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test.py
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''' Test Codes '''
import torch
from torch.utils.data.dataloader import DataLoader
import os
from utils.imtools import imshow
from utils.metrics import aver_bmp_psnr_ssim_par, aver_bmp_psnr
from config import get_test_config
from model.net import kernel_error_model
from data_loader.dataset import Test_Lai_NoiseKernel, Test_Real_NoiseKernel, Test_NoiseKernel
class Tester():
def __init__(self, args, net, test_dset, parallel = True):
self.args = args
self.net = net
self.test_DLoader = {}
self.parallel = parallel
for key in test_dset.keys():
self.test_DLoader[key] = DataLoader(test_dset[key], batch_size=1, shuffle=False,
num_workers=0, pin_memory=True)
self.load_model()
self.test_input = {}
def __call__(self):
if self.args.save_img:
for key in self.test_DLoader.keys():
os.mkdir(self.args.test_save_dir + self.args.dataset_name + '_' + key + '/')
if self.args.dataset_name == 'Lai':
for key in self.test_DLoader.keys():
self.test_lai(key, parallel=self.parallel, input=False)
elif self.args.dataset_name == 'Lai_Real':
for key in self.test_DLoader.keys():
self.test_lai_real(key)
else:
for key in self.test_DLoader.keys():
self.test(key, parallel=self.parallel, input=False)
def test(self, name, parallel, input = False):
if input:
if name not in self.test_input.keys():
bat_x = []
bat_y = []
for i, bat in enumerate(self.test_DLoader[name]):
bat_x.append(bat['bl'])
bat_y.append(bat['sp'])
if parallel:
PSNR = aver_bmp_psnr_ssim_par(bat_x,bat_y, bd_cut=self.args.bd_cut, to_int = True)
else:
PSNR = aver_bmp_psnr(bat_x,bat_y, cut=self.args.bd_cut, to_int=True)
self.test_input[name] = PSNR
bat_y = []
bat_opt = []
for i, bat in enumerate(self.test_DLoader[name]):
bat_y.append(bat['sp'])
opt_db = self.eval_net(bat['bl'].cuda(), bat['Fker'].cuda())
bat_opt.append(opt_db[-1].cpu())
if self.args.save_img:
imshow(opt_db[-1].cpu(),dir=self.args.test_save_dir + self.args.dataset_name + '_' + name + '/', str = bat['name'][0])
if self.args.compute_metrics:
if parallel:
PSNR, SSIM = aver_bmp_psnr_ssim_par(bat_opt,bat_y, bd_cut=self.args.bd_cut, to_int = True, ssim_compute=True)
else:
PSNR = aver_bmp_psnr(bat_opt,bat_y, cut=self.args.bd_cut, to_int=True)
if input:
print(['%s: In %2.3f, Out %2.3f' % (name, self.test_input[name], PSNR)])
else:
print(['%s: Out PSNR %2.3f, SSIM %2.3f' % (name, PSNR, SSIM)])
def test_lai(self, name, parallel,input = False):
if input:
if name not in self.test_input.keys():
bat_x = []
bat_y = []
for i, bat in enumerate(self.test_DLoader[name]):
bat_x.append(bat['bl'])
bat_y.append(bat['sp'])
if parallel:
PSNR = aver_bmp_psnr_ssim_par(bat_x,bat_y, bd_cut=self.args.bd_cut, to_int = True, ssim_compute=False)
else:
PSNR = aver_bmp_psnr(bat_x,bat_y, cut=self.args.bd_cut, to_int=True)
self.test_input[name] = PSNR
self.net.eval()
bat_y = []
bat_opt = []
for i, bat in enumerate(self.test_DLoader[name]):
bat_y.append(bat['sp'])
opt_db_chn = torch.zeros_like(bat['sp'])
for c in range(3):
opt_db = self.eval_net(bat['bl'][:,:,:,:,c].cuda(), bat['Fker'].cuda())
opt_db_chn[:,:,:,:,c] = opt_db[-1].cpu()
bat_opt.append(opt_db_chn)
if self.args.save_img:
imshow(opt_db_chn.cpu(),dir=self.args.test_save_dir + self.args.dataset_name + '_' + name + '/', str = bat['name'][0])
if self.args.compute_metrics:
if parallel:
PSNR,SSIM = aver_bmp_psnr_ssim_par(bat_opt,bat_y, bd_cut=self.args.bd_cut, to_int = True, ssim_compute=True)
else:
PSNR = aver_bmp_psnr(bat_opt,bat_y, cut=self.args.bd_cut, to_int=True)
if input:
print(['%s: In %2.3f, Out PSNR: %2.3f, SSIM:%2.3F' % (name, self.test_input[name], PSNR, SSIM)])
else:
print(['%s: Out PSNR: %2.3f, SSIM:%2.3F' % (name, PSNR, SSIM)])
def test_lai_real(self, name):
self.net.eval()
bat_opt = []
for i, bat in enumerate(self.test_DLoader[name]):
opt_db_chn = torch.zeros_like(bat['bl'])
for c in range(3):
opt_db = self.eval_net(bat['bl'][:,:,:,:,c].cuda(), bat['Fker'].cuda())
opt_db_chn[:,:,:,:,c] = opt_db[-1].cpu()
bat_opt.append(opt_db_chn)
if self.args.save_img:
imshow(opt_db_chn,dir=self.args.test_save_dir + self.args.dataset_name + '_' + name + '/', str = bat['name'][0])
@staticmethod
def _ker_to_list(ker):
import numpy as np
ker = ker.numpy()
Kker = [None] * ker.shape[0]
for i in range(ker.shape[0]):
x, y = np.where(~np.isnan(ker[i]))
x_max = np.max(x)
y_max = np.max(y)
Kker[i] = ker[i, :x_max, :y_max]
return Kker
def load_model(self):
ckp = torch.load(self.args.test_ckp_dir, map_location=lambda storage, loc: storage.cuda(self.args.gpu_idx))
self.net.load_state_dict(ckp['model'])
return ckp
def eval_net(self, bl, *args):
with torch.no_grad():
self.net.eval()
bl = bl.cuda()
db = self.net(bl,*args)
return db
if __name__ == "__main__":
args = get_test_config()
torch.cuda.set_device(args.gpu_idx)
net = kernel_error_model(args).cuda()
test_dset = {}
if args.dataset_name =='Lai':
for name in args.test_ker:
test_dset[name] = Test_Lai_NoiseKernel(args.test_sp_dir, args.test_bl_dir, args.ker_dir[name],args.taper)
elif args.dataset_name == 'Lai_Real':
test_dset["lai_real"] = Test_Real_NoiseKernel(args.test_bl_dir, args.ker_dir["lai_real"],args.taper)
else:
for name in args.test_ker:
test_dset[name] = Test_NoiseKernel(args.test_sp_dir, args.test_bl_dir, args.ker_dir[name], args.tr_ker_dir, args.taper)
test = Tester(args, net, test_dset)
test()
print('[*] Finish!')