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trainer_SID.py
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import os
import time
import random
from torch.optim import Adam, lr_scheduler
from data_process import *
from utils import *
from archs import *
from losses import *
from base_trainer import *
class SID_Trainer(Base_Trainer):
def __init__(self):
super().__init__()
# model
self.net = globals()[self.arch['name']](self.arch)
# load weight
if self.hyper['last_epoch']: # 不是初始化
try:
model_path = os.path.join(f'./checkpoints/{self.model_name}_best_model.pth')
if not os.path.exists(model_path):
model_path = os.path.join(f'./checkpoints/{self.model_name}_last_model.pth')
model = torch.load(model_path, map_location=self.device)
self.net = load_weights(self.net, model, by_name=True)
except:
log('No checkpoint file!!!')
else:
log(f'Initializing {self.arch["name"]}...')
# initialize_weights(self.net)
self.optimizer = Adam(self.net.parameters(), lr=self.hyper['learning_rate'])
self.infos = None
if self.mode=='train':
self.dst_train = globals()[self.args['dst_train']['dataset']](self.args['dst_train'])
self.dataloader_train = DataLoader(self.dst_train, batch_size=self.hyper['batch_size'], worker_init_fn=self.worker_init_fn,
shuffle=True, num_workers=self.args['num_workers'], pin_memory=False)
self.change_eval_dst('eval')
self.dataloader_eval = DataLoader(self.dst_eval, batch_size=1, shuffle=False,
num_workers=self.args['num_workers'], pin_memory=False)
# Choose Learning Rate
self.lr_lambda = self.get_lr_lambda_func()
self.scheduler = LambdaScheduler(self.optimizer, self.lr_lambda)
self.net = self.net.to(self.device)
self.loss = Unet_Loss()
self.corrector = IlluminanceCorrect()
torch.backends.cudnn.benchmark = True
# model log
self.best_psnr = self.hyper['best_psnr'] if 'best_psnr' in self.hyper else 0
last_eval_epoch = self.hyper['last_epoch'] // self.hyper['plot_freq']
self.train_psnr = AverageMeter('PSNR', ':2f', last_epoch=self.hyper['last_epoch'])
self.eval_psnr = AverageMeter('PSNR', ':2f', last_epoch=last_eval_epoch)
self.eval_psnr_lr = AverageMeter('PSNR', ':2f')
self.eval_ssim_lr = AverageMeter('SSIM', ':4f')
self.eval_psnr_dn = AverageMeter('PSNR', ':2f')
self.eval_ssim_dn = AverageMeter('SSIM', ':4f')
self.logfile = f'./logs/log_{self.model_name}.log'
log(f'Model Name:\t{self.model_name}', log=self.logfile, notime=True)
log(f'Architecture:\t{self.arch["name"]}', log=self.logfile, notime=True)
log(f'TrainDataset:\t{self.args["dst_train"]["dataset"]}', log=self.logfile, notime=True)
log(f'EvalDataset:\t{self.args["dst_eval"]["dataset"]}', log=self.logfile, notime=True)
log(f'CameraType:\t{self.dst["camera_type"]}', log=self.logfile, notime=True)
log(f'num_channels:\t{self.arch["nf"]}', log=self.logfile, notime=True)
log(f'BatchSize:\t{self.hyper["batch_size"]}', log=self.logfile, notime=True)
log(f'PatchSize:\t{self.dst["patch_size"]}', log=self.logfile, notime=True)
log(f'LearningRate:\t{self.hyper["learning_rate"]}', log=self.logfile, notime=True)
log(f'Epoch:\t\t{self.hyper["stop_epoch"]}', log=self.logfile, notime=True)
log(f'num_workers:\t{self.args["num_workers"]}', log=self.logfile, notime=True)
log(f'Command:\t{self.dst["command"]}', log=self.logfile, notime=True)
log(f"Let's use {torch.cuda.device_count()} GPUs!", log=self.logfile, notime=True)
# self.device != torch.device(type='cpu')
if 'gpu_preprocess' in self.dst and self.dst['gpu_preprocess']:
log("Using PyTorch's GPU Preprocess...")
self.use_gpu = True
else:
log(f"Using Numpy's CPU Preprocess")
self.use_gpu = False
if torch.cuda.device_count() > 1:
log("Using PyTorch's nn.DataParallel for multi-gpu...")
self.multi_gpu = True
self.net = nn.DataParallel(self.net)
else:
self.multi_gpu = False
self.ratiofix = True if 'ratiofix' in self.dst['command'] else False
def change_eval_dst(self, mode='eval'):
self.dst = self.args[f'dst_{mode}']
self.dstname = self.dst['dstname']
self.dst_eval = globals()[self.dst['dataset']](self.dst)
self.dataloader_eval = DataLoader(self.dst_eval, batch_size=1, shuffle=False,
num_workers=self.args['num_workers'], pin_memory=False)
self.cache_dir = f'/data/cache/{self.dstname}'
def train(self):
self.scheduler.step()
lr = self.scheduler.get_last_lr()[0]
for epoch in range(self.hyper['last_epoch']+1, self.hyper['stop_epoch']+1):
# log init
self.net.train()
self.train_psnr.reset()
runtime = {'preprocess':0, 'dataloader':0, 'net':0, 'bp':0, 'metric':0, 'total':1e-9}
time_points = [0] * 10
time_points[0] = time.time()
with tqdm(total=len(self.dataloader_train)) as t:
for k, data in enumerate(self.dataloader_train):
runtime['dataloader'] += timestamp(time_points, 1)
# Preprocess
imgs_lr, imgs_hr, ratio = self.preprocess(data, mode='train', preprocess=True)
runtime['preprocess'] += timestamp(time_points, 2)
# 训练
self.optimizer.zero_grad()
pred = self.net(imgs_lr)
runtime['net'] += timestamp(time_points, 3)
# 极暗，乘上去
if self.dst['ori'] is True:
pred = pred * ratio
loss = self.loss(pred.clamp(0,1), imgs_hr)
loss.backward()
self.optimizer.step()
runtime['bp'] += timestamp(time_points, 4)
# 更新tqdm的参数
with torch.no_grad():
if self.arch['use_dpsv']:
pred = pred[0]
if 'rgb_gain' in data:
data['rgb_gain'] = data['rgb_gain'].view_as(ratio)
pred = pred / data['rgb_gain']
imgs_hr = imgs_hr / data['rgb_gain']
pred = torch.clamp(pred, 0, 1)
imgs_hr = torch.clamp(imgs_hr, 0, 1)
psnr = PSNR_Loss(pred, imgs_hr)
self.train_psnr.update(psnr.item())
runtime['total'] = runtime['preprocess']+runtime['dataloader']+runtime['net']+runtime['bp']
t.set_description(f'Epoch {epoch}')
t.set_postfix({'lr':f"{lr:.2e}", 'PSNR':f"{self.train_psnr.avg:.2f}",
'loader':f"{100*runtime['dataloader']/runtime['total']:.1f}%",
'process':f"{100*runtime['preprocess']/runtime['total']:.1f}%",
'net':f"{100*runtime['net']/runtime['total']:.1f}%",
'bp':f"{100*runtime['bp']/runtime['total']:.1f}%",})
t.update(1)
time_points[0] = time.time()
# 更新学习率
self.scheduler.step()
lr = self.scheduler.get_last_lr()[0]
# 存储模型
if epoch % self.hyper['save_freq'] == 0:
model_dict = self.net.module.state_dict() if self.multi_gpu else self.net.state_dict()
epoch_id = epoch // self.hyper['plot_freq'] * self.hyper['plot_freq']
save_path = os.path.join(self.model_dir, '%s_e%04d.pth'% (self.model_name, epoch_id))
torch.save(model_dict, save_path)
# 输出过程量，随时看
savefile = os.path.join(self.sample_dir, f'{self.model_name}_train_psnr.jpg')
logfile = os.path.join(self.sample_dir, f'{self.model_name}_train_psnr.pkl')
self.train_psnr.plot_history(savefile=savefile, logfile=logfile)
# if epoch % self.hyper['plot_freq'] == 0:
wb = data['wb'][0].numpy()
if self.dst['ori'] is True:
imgs_lr = imgs_lr * ratio
pred = pred
imgs_hr = imgs_hr# * ratio
if self.save_plot:
inputs = imgs_lr[0].detach().cpu().numpy().clip(0,1)
output = pred[0].detach().cpu().numpy()
target = imgs_hr[0].detach().cpu().numpy()
temp_img = np.concatenate((inputs, output, target),axis=2)[:3]
temp_img[0] = temp_img[0] * wb[0]
temp_img[2] = temp_img[2] * wb[2]
filename = os.path.join(self.sample_dir, 'temp', f'temp_{epoch//10*10:04d}.png')
temp_img = temp_img.transpose(1,2,0)[:,:,::-1] ** (1/2.2)
cv2.imwrite(filename, np.uint8(temp_img*255))
# fast eval
if (self.hyper['last_epoch']+epoch) % self.hyper['plot_freq'] == 0:
log(f"learning_rate: {lr:.3e}")
self.dst_eval.fast_eval(on=True)
self.eval(epoch=epoch)
self.dst_eval.fast_eval(on=False)
model_dict = self.net.module.state_dict() if self.multi_gpu else self.net.state_dict()
torch.save(model_dict, f'./checkpoints/{self.model_name}_last_model.pth')
# reload best model each period
num_of_epochs = self.hyper['stop_epoch'] - self.hyper['last_epoch']
T = self.hyper['T'] if 'T' in self.hyper else 1
period = num_of_epochs//T
if (self.hyper['last_epoch']+epoch) % period == 0:
model_path = os.path.join(f'./checkpoints/{self.model_name}_best_model.pth')
if os.path.exists(model_path):
model = torch.load(model_path, map_location=self.device)
self.net = load_weights(self.net, model, by_name=True, multi_gpu=self.multi_gpu)
log(f'Successfully reload best model (Eval PSNR:{self.best_psnr})',
log=f'./logs/log_{self.model_name}.log')
def eval(self, epoch=-1):
self.net.eval()
self.eval_psnr.reset()
self.eval_psnr_lr.reset()
self.eval_psnr_dn.reset()
self.eval_ssim_lr.reset()
self.eval_ssim_dn.reset()
if epoch > 0:
pool = []
else:
pool = ProcessPoolExecutor(max_workers=max(4, self.args['num_workers']))
# record every metric
metrics = {}
metrics_path = f'./metrics/{self.model_name}_metrics.pkl'
if os.path.exists(metrics_path):
with open(metrics_path, 'rb') as f:
metrics = pkl.load(f)
# multiprocess
task_list = []
save_plot = self.save_plot
with tqdm(total=len(self.dataloader_eval)) as t:
for k, data in enumerate(self.dataloader_eval):
# 由于crops的存在，Dataloader会把数据变成5维，需要view回4维
imgs_lr, imgs_hr, ratio = self.preprocess(data, mode='eval', preprocess=False)
wb = data['wb'][0].numpy()
ccm = data['ccm'][0].numpy()
name = data['name'][0]
ISO = data['ISO'].item()
# print(ISO)
with torch.no_grad():
# # 太大了就用下面这个策略
# croped_imgs_lr = self.dst_eval.eval_crop(imgs_lr)
# croped_imgs_hr = self.dst_eval.eval_crop(imgs_hr)
# croped_imgs_dn = []
# for img_lr, img_hr in zip(croped_imgs_lr, croped_imgs_hr):
# img_dn = self.net(img_lr)
# croped_imgs_dn.append(img_dn)
# croped_imgs_dn = torch.cat(croped_imgs_dn)
# imgs_lr = self.dst_eval.eval_merge(croped_imgs_lr)
# imgs_dn = self.dst_eval.eval_merge(croped_imgs_dn)
# 扛得住就pad再crop
if imgs_lr.shape[-1] % 16 != 0:
p2d = (4,4,4,4)
imgs_lr = F.pad(imgs_lr, p2d, mode='reflect')
imgs_dn = self.net(imgs_lr)
imgs_lr = imgs_lr[..., 4:-4, 4:-4]
imgs_dn = imgs_dn[..., 4:-4, 4:-4]
else:
imgs_dn = self.net(imgs_lr)
# brighten
if self.dst['ori']:
imgs_lr = imgs_lr * ratio
imgs_dn = imgs_dn * ratio
imgs_lr = torch.clamp(imgs_lr, 0, 1)
imgs_dn = torch.clamp(imgs_dn, 0, 1)
# align to ELD's configuration (.=_=.)
if self.args['brightness_correct'] and epoch < 0:
# ds_raw = self.dst_eval.get_darkshading(ISO)
# ds = raw2bayer(ds_raw, wp=16383-512, bl=0, norm=True, clip=False)
# imgs_ds = torch.from_numpy(ds[None,]).type(torch.FloatTensor).to(self.device)
# imgs_dn = imgs_dn + imgs_ds
imgs_dn = self.corrector(imgs_dn, imgs_hr)
# PSNR & SSIM (Raw domain)
output = tensor2im(imgs_dn)
target = tensor2im(imgs_hr)
res = quality_assess(output, target, data_range=255)
raw_metrics = [res['PSNR'], res['SSIM']]
self.eval_psnr.update(res['PSNR'])
self.eval_psnr_dn.update(res['PSNR'])
self.eval_ssim_dn.update(res['SSIM'])
metrics[name] = raw_metrics
# convert raw to rgb
if save_plot:
if self.infos is None:
inputs = tensor2im(imgs_lr)
res_in = quality_assess(inputs, target, data_range=255)
raw_metrics = [res_in['PSNR'], res_in['SSIM']] + raw_metrics
else:
raw_metrics = [self.infos[k]['PSNR_raw'], self.infos[k]['SSIM_raw']] + raw_metrics
if epoch > 0:
pool.append(threading.Thread(target=self.multiprocess_plot, args=(imgs_lr, imgs_dn, imgs_hr,
wb, ccm, name, save_plot, epoch, raw_metrics, k)))
pool[k].start()
else:
infos = self.infos[k] if self.infos is not None else None
# 多进程
if infos is None:
inputs = raw2rgb_rawpy(imgs_lr, wb=wb, ccm=ccm)
target = raw2rgb_rawpy(imgs_hr, wb=wb, ccm=ccm)
else:
inputs = np.load(infos['path_npy_in'])
target = np.load(infos['path_npy_gt'])
output = raw2rgb_rawpy(imgs_dn, wb=wb, ccm=ccm)
task_list.append(
pool.submit(plot_sample, inputs, output, target,
filename=name, save_plot=save_plot, epoch=epoch,
model_name=self.model_name, save_path=self.sample_dir,
res=raw_metrics
)
)
t.set_description(f'{name}')
t.set_postfix({'PSNR':f"{self.eval_psnr.avg:.2f}"})
t.update(1)
if save_plot:
if epoch > 0:
for i in range(len(pool)):
pool[i].join()
else:
pool.shutdown(wait=True)
for task in as_completed(task_list):
psnr, ssim = task.result()
self.eval_psnr_lr.update(psnr[0])
self.eval_psnr_dn.update(psnr[1])
self.eval_ssim_lr.update(ssim[0])
self.eval_ssim_dn.update(ssim[1])
# 超过最好记录才保存
if self.eval_psnr_dn.avg >= self.best_psnr and epoch > 0:
self.best_psnr = self.eval_psnr_dn.avg
log(f"Best PSNR is {self.best_psnr} now!!")
model_dict = self.net.module.state_dict() if self.multi_gpu else self.net.state_dict()
torch.save(model_dict, f'./checkpoints/{self.model_name}_best_model.pth')
log(f"Epoch {epoch}: PSNR={self.eval_psnr.avg:.2f}\n"
+f"psnrs_lr={self.eval_psnr_lr.avg:.2f}, psnrs_dn={self.eval_psnr_dn.avg:.2f}"
+f"\nssims_lr={self.eval_ssim_lr.avg:.4f}, ssims_dn={self.eval_ssim_dn.avg:.4f}",
log=f'./logs/log_{self.model_name}.log')
with open(metrics_path, 'wb') as f:
pkl.dump(metrics, f)
savefile = os.path.join(self.sample_dir, f'{self.model_name}_eval_psnr.jpg')
logfile = os.path.join(self.sample_dir, f'{self.model_name}_eval_psnr.pkl')
if epoch > 0:
self.eval_psnr.plot_history(savefile=savefile, logfile=logfile)
del pool
plt.close('all')
gc.collect()
return metrics
def multiprocess_plot(self, imgs_lr, imgs_dn, imgs_hr, wb, ccm, name, save_plot, epoch, raw_metrics, k):
# if self.infos is None:
inputs = raw2rgb_rawpy(imgs_lr, wb=wb, ccm=ccm)
target = raw2rgb_rawpy(imgs_hr, wb=wb, ccm=ccm)
# else:
# inputs = np.load(self.infos[k]['path_npy_in'])
# target = np.load(self.infos[k]['path_npy_gt'])
output = raw2rgb_rawpy(imgs_dn, wb=wb, ccm=ccm)
psnr, ssim = plot_sample(inputs, output, target,
filename=name,
save_plot=save_plot, epoch=epoch,
model_name=self.model_name,
save_path=self.sample_dir,
res=raw_metrics)
self.eval_psnr_lr.update(psnr[0])
self.eval_psnr_dn.update(psnr[1])
self.eval_ssim_lr.update(ssim[0])
self.eval_ssim_dn.update(ssim[1])
def predict(self, raw, name='ds'):
self.net.eval()
img_lr = raw2bayer(raw+self.dst["bl"])[None, ...]
img_lr = torch.from_numpy(img_lr)
img_lr = img_lr.type(torch.FloatTensor).to(self.device)
with torch.no_grad():
croped_imgs_lr = self.dst_eval.eval_crop(img_lr)
croped_imgs_dn = []
for img_lr in tqdm(croped_imgs_lr):
img_dn = self.net(img_lr)
croped_imgs_dn.append(img_dn)
croped_imgs_dn = torch.cat(croped_imgs_dn)
img_dn = self.dst_eval.eval_merge(croped_imgs_dn)
img_dn = img_dn
img_dn = img_dn[0].detach().cpu().numpy()
np.save(f'{name}.npy', img_dn)
def preprocess(self, data, mode='train', preprocess=True):
# 由于crops的存在，Dataloader会把数据变成5维，需要view回4维
imgs_hr = tensor_dim5to4(data['hr']).type(torch.FloatTensor).to(self.device)
imgs_lr = tensor_dim5to4(data['lr']).type(torch.FloatTensor).to(self.device)
# self.use_gpu = True
dst = self.dst_train if mode=='train' else self.dst_eval
if self.use_gpu and mode=='train' and preprocess:
b = imgs_lr.shape[0]
if self.args['dst_train']['dataset'] == 'Mix_Dataset':
data['ratio'] = data['ratio'].view(-1).type(torch.FloatTensor).to(self.device)
aug_r, aug_g, aug_b = get_aug_param_torch(data, b=b, command=self.dst['command'])
aug_wbs = torch.stack((aug_r, aug_g, aug_b, aug_g), dim=1)
data['rgb_gain'] = torch.ones(b) * (aug_g + 1)
for i in range(b):
aug_wb = aug_wbs[i].numpy()
dgain = data['ratio'][i]
if np.abs(aug_wb).max() != 0:
data['wb'] *= (1+aug_wb) / (1+aug_g[i])
if self.ratiofix:
data['ratio'][i] /= (1+aug_g[i])
iso = data['ISO'][i//self.dst['crop_per_image']].item()
imgs_lr[i], imgs_hr[i] = raw_wb_aug_torch(imgs_lr[i], imgs_hr[i], iso=iso, ratiofix=self.ratiofix,
aug_wb=aug_wb, camera_type=self.dst['camera_type'], ratio=dgain, ori=self.dst['ori'])
else:
imgs_lr[i] = imgs_lr[i] if self.dst['ori'] else imgs_lr[i] * dgain
elif self.args['dst_train']['dataset'] == 'Raw_Dataset':
data['ratio'] = torch.ones(b, device=self.device)
# 人工加噪声，注意，这里统一时间的视频应该共享相同的噪声参数！！
for i in range(b):
if dst.args['params'] is None:
noise_param = sample_params_max(camera_type=self.dst['camera_type'], ratio=None)
else:
noise_param = dst.args['params']
for key in noise_param:
if torch.is_tensor(noise_param[key]) is False:
noise_param[key] = torch.from_numpy(np.array(noise_param[key], np.float32))
noise_param[key] = noise_param[key].to(self.device)
data['ratio'][i] = noise_param['ratio']
imgs_lr[i] = generate_noisy_torch(imgs_lr[i], param=noise_param,
noise_code=self.dst['noise_code'], ori=self.dst['ori'], clip=self.dst['clip'])
else: # mode == 'eval'
pass
ratio = data['ratio'].type(torch.FloatTensor).to(self.device)
ratio = ratio.view(-1,1,1,1)
if 'rgb_gain' in data:
data['rgb_gain'] = data['rgb_gain'].type(torch.FloatTensor).to(self.device).view_as(ratio)
if self.dst['clip']:
imgs_lr = imgs_lr.clamp(0, 1)
imgs_hr = imgs_hr.clamp(0, 1)
return imgs_lr, imgs_hr, ratio
def MultiProcessPlot(imgs_lr, imgs_dn, imgs_hr, wb, ccm, name, save_plot, epoch,
raw_metrics, infos, model_name, sample_dir):
if infos is None:
inputs = raw2rgb_rawpy(imgs_lr, wb=wb, ccm=ccm)
target = raw2rgb_rawpy(imgs_hr, wb=wb, ccm=ccm)
else:
inputs = np.load(infos['path_npy_in'])
target = np.load(infos['path_npy_gt'])
output = raw2rgb_rawpy(imgs_dn, wb=wb, ccm=ccm)
psnr, ssim = plot_sample(inputs, output, target,
filename=name,
save_plot=save_plot, epoch=epoch,
model_name=model_name,
save_path=sample_dir,
res=raw_metrics)
return psnr, ssim
if __name__ == '__main__':
trainer = SID_Trainer()
if trainer.mode == 'train':
trainer.train()
savefile = os.path.join(trainer.sample_dir, f'{trainer.model_name}_train_psnr.jpg')
logfile = os.path.join(trainer.sample_dir, f'{trainer.model_name}_train_psnr.pkl')
trainer.train_psnr.plot_history(savefile=savefile, logfile=logfile)
trainer.eval_psnr.plot_history(savefile=os.path.join(trainer.sample_dir, f'{trainer.model_name}_eval_psnr.jpg'))
trainer.mode = 'evaltest'
# best_model
best_model_path = os.path.join(f'./checkpoints/{trainer.model_name}_best_model.pth')
if os.path.exists(best_model_path) is False:
best_model_path = os.path.join(f'./checkpoints/{trainer.model_name}_last_model.pth')
best_model = torch.load(best_model_path, map_location=trainer.device)
trainer.net = load_weights(trainer.net, best_model, multi_gpu=trainer.multi_gpu)
if 'eval' in trainer.mode:
# ELD
trainer.change_eval_dst('eval')
for dgain in trainer.args['dst_eval']['ratio_list']:
info_path = os.path.join(trainer.cache_dir, f'{trainer.dstname}_{dgain}.pkl')
if os.path.exists(info_path):
with open(info_path,'rb') as f:
trainer.infos = pkl.load(f)
log(f'ELD Datasets: Dgain={dgain}',log=f'./logs/log_{trainer.model_name}.log')
trainer.dst_eval.ratio_list=[dgain]
trainer.dst_eval.recheck_length()
metrics = trainer.eval(-1)
print(metrics)
if 'test' in trainer.mode:
# SID
trainer.change_eval_dst('test')
SID_ratio_list = [100, 250, 300]
for dgain in SID_ratio_list:
info_path = os.path.join(trainer.cache_dir, f'{trainer.dstname}_{dgain}.pkl')
if os.path.exists(info_path):
with open(info_path,'rb') as f:
trainer.infos = pkl.load(f)
log(f'SID Datasets: Dgain={dgain}',log=f'./logs/log_{trainer.model_name}.log')
trainer.dst_eval.change_eval_ratio(ratio=dgain)
metrics = trainer.eval(-1)
print(metrics)