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trainsw.py
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trainsw.py
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# -*- coding: utf-8 -*-
# @Date : 2019-07-25
# @Author : Xinyu Gong (xy_gong@tamu.edu)
# @Link : None
# @Version : 0.0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cfg
import models
import datasets
from functions import train_hsw, validate, LinearLrDecay, load_params, copy_params
from utils.utils import set_log_dir, save_checkpoint, create_logger
from utils.inception_score import _init_inception
from utils.fid_score import create_inception_graph, check_or_download_inception
import torch
import os
import numpy as np
import torch.nn as nn
from tensorboardX import SummaryWriter
from tqdm import tqdm
from copy import deepcopy
import numpy as np
from slicers import Hierarhcial_Slicer
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set tf env
_init_inception()
inception_path = check_or_download_inception(None)
create_inception_graph(inception_path)
# import network
gen_net = eval('models.'+args.model+'.Generator')(args=args).cuda()
dis_net = eval('models.'+args.model+'.Discriminator')(args=args).cuda()
# weight init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
if args.init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif args.init_type == 'orth':
nn.init.orthogonal_(m.weight.data)
elif args.init_type == 'xavier_uniform':
nn.init.xavier_uniform(m.weight.data, 1.)
else:
raise NotImplementedError('{} unknown inital type'.format(args.init_type))
elif classname.find('BatchNorm2d') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0.0)
gen_net.apply(weights_init)
dis_net.apply(weights_init)
if (args.dataset.lower() == 'stl10'):
feat_size = 4608
elif (args.dataset.lower() == 'celebahq'):
feat_size = 8192
else:
feat_size = 8192
Ls=args.Ls
Ls = [int(L) for L in Ls.split(',')]
slicer = Hierarhcial_Slicer(d=feat_size, Ls=Ls).cuda()
slicer.eval()
# set optimizer
gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()),
args.g_lr, (args.beta1, args.beta2))
dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()),
args.d_lr, (args.beta1, args.beta2))
gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# fid stat
if args.dataset.lower() == 'cifar10':
fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
elif args.dataset.lower() == 'stl10':
fid_stat = 'fid_stat/fid_stats_stl10.npz'
elif args.dataset.lower() == 'celeba':
fid_stat = 'fid_stat/fid_stats_celeba.npz'
elif args.dataset.lower() == 'celebahq':
fid_stat = 'fid_stat/fid_stats_celebahq.npz'
elif args.dataset.lower() == 'lsun_church':
fid_stat = 'fid_stat/fid_stats_lsun_church.npz'
elif args.dataset.lower() == 'tinyimagenet':
fid_stat = 'fid_stat/fid_stats_tinyimagenet.npz'
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# epoch number for dis_net
args.max_epoch = args.max_epoch * args.n_critic
if args.max_iter:
args.max_epoch = np.ceil(args.max_iter * args.n_critic / len(train_loader))
# initial
if(args.dataset.lower() == 'lsun_church' or 'celebahq'):
fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))
else:
fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))
gen_avg_param = copy_params(gen_net)
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
# set writer
if args.load_path:
print(f'=> resuming from {args.load_path}')
assert os.path.exists(args.load_path)
checkpoint_file = os.path.join(args.load_path, 'Model', 'checkpoint.pth')
assert os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file)
start_epoch = checkpoint['epoch']
best_fid = checkpoint['best_fid']
gen_net.load_state_dict(checkpoint['gen_state_dict'])
dis_net.load_state_dict(checkpoint['dis_state_dict'])
gen_optimizer.load_state_dict(checkpoint['gen_optimizer'])
dis_optimizer.load_state_dict(checkpoint['dis_optimizer'])
avg_gen_net = deepcopy(gen_net)
avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
gen_avg_param = copy_params(avg_gen_net)
del avg_gen_net
args.path_helper = checkpoint['path_helper']
logger = create_logger(args.path_helper['log_path'])
logger.info(f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})')
else:
# create new log dir
args.path_helper = set_log_dir(args.dataset.lower()+'_logs', 'sw_L{}'.format(args.Ls))
logger = create_logger(args.path_helper['log_path'])
logger.info(args)
writer_dict = {
'writer': SummaryWriter(args.path_helper['log_path']),
'train_global_steps': start_epoch * len(train_loader),
'valid_global_steps': start_epoch // args.val_freq,
}
# train loop
fids=[]
iscores=[]
lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)), desc='total progress'):
train_hsw(args, gen_net, dis_net, slicer, gen_optimizer, dis_optimizer, gen_avg_param, train_loader, epoch, writer_dict,
lr_schedulers)
if epoch % args.val_freq == 0 or epoch == int(args.max_epoch)-1:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict)
iscores.append(inception_score)
fids.append(fid_score)
np.savetxt(args.path_helper['log_path'] + "/fid.csv", np.array(fids), delimiter=",")
np.savetxt(args.path_helper['log_path'] + "/is.csv", np.array(iscores), delimiter=",")
logger.info(f'Inception score: {inception_score}, FID score: {fid_score} || @ epoch {epoch}.')
load_params(gen_net, backup_param)
if fid_score < best_fid:
best_fid = fid_score
is_best = True
else:
is_best = False
else:
is_best = False
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint({
'epoch': epoch + 1,
'model': args.model,
'gen_state_dict': gen_net.state_dict(),
'dis_state_dict': dis_net.state_dict(),
'avg_gen_state_dict': avg_gen_net.state_dict(),
'gen_optimizer': gen_optimizer.state_dict(),
'dis_optimizer': dis_optimizer.state_dict(),
'best_fid': best_fid,
'path_helper': args.path_helper
}, is_best, args.path_helper['ckpt_path'])
del avg_gen_net
if __name__ == '__main__':
main()