def validate(args, fixed_z, fid_stat, epoch, gen_net: nn.Module, writer_dict, clean_dir=True):
writer = writer_dict['writer']
global_steps = writer_dict['valid_global_steps']
# eval mode
gen_net = gen_net.eval()
# generate images
# sample_imgs = gen_net(fixed_z, epoch)
# img_grid = make_grid(sample_imgs, nrow=5, normalize=True, scale_each=True)
# get fid and inception score
fid_buffer_dir = os.path.join(args.path_helper['sample_path'], 'fid_buffer')
os.makedirs(fid_buffer_dir, exist_ok=True)
eval_iter = args.num_eval_imgs // args.eval_batch_size
img_list = list()
logger.info('=> calculate fid score')
fid_score = get_fid(args, fid_stat, epoch, gen_net, args.num_eval_imgs, args.gen_batch_size*2, writer_dict=writer_dict, cls_idx=None)
# fid_score = calculate_fid_given_paths([fid_buffer_dir, fid_stat], inception_path=None)
# fid_score = 10000
print(f"FID score: {fid_score}")
# writer.add_image('sampled_images', img_grid, global_steps)
writer.add_scalar('FID_score', fid_score, global_steps)
writer_dict['valid_global_steps'] = global_steps + 1
return fid_score
def validate(args,
fixed_z,
fid_stat,
epoch,
gen_net: nn.Module,
writer_dict,
clean_dir=True):
writer = writer_dict['writer']
global_steps = writer_dict['valid_global_steps']
# eval mode
gen_net = gen_net.eval()
# generate images
sample_imgs = gen_net(fixed_z, epoch)
img_grid = make_grid(sample_imgs, nrow=5, normalize=True, scale_each=True)
# get fid and inception score
fid_buffer_dir = os.path.join(args.path_helper['sample_path'],
'fid_buffer')
os.makedirs(fid_buffer_dir, exist_ok=True)
eval_iter = args.num_eval_imgs // args.eval_batch_size
img_list = list()
for iter_idx in tqdm(range(eval_iter), desc='sample images'):
z = torch.cuda.FloatTensor(
np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim)))
# Generate a batch of images
gen_imgs = gen_net(z, epoch).mul_(127.5).add_(127.5).clamp_(
0.0, 255.0).permute(0, 2, 3, 1).to('cpu', torch.uint8).numpy()
for img_idx, img in enumerate(gen_imgs):
file_name = os.path.join(fid_buffer_dir,
f'iter{iter_idx}_b{img_idx}.png')
imsave(file_name, img)
img_list.extend(list(gen_imgs))
# get inception score
logger.info('=> calculate inception score')
mean, std = get_inception_score(img_list)
print(f"Inception score: {mean}")
# get fid score
logger.info('=> calculate fid score')
fid_score = get_fid(args,
fid_stat,
epoch,
gen_net,
args.num_eval_imgs,
args.gen_batch_size * 2,
writer_dict=writer_dict,
cls_idx=None)
print(f"FID score: {fid_score}")
if clean_dir:
os.system('rm -r {}'.format(fid_buffer_dir))
else:
logger.info(f'=> sampled images are saved to {fid_buffer_dir}')
# print('first')
writer.add_image('sampled_images', img_grid, global_steps)
writer.add_scalar('Inception_score/mean', mean, global_steps)
writer.add_scalar('Inception_score/std', std, global_steps)
writer.add_scalar('FID_score', fid_score, global_steps)
# print('second')
writer_dict['valid_global_steps'] = global_steps + 1
# print('third')
return mean, fid_score