need_bias=True,
pad=pad,
act_fun='LeakyReLU')
if os.path.exists(os.path.join(opt.save_path, "%s_xnet.pth" % imgname)):
net = torch.load(os.path.join(opt.save_path, "%s_xnet.pth" % imgname))
net = net.type(dtype)
'''
k_net:
'''
n_k = 200
net_input_kernel = get_noise(n_k, INPUT, (1, 1)).type(dtype).detach()
net_input_kernel.squeeze_()
net_kernel = fcn(n_k, opt.kernel_size[0] * opt.kernel_size[1])
if os.path.exists(os.path.join(opt.save_path, "%s_knet.pth" % imgname)):
net_kernel = torch.load(
os.path.join(opt.save_path, "%s_knet.pth" % imgname))
net_kernel = net_kernel.type(dtype)
# Losses
mse = torch.nn.MSELoss().type(dtype)
L1 = torch.nn.L1Loss(reduction='sum').type(dtype)
tv_loss = TVLoss(tv_loss_weight=tv_weight)
# optimizer
optimizer = torch.optim.Adam([{
'params': net.parameters()
def deblur(input,
kernel_size,
output,
outputk=None,
sigma=0,
lr=0.01,
reg_noise_std=0.001,
num_iter=5000,
normalization=1):
INPUT = 'noise'
pad = 'reflection'
kernel_size = [kernel_size, kernel_size]
import iio
imgs = iio.read(input) / normalization
imgs = imgs.transpose((2, 0, 1))
y = np_to_torch(imgs).type(dtype)
img_size = imgs.shape
padh, padw = kernel_size[0] - 1, kernel_size[1] - 1
'''
x_net:
'''
input_depth = 8
net_input = get_noise(
input_depth, INPUT,
(img_size[1] + padh, img_size[2] + padw)).type(dtype).detach()
net = skip(input_depth,
3,
num_channels_down=[128, 128, 128, 128, 128],
num_channels_up=[128, 128, 128, 128, 128],
num_channels_skip=[16, 16, 16, 16, 16],
upsample_mode='bilinear',
need_sigmoid=True,
need_bias=True,
pad=pad,
act_fun='LeakyReLU')
net = net.type(dtype)
'''
k_net:
'''
n_k = 200
net_input_kernel = get_noise(n_k, INPUT, (1, 1)).type(dtype).detach()
net_input_kernel = net_input_kernel.squeeze()
net_kernel = fcn(n_k, kernel_size[0] * kernel_size[1])
net_kernel = net_kernel.type(dtype)
# Losses
mse = torch.nn.MSELoss().type(dtype)
L1 = torch.nn.L1Loss(reduction='sum').type(dtype)
lambda_ = 0.1 * sigma / normalization
tv_loss = TVLoss(tv_loss_weight=lambda_).type(dtype)
# optimizer
optimizer = torch.optim.Adam([{
'params': net.parameters()
}, {
'params': net_kernel.parameters(),
'lr': 1e-4
}],
lr=lr)
ml = [
int(num_iter * 2000 / 5000),
int(num_iter * 3000 / 5000),
int(num_iter * 4000 / 5000)
]
scheduler = MultiStepLR(optimizer, milestones=ml,
gamma=0.5) # learning rates
# initilization inputs
net_input_saved = net_input.detach().clone()
net_input_kernel_saved = net_input_kernel.detach().clone()
### start SelfDeblur
for step in tqdm(range(num_iter)):
# input regularization
net_input = net_input_saved + reg_noise_std * torch.zeros(
net_input_saved.shape).type_as(net_input_saved.data).normal_()
# change the learning rate
scheduler.step(step)
optimizer.zero_grad()
# get the network output
out_x = net(net_input)
out_k = net_kernel(net_input_kernel)
out_k_m = out_k.view(-1, 1, kernel_size[0], kernel_size[1])
out_y = nn.functional.conv2d(out_x,
out_k_m.expand((3, -1, -1, -1)),
padding=0,
bias=None,
groups=3)
total_loss = mse(out_y, y) + tv_loss(out_x)
total_loss.backward()
optimizer.step()
out_x_np = torch_to_np(out_x)
out_x_np = out_x_np.squeeze()
out_x_np = out_x_np[:, padh // 2:padh // 2 + img_size[1],
padw // 2:padw // 2 + img_size[2]]
out_x_np = out_x_np.transpose((1, 2, 0))
iio.write(output, out_x_np * normalization)
if outputk:
out_k_np = torch_to_np(out_k_m)
out_k_np = out_k_np.squeeze()
iio.write(outputk, out_k_np)