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fully_train_arch.py
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from __future__ import absolute_import, division, print_function
import cfg
import archs
import datasets
from network import train, validate, LinearLrDecay, load_params, copy_params
from utils.utils import set_log_dir, save_checkpoint, save_is_checkpoint, create_logger, count_parameters_in_MB
from utils.inception_score import _init_inception
from utils.fid_score import create_inception_graph, check_or_download_inception
from utils.flop_benchmark import print_FLOPs
from archs.fully_super_network import Generator, Discriminator
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
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
def main():
args = cfg.parse_args()
torch.cuda.manual_seed(args.random_seed)
# set visible GPU ids
if len(args.gpu_ids) > 0:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_ids
# set TensorFlow environment for evaluation (calculate IS and FID)
_init_inception()
inception_path = check_or_download_inception('./tmp/imagenet/')
create_inception_graph(inception_path)
# the first GPU in visible GPUs is dedicated for evaluation (running Inception model)
str_ids = args.gpu_ids.split(',')
args.gpu_ids = []
for id in range(len(str_ids)):
if id >= 0:
args.gpu_ids.append(id)
if len(args.gpu_ids) > 1:
args.gpu_ids = args.gpu_ids[1:]
else:
args.gpu_ids = args.gpu_ids
genotype_G = np.load(os.path.join('exps', 'best_G.npy'))
genotype_D = np.load(os.path.join('exps', args.genotypes_exp))
basemodel_gen = Generator(args, genotype_G)
gen_net = torch.nn.DataParallel(basemodel_gen, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
basemodel_dis = Discriminator(args, genotype_D)
dis_net = torch.nn.DataParallel(basemodel_dis, device_ids=args.gpu_ids).cuda(args.gpu_ids[0])
# 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)
# set up data_loader
dataset = datasets.ImageDataset(args)
train_loader = dataset.train
# epoch number for dis_net
args.max_epoch_D = args.max_epoch_G * args.n_critic
if args.max_iter_G:
args.max_epoch_D = np.ceil(args.max_iter_G * args.n_critic / len(train_loader))
max_iter_D = args.max_epoch_D * len(train_loader)
# 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, max_iter_D)
dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, max_iter_D)
# 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/stl10_train_unlabeled_fid_stats_48.npz'
else:
raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
assert os.path.exists(fid_stat)
# initial
gen_avg_param = copy_params(gen_net)
start_epoch = 0
best_fid = 1e4
best_is = 0.
# set writer
if args.checkpoint:
# resuming
print(f'=> resuming from {args.checkpoint}')
assert os.path.exists(os.path.join('exps', args.checkpoint))
checkpoint_file = os.path.join('exps', args.checkpoint, 'Model', 'checkpoint_best.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
assert args.exp_name
args.path_helper = set_log_dir('exps', args.exp_name)
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,
}
# model size
logger.info('Param size of G = %fMB', count_parameters_in_MB(gen_net))
logger.info('Param size of D = %fMB', count_parameters_in_MB(dis_net))
print_FLOPs(basemodel_gen, (1, args.latent_dim), logger)
print_FLOPs(basemodel_dis, (1, 3, args.img_size, args.img_size), logger)
# for visualization
if args.draw_arch:
from utils.genotype import draw_graph_G
draw_graph_G(genotype_G, save=True, file_path=os.path.join(args.path_helper['graph_vis_path'], 'latest_G'))
fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (100, args.latent_dim)))
# train loop
for epoch in tqdm(range(int(start_epoch), int(args.max_epoch_D)), desc='total progress'):
lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None
train(args, gen_net, dis_net, gen_optimizer, dis_optimizer,
gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers)
if epoch % args.val_freq == 0 and epoch >300:
backup_param = copy_params(gen_net)
load_params(gen_net, gen_avg_param)
inception_score, std, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict)
logger.info(f'Inception score mean: {inception_score}, Inception score std: {std}, '
f'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
if inception_score > best_is:
bset_is = inception_score
is_best_is = True
else:
is_best_is = False
else:
is_best = False
is_best_is = False
# save model
if epoch % args.val_freq == 0:
avg_gen_net = deepcopy(gen_net)
load_params(avg_gen_net, gen_avg_param)
save_checkpoint({
'epoch': epoch + 1,
'model': args.arch,
'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'])
save_is_checkpoint({
'epoch': epoch + 1,
'model': args.arch,
'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_is, args.path_helper['ckpt_path'])
del avg_gen_net
if __name__ == '__main__':
main()