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eval_Bread.py
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eval_Bread.py
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import argparse
import os
import kornia
import torch
import torch.nn.functional as F
import tqdm
from torch import nn
from torch.utils.data import DataLoader
import models
from datasets import LowLightDataset
from tools import saver, mutils
from models import PSNR, SSIM
import numpy as np
def get_args():
parser = argparse.ArgumentParser('Breaking Downing the Darkness')
parser.add_argument('--num_gpus', type=int, default=1, help='number of gpus being used')
parser.add_argument('--num_workers', type=int, default=12, help='num_workers of dataloader')
parser.add_argument('--batch_size', type=int, default=4, help='The number of images per batch among all devices')
parser.add_argument('-m1', '--model1', type=str, default='IANet', help='Model1 Name')
parser.add_argument('-m2', '--model2', type=str, default='NSNet', help='Model2 Name')
parser.add_argument('-m3', '--model3', type=str, default='FuseNet', help='Model3 Name')
parser.add_argument('-m4', '--model4', type=str, default=None, help='Model4 Name')
parser.add_argument('-m1w', '--model1_weight', type=str, default=None, help='Model weight of IAN')
parser.add_argument('-m2w', '--model2_weight', type=str, default=None, help='Model weight of ANSN')
parser.add_argument('-m3w', '--model3_weight', type=str, default=None, help='Model weight of CAN')
parser.add_argument('-m4w', '--model4_weight', type=str, default=None, help='Model weight of NFM')
parser.add_argument('--mef', action='store_true', help='using color adation based MEF data or not')
parser.add_argument('--gc', action='store_true', help='using gamma correction or not')
parser.add_argument('--save_extra', action='store_true', help='save intermediate outputs or not')
parser.add_argument('--comment', type=str, default='default',
help='Project comment')
parser.add_argument('--alpha', '-a', type=float, default=0.10)
parser.add_argument('--lr', type=float, default=0.01)
parser.add_argument('--optim', type=str, default='adamw', help='select optimizer for training, '
'suggest using \'admaw\' until the'
' very final stage then switch to \'sgd\'')
parser.add_argument('--data_path', type=str, default='./data/LOL/eval',
help='the root folder of dataset')
parser.add_argument('--log_path', type=str, default='logs/')
parser.add_argument('--saved_path', type=str, default='logs/')
args = parser.parse_args()
return args
class ModelBreadNet(nn.Module):
def __init__(self, model1, model2, model3, model4):
super().__init__()
self.eps = 1e-6
self.model_ianet = model1(in_channels=1, out_channels=1)
self.model_nsnet = model2(in_channels=2, out_channels=1)
self.model_canet = model3(in_channels=4, out_channels=2) if opt.mef else model3(in_channels=6, out_channels=2)
self.model_fdnet = model4(in_channels=3, out_channels=1) if opt.model4 else None
self.load_weight(self.model_ianet, opt.model1_weight)
self.load_weight(self.model_nsnet, opt.model2_weight)
self.load_weight(self.model_canet, opt.model3_weight)
self.load_weight(self.model_fdnet, opt.model4_weight)
def load_weight(self, model, weight_pth):
if model is not None:
state_dict = torch.load(weight_pth)
ret = model.load_state_dict(state_dict, strict=True)
print(ret)
def noise_syn_exp(self, illumi, strength):
return torch.exp(-illumi) * strength
def forward(self, image, image_gt):
# Color space mapping
texture_in, cb_in, cr_in = torch.split(kornia.color.rgb_to_ycbcr(image), 1, dim=1)
texture_gt, _, _ = torch.split(kornia.color.rgb_to_ycbcr(image_gt), 1, dim=1)
# Illumination prediction
texture_in_down = F.interpolate(texture_in, scale_factor=0.5, mode='bicubic', align_corners=True)
texture_illumi = self.model_ianet(texture_in_down)
texture_illumi = F.interpolate(texture_illumi, scale_factor=2, mode='bicubic', align_corners=True)
# Illumination adjustment
texture_illumi = torch.clamp(texture_illumi, 0., 1.)
texture_ia = texture_in / torch.clamp_min(texture_illumi, self.eps)
texture_ia = torch.clamp(texture_ia, 0., 1.)
# Noise suppression and fusion
texture_nss = []
for strength in [0., 0.05, 0.1]:
attention = self.noise_syn_exp(texture_illumi, strength=strength)
texture_res = self.model_nsnet(torch.cat([texture_ia, attention], dim=1))
texture_ns = texture_ia + texture_res
texture_nss.append(texture_ns)
texture_nss = torch.cat(texture_nss, dim=1).detach()
texture_fd = self.model_fdnet(texture_nss)
# Gamma correction to align the brightness with ground truth;
# other methods involved in our main paper are also conducted the same correction for evaluation.
if opt.gc:
max_psnr = 0
best = None
for ga in np.arange(0.1, 2.0, 0.01):
tx_en = texture_fd ** ga
psnr = PSNR(tx_en, texture_gt)
if psnr > max_psnr:
max_psnr = psnr
best = tx_en
texture_fd = torch.clamp(best, 0, 1)
# Color adaption
if not opt.mef:
image_ia_ycbcr = kornia.color.rgb_to_ycbcr(torch.clamp(image / (texture_illumi + self.eps), 0, 1))
_, cb_ia, cr_ia = torch.split(image_ia_ycbcr, 1, dim=1)
colors = self.model_canet(torch.cat([texture_in, cb_in, cr_in, texture_fd, cb_ia, cr_ia], dim=1))
else:
colors = self.model_canet(
torch.cat([texture_in, cb_in, cr_in, texture_fd], dim=1))
cb_out, cr_out = torch.split(colors, 1, dim=1)
cb_out = torch.clamp(cb_out, 0, 1)
cr_out = torch.clamp(cr_out, 0, 1)
# Color space mapping
image_out = kornia.color.ycbcr_to_rgb(
torch.cat([texture_fd, cb_out, cr_out], dim=1))
image_out = torch.clamp(image_out, 0, 1)
# Calculating image quality metrics
psnr = PSNR(image_out, image_gt)
ssim = SSIM(image_out, image_gt).item()
return texture_ia, texture_nss, texture_fd, image_out, texture_illumi, texture_res, psnr, ssim
def evaluation(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(42)
else:
torch.manual_seed(42)
timestamp = mutils.get_formatted_time()
opt.saved_path = opt.saved_path + f'/{opt.comment}/{timestamp}'
os.makedirs(opt.saved_path, exist_ok=True)
val_params = {'batch_size': 1,
'shuffle': False,
'drop_last': False,
'num_workers': opt.num_workers}
val_set = LowLightDataset(opt.data_path)
val_generator = DataLoader(val_set, **val_params)
val_generator = tqdm.tqdm(val_generator)
model1 = getattr(models, opt.model1)
model2 = getattr(models, opt.model2)
model3 = getattr(models, opt.model3)
model4 = getattr(models, opt.model4) if opt.model4 else None
model = ModelBreadNet(model1, model2, model3, model4)
print(model)
if opt.num_gpus > 0:
model = model.cuda()
if opt.num_gpus > 1:
model = nn.DataParallel(model)
model.eval()
psnrs, ssims, fns = [], [], []
for iter, (data, target, name) in enumerate(val_generator):
saver.base_url = os.path.join(opt.saved_path, 'results')
with torch.no_grad():
if opt.num_gpus == 1:
data = data.cuda()
target = target.cuda()
texture_in, _, _ = torch.split(kornia.color.rgb_to_ycbcr(data), 1, dim=1)
texture_gt, _, _ = torch.split(kornia.color.rgb_to_ycbcr(target), 1, dim=1)
texture_ia, texture_nss, texture_fd, image_out, \
texture_illumi, texture_res, psnr, ssim = model(data, target)
if opt.save_extra:
saver.save_image(data, name=os.path.splitext(name[0])[0] + '_im_in')
saver.save_image(target, name=os.path.splitext(name[0])[0] + '_im_gt')
saver.save_image(texture_in, name=os.path.splitext(name[0])[0] + '_y_in')
saver.save_image(texture_gt, name=os.path.splitext(name[0])[0] + '_y_gt')
saver.save_image(texture_ia, name=os.path.splitext(name[0])[0] + '_ia')
for i in range(texture_nss.shape[1]):
saver.save_image(texture_nss[:, i, ...], name=os.path.splitext(name[0])[0] + f'_ns_{i}')
saver.save_image(texture_fd, name=os.path.splitext(name[0])[0] + '_fd')
saver.save_image(texture_illumi, name=os.path.splitext(name[0])[0] + '_illumi')
saver.save_image(texture_res, name=os.path.splitext(name[0])[0] + '_res')
saver.save_image(image_out, name=os.path.splitext(name[0])[0] + '_out')
else:
saver.save_image(image_out, name=os.path.splitext(name[0])[0] + '_Bread')
psnrs.append(psnr)
ssims.append(ssim)
fns.append(name[0])
results = list(zip(psnrs, ssims, fns))
results.sort(key=lambda item: item[0])
for r in results:
print(*r)
psnr = np.mean(np.array(psnrs))
ssim = np.mean(np.array(ssims))
print('psnr: ', psnr, ', ssim: ', ssim)
if __name__ == '__main__':
opt = get_args()
evaluation(opt)