis_x/loss', 'dis_y/loss']
trainer.extend(extensions.LogReport(keys=log_keys, trigger=(20, 'iteration')))
trainer.extend(extensions.PrintReport(log_keys), trigger=(20, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=50))
#trainer.extend(
# evaluation(gen_g, gen_f, args.eval_folder, image_size=args.crop_to
# ), trigger=(args.eval_interval ,'iteration')
#)
# Run the training
trainer.run()
if __name__ == '__main__':
main()