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selfdeblur_lai_reproduce.py
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selfdeblur_lai_reproduce.py
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# coding: utf-8
from __future__ import print_function
import matplotlib.pyplot as plt
import argparse
import os
import numpy as np
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("--preprocess", type=bool, default=False, help='run prepare_data or not')
parser.add_argument('--num_iter', type=int, default=2, 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="imgs/lai/uniform_ycbcr/", help='path to blurry image')
parser.add_argument('--save_path', type=str, default="results/lai/uniform_reproduce/", help='path to save results')
parser.add_argument('--save_frequency', type=int, default=1, help='lfrequency to save results')
opt = parser.parse_args()
#print(opt)
#os.environ['CUDA_VISIBLE_DEVICES'] = '1'
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 = 0.0001
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 = torch.load(os.path.join(opt.save_path, "%s_xnet.pth" % imgname))
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 = 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)
ssim = SSIM().type(dtype)
# optimizer
optimizer = torch.optim.Adam([{'params':net.parameters()},{'params':net_kernel.parameters(),'lr':0e-4}], lr=LR)
scheduler = MultiStepLR(optimizer, milestones=[700, 800, 900], 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_()
# 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 < 0:
total_loss = mse(out_y, y)
else:
total_loss = 1 - ssim(out_y, y) # + tv_loss(out_x) #+ tv_loss2(out_k_m)
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]]
#out_x_np = np.uint8(out_x_np*255)
#cv2.imwrite(save_path, out_x_np)
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))