class Session:
def __init__(self):
self.log_dir = settings.log_dir
self.model_dir = settings.model_dir
ensure_dir(settings.log_dir)
ensure_dir(settings.model_dir)
logger.info('set log dir as %s' % settings.log_dir)
logger.info('set model dir as %s' % settings.model_dir)
# self.net = RESCAN().cuda()
if len(settings.device_id) > 1:
self.net = nn.DataParallel(RESCAN()).cuda()
else:
self.net = RESCAN().cuda()
self.crit = MSELoss().cuda()
self.ssim = SSIM().cuda()
self.dataloaders = {}
def get_dataloader(self, dataset_name):
dataset = TestDataset(dataset_name)
if not dataset_name in self.dataloaders:
self.dataloaders[dataset_name] = \
DataLoader(dataset, batch_size=1,
shuffle=False, num_workers=1, drop_last=False)
return self.dataloaders[dataset_name]
def load_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
try:
obj = torch.load(ckp_path)
logger.info('Load checkpoint %s' % ckp_path)
except FileNotFoundError:
logger.info('No checkpoint %s!!' % ckp_path)
return
self.net.load_state_dict(obj['net'])
def inf_batch(self, name, batch):
O, B = batch['O'].cuda(), batch['B'].cuda()
O, B = Variable(O, requires_grad=False), Variable(B,
requires_grad=False)
R = O - B
with torch.no_grad():
O_Rs = self.net(O)
loss_list = [self.crit(O_R, B) for O_R in O_Rs]
ssim_list = [self.ssim(O_R, B) for O_R in O_Rs]
psnr = PSNR(O_Rs[0].data.cpu().numpy() * 255,
B.data.cpu().numpy() * 255)
losses = {
'loss%d' % i: loss.item()
for i, loss in enumerate(loss_list)
}
ssimes = {
'ssim%d' % i: ssim.item()
for i, ssim in enumerate(ssim_list)
}
losses.update(ssimes)
return losses, psnr
class Session:
def __init__(self):
self.show_dir = settings.show_dir
self.model_dir = settings.model_dir
ensure_dir(settings.show_dir)
ensure_dir(settings.model_dir)
logger.info('set show dir as %s' % settings.show_dir)
logger.info('set model dir as %s' % settings.model_dir)
# self.net = RESCAN().cuda()
if len(settings.device_id) >1:
self.net = nn.DataParallel(RESCAN()).cuda()
else:
self.net = RESCAN().cuda()
self.dataloaders = {}
self.ssim=SSIM().cuda()
def get_dataloader(self, dataset_name):
dataset = ShowDataset(dataset_name)
self.dataloaders[dataset_name] = \
DataLoader(dataset, batch_size=1,
shuffle=False, num_workers=1)
return self.dataloaders[dataset_name]
def load_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
try:
obj = torch.load(ckp_path)
logger.info('Load checkpoint %s' % ckp_path)
except FileNotFoundError:
logger.info('No checkpoint %s!!' % ckp_path)
return
self.net.load_state_dict(obj['net'])
def inf_batch(self, name, batch):
O, B = batch['O'].cuda(), batch['B'].cuda()
O, B = Variable(O, requires_grad=False), Variable(B, requires_grad=False)
with torch.no_grad():
O_Rs = self.net(O)
#loss_list = [self.crit(O_R, B) for O_R in O_Rs]
ssim_list = [self.ssim(O_R, B) for O_R in O_Rs]
psnr=PSNR(O_Rs[0].data.cpu().numpy()*255, B.data.cpu().numpy()*255)
print('psnr:%4f-------------ssim:%4f'%(psnr,ssim_list[0]))
return O_Rs[-1],psnr,ssim_list[0]
def save_image(self, No, imgs, name, psnr, ssim):
for i, img in enumerate(imgs):
img = (img.cpu().data * 255).numpy()
img = np.clip(img, 0, 255)
img = np.transpose(img, (1, 2, 0))
h, w, c = img.shape
img_file = os.path.join(self.show_dir, '%s_%d_%d_%4f_%4f.png' % (name, No, i,psnr,ssim))
cv2.imwrite(img_file, img)
class Session:
def __init__(self):
self.show_dir = settings.show_dir
self.model_dir = settings.model_dir
ensure_dir(settings.show_dir)
ensure_dir(settings.model_dir)
logger.info('set show dir as %s' % settings.show_dir)
logger.info('set model dir as %s' % settings.model_dir)
self.net = RESCAN().cuda()
self.dataset = None
self.dataloader = None
def get_dataloader(self, dataset_name):
self.dataset = ShowDataset(dataset_name)
self.dataloader = \
DataLoader(self.dataset, batch_size=1,
shuffle=False, num_workers=1)
return self.dataloader
def load_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
try:
obj = torch.load(ckp_path)
logger.info('Load checkpoint %s' % ckp_path)
except FileNotFoundError:
logger.info('No checkpoint %s!!' % ckp_path)
return
self.net.load_state_dict(obj['net'])
def inf_batch(self, name, batch):
O = batch['O'].cuda()
O = Variable(O, requires_grad=False)
with torch.no_grad():
O_Rs = self.net(O)
O_Rs = [O - O_R for O_R in O_Rs]
return O_Rs
def save_image(self, No, imgs, ori):
for i, img in enumerate(imgs):
img = (img.cpu().data * 255).numpy()
img = np.clip(img, 0, 255)[0]
img = np.transpose(img, (1, 2, 0))
# h, w, c = img.shape
# Add original
img = np.hstack((ori, img))
img_file = os.path.join(self.show_dir, '%s_%d.png' % (No, i))
cv2.imwrite(img_file, img)
class Session:
def __init__(self):
self.log_dir = settings.log_dir
self.model_dir = settings.model_dir
ensure_dir(settings.log_dir)
ensure_dir(settings.model_dir)
logger.info('set log dir as %s' % settings.log_dir)
logger.info('set model dir as %s' % settings.model_dir)
self.net = RESCAN().cuda()
self.crit = MSELoss().cuda()
self.ssim = SSIM().cuda()
self.step = 0
self.save_steps = settings.save_steps
self.num_workers = settings.num_workers
self.batch_size = settings.batch_size
self.writers = {}
self.dataloaders = {}
self.opt = Adam(self.net.parameters(), lr=settings.lr)
self.sche = MultiStepLR(self.opt, milestones=[15000, 17500], gamma=0.1)
def tensorboard(self, name):
self.writers[name] = SummaryWriter(
os.path.join(self.log_dir, name + '.events'))
return self.writers[name]
def write(self, name, out):
for k, v in out.items():
self.writers[name].add_scalar(k, v, self.step)
out['lr'] = self.opt.param_groups[0]['lr']
out['step'] = self.step
outputs = ["{}:{:.4g}".format(k, v) for k, v in out.items()]
logger.info(name + '--' + ' '.join(outputs))
def get_dataloader(self, dataset_name):
dataset = TrainValDataset(dataset_name)
if not dataset_name in self.dataloaders:
self.dataloaders[dataset_name] = \
DataLoader(dataset, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers, drop_last=True)
return iter(self.dataloaders[dataset_name])
def save_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
obj = {
'net': self.net.state_dict(),
'clock': self.step,
'opt': self.opt.state_dict(),
}
torch.save(obj, ckp_path)
def load_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
try:
obj = torch.load(ckp_path)
logger.info('Load checkpoint %s' % ckp_path)
except FileNotFoundError:
logger.info('No checkpoint %s!!' % ckp_path)
return
self.net.load_state_dict(obj['net'])
self.opt.load_state_dict(obj['opt'])
self.step = obj['clock']
self.sche.last_epoch = self.step
def inf_batch(self, name, batch):
O, B = batch['O'].cuda(), batch['B'].cuda()
O, B = Variable(O, requires_grad=False), Variable(B,
requires_grad=False)
R = O - B
O_Rs = self.net(O)
loss_list = [self.crit(O_R, R) for O_R in O_Rs]
ssim_list = [self.ssim(O - O_R, O - R) for O_R in O_Rs]
if name == 'train':
self.net.zero_grad()
sum(loss_list).backward()
self.opt.step()
losses = {
'loss%d' % i: loss.item()
for i, loss in enumerate(loss_list)
}
ssimes = {
'ssim%d' % i: ssim.item()
for i, ssim in enumerate(ssim_list)
}
losses.update(ssimes)
self.write(name, losses)
return O - O_Rs[-1]
def save_image(self, name, img_lists):
data, pred, label = img_lists
pred = pred.cpu().data
data, label, pred = data * 255, label * 255, pred * 255
pred = np.clip(pred, 0, 255)
h, w = pred.shape[-2:]
gen_num = (6, 2)
img = np.zeros((gen_num[0] * h, gen_num[1] * 3 * w, 3))
for img_list in img_lists:
for i in range(gen_num[0]):
row = i * h
for j in range(gen_num[1]):
idx = i * gen_num[1] + j
tmp_list = [data[idx], pred[idx], label[idx]]
for k in range(3):
col = (j * 3 + k) * w
tmp = np.transpose(tmp_list[k], (1, 2, 0))
img[row:row + h, col:col + w] = tmp
img_file = os.path.join(self.log_dir, '%d_%s.jpg' % (self.step, name))
cv2.imwrite(img_file, img)
class Session:
def __init__(self):
self.log_dir = settings.log_dir
self.model_dir = settings.model_dir
ensure_dir(settings.log_dir)
ensure_dir(settings.model_dir)
logger.info('set log dir as %s' % settings.log_dir)
logger.info('set model dir as %s' % settings.model_dir)
# self.net = RESCAN().cuda()
if len(settings.device_id) >1:
self.net = nn.DataParallel(RESCAN()).cuda()
else:
self.net = RESCAN().cuda()
self.crit = MSELoss().cuda()
self.ssim = SSIM().cuda()
self.step = 0
self.save_steps = settings.save_steps
self.num_workers = settings.num_workers
self.batch_size = settings.batch_size
self.writers = {}
self.dataloaders = {}
self.opt = Adam(self.net.parameters(), lr=settings.lr)
self.sche = MultiStepLR(self.opt, milestones=[240000, 320000], gamma=0.1)
def tensorboard(self, name):
self.writers[name] = SummaryWriter(os.path.join(self.log_dir, name + '.events'))
return self.writers[name]
def write(self, name, out):
for k, v in out.items():
self.writers[name].add_scalar(k, v, self.step)
out['lr'] = self.opt.param_groups[0]['lr']
out['step'] = self.step
outputs = [
"{}:{:.4g}".format(k, v)
for k, v in out.items()
]
logger.info(name + '--' + ' '.join(outputs))
def get_dataloader(self, dataset_name):
dataset = TrainValDataset(dataset_name)
if not dataset_name in self.dataloaders:
self.dataloaders[dataset_name] = \
DataLoader(dataset, batch_size=self.batch_size,
shuffle=True, num_workers=self.num_workers, drop_last=True)
return iter(self.dataloaders[dataset_name])
def save_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
obj = {
'net': self.net.state_dict(),
'clock': self.step,
'opt': self.opt.state_dict(),
}
torch.save(obj, ckp_path)
def load_checkpoints(self, name):
ckp_path = os.path.join(self.model_dir, name)
try:
logger.info('Load checkpoint %s' % ckp_path)
obj = torch.load(ckp_path)
except FileNotFoundError:
logger.info('No checkpoint %s!!' % ckp_path)
return
self.net.load_state_dict(obj['net'] )
self.opt.load_state_dict(obj['opt'])
self.step = obj['clock']
self.sche.last_epoch = self.step
def print_network(self, model):
num_params = 0
for p in model.parameters():
num_params += p.numel() # 1. torch.numel() 返回一个tensor变量内所有元素个数，可以理解为矩阵内元素的个数
print(model)
print("The number of parameters: {}".format(num_params))
def inf_batch(self, name, batch):
if name == 'train':
self.net.zero_grad()
if self.step==0:
self.print_network(self.net)
O, B = batch['O'].cuda(), batch['B'].cuda()
O, B = Variable(O, requires_grad=False), Variable(B, requires_grad=False)
O_Rs = self.net(O)
loss_list = [self.crit(O_Rs[0],B)]
ssim_list = [self.ssim(O_Rs[0],B)]
#ssim_list = [self.ssim(O - O_R, O - R) for O_R in O_Rs]
#vgg_gt = self.vgg.forward(B)
#path1_vgg_eval = self.vgg.forward(O_Rs[0])
#loss1 = [self.crit(path1_vgg_eval[m], vgg_gt[m]) for m in range(len(vgg_gt))]
if name == 'train':
loss = (loss_list[0])
loss.backward()
self.opt.step()
losses = {
'loss%d' % i: loss.item()
for i, loss in enumerate(loss_list)
}
ssimes = {
'ssim%d' % i: ssim.item()
for i, ssim in enumerate(ssim_list)
}
losses.update(ssimes)
self.write(name, losses)
return O_Rs[-1]
def save_image(self, name, img_lists):
data, pred, label = img_lists # data 有雨图，pred 网络学习的无雨图，label：clear gt
pred = pred.cpu().data # pred 网络预测的无雨图
data, label, pred = data * 255, label * 255, pred * 255
pred = np.clip(pred, 0, 255)
h, w = pred.shape[-2:] # c, h, w
#gen_num = (6, 2)
gen_num = (3, 1)
img = np.zeros((gen_num[0] * h, gen_num[1] * 3 * w, 3))
for img_list in img_lists:
for i in range(gen_num[0]):
row = i * h
for j in range(gen_num[1]):
idx = i * gen_num[1] + j
print(idx)
tmp_list = [data[idx], pred[idx], label[idx]]
for k in range(3):
col = (j * 3 + k) * w
tmp = np.transpose(tmp_list[k], (1, 2, 0))
img[row: row+h, col: col+w] = tmp
img_file = os.path.join(self.log_dir, '%d_%s.jpg' % (self.step, name))
cv2.imwrite(img_file, img)