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selfdeblur_lai.py
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selfdeblur_lai.py
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from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
from networks.skip import skip
from networks.fcn import *
import cv2
import torch
import torch.optim
import glob
from skimage.io import imread
from skimage.io import imsave
import warnings
from tqdm import tqdm
from torch.optim.lr_scheduler import MultiStepLR
from utils.common_utils import *
from SSIM import SSIM
parser = argparse.ArgumentParser()
parser.add_argument('--num_iter', type=int, default=5000, help='number of epochs of training')
parser.add_argument('--img_size', type=int, default=[256, 256], help='size of each image dimension')
parser.add_argument('--kernel_size', type=int, default=[21, 21], help='size of blur kernel [height, width]')
parser.add_argument('--data_path', type=str, default="datasets/lai/uniform_ycbcr/", help='path to blurry image')
parser.add_argument('--save_path', type=str, default="results/lai/uniform", help='path to save results')
parser.add_argument('--save_frequency', type=int, default=100, help='lfrequency to save results')
opt = parser.parse_args()
#print(opt)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor
warnings.filterwarnings("ignore")
files_source = glob.glob(os.path.join(opt.data_path, '*.png'))
files_source.sort()
save_path = opt.save_path
os.makedirs(save_path, exist_ok=True)
# start #image
for f in files_source:
INPUT = 'noise'
pad = 'reflection'
LR = 0.01
num_iter = opt.num_iter
reg_noise_std = 0.001
path_to_image = f
imgname = os.path.basename(f)
imgname = os.path.splitext(imgname)[0]
if imgname.find('kernel_01') != -1:
opt.kernel_size = [31, 31]
if imgname.find('kernel_02') != -1:
opt.kernel_size = [51, 51]
if imgname.find('kernel_03') != -1:
opt.kernel_size = [55, 55]
if imgname.find('kernel_04') != -1:
opt.kernel_size = [75, 75]
_, imgs = get_image(path_to_image, -1) # load image and convert to np.
y = np_to_torch(imgs).type(dtype)
img_size = imgs.shape
print(imgname)
# ######################################################################
padh, padw = opt.kernel_size[0]-1, opt.kernel_size[1]-1
opt.img_size[0], opt.img_size[1] = img_size[1]+padh, img_size[2]+padw
'''
x_net:
'''
input_depth = 8
net_input = get_noise(input_depth, INPUT, (opt.img_size[0], opt.img_size[1])).type(dtype)
net = skip( input_depth, 1,
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)
n_k = 200
net_input_kernel = get_noise(n_k, INPUT, (1, 1)).type(dtype)
net_input_kernel.squeeze_()
net_kernel = fcn(n_k, opt.kernel_size[0]*opt.kernel_size[1])
net_kernel = net_kernel.type(dtype)
# Losses
mse = torch.nn.MSELoss().type(dtype)
ssim = SSIM().type(dtype)
# optimizer
optimizer = torch.optim.Adam([{'params':net.parameters()},{'params':net_kernel.parameters(),'lr':1e-4}], lr=LR)
scheduler = MultiStepLR(optimizer, milestones=[2000, 3000, 4000], gamma=0.5) # learning rates
#
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_()
# net_input_kernel = net_input_kernel_saved + reg_noise_std*torch.zeros(net_input_kernel_saved.shape).type_as(net_input_kernel_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,opt.kernel_size[0],opt.kernel_size[1])
# print(out_k_m)
out_y = nn.functional.conv2d(out_x, out_k_m, padding=0, bias=None)
if step < 500:
total_loss = mse(out_y, y)
else:
total_loss = 1 - ssim(out_y, y)
total_loss.backward()
optimizer.step()
if (step+1) % opt.save_frequency == 0:
#print('Iteration %05d' %(step+1))
save_path = os.path.join(opt.save_path, '%s_x.png'%imgname)
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]]
imsave(save_path, out_x_np)
save_path = os.path.join(opt.save_path, '%s_k.png'%imgname)
out_k_np = torch_to_np(out_k_m)
out_k_np = out_k_np.squeeze()
out_k_np /= np.max(out_k_np)
imsave(save_path, out_k_np)
torch.save(net, os.path.join(opt.save_path, "%s_xnet.pth" % imgname))
torch.save(net_kernel, os.path.join(opt.save_path, "%s_knet.pth" % imgname))