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train_syn.py
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from __future__ import division
from __future__ import print_function
import os, time, scipy.io, shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import glob
import re
import cv2
from utils import *
from model import *
def load_CRF():
CRF = scipy.io.loadmat('matdata/201_CRF_data.mat')
iCRF = scipy.io.loadmat('matdata/dorfCurvesInv.mat')
B_gl = CRF['B']
I_gl = CRF['I']
B_inv_gl = iCRF['invB']
I_inv_gl = iCRF['invI']
if os.path.exists('matdata/201_CRF_iCRF_function.mat')==0:
CRF_para = np.array(CRF_function_transfer(I_gl, B_gl))
iCRF_para = 1. / CRF_para
scipy.io.savemat('matdata/201_CRF_iCRF_function.mat', {'CRF':CRF_para, 'iCRF':iCRF_para})
else:
Bundle = scipy.io.loadmat('matdata/201_CRF_iCRF_function.mat')
CRF_para = Bundle['CRF']
iCRF_para = Bundle['iCRF']
return CRF_para, iCRF_para, I_gl, B_gl, I_inv_gl, B_inv_gl
def load_checkpoint(checkpoint_dir):
if os.path.exists(checkpoint_dir + 'checkpoint.pth.tar'):
# load existing model
model_info = torch.load(checkpoint_dir + 'checkpoint.pth.tar')
print('==> loading existing model:', checkpoint_dir + 'checkpoint.pth.tar')
model = CBDNet()
model.cuda()
model.load_state_dict(model_info['state_dict'])
optimizer = torch.optim.Adam(model.parameters())
optimizer.load_state_dict(model_info['optimizer'])
cur_epoch = model_info['epoch']
else:
# create model
model = CBDNet()
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
cur_epoch = 0
return model, optimizer, cur_epoch
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, checkpoint_dir + 'checkpoint.pth.tar')
if is_best:
shutil.copyfile(checkpoint_dir + 'checkpoint.pth.tar',checkpoint_dir + 'model_best.pth.tar')
def adjust_learning_rate(optimizer, epoch, lr_update_freq):
if not epoch % lr_update_freq and epoch:
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr'] * 0.1
return optimizer
if __name__ == '__main__':
input_dir = './dataset/synthetic/'
checkpoint_dir = './checkpoint/synthetic/'
result_dir = './result/synthetic/'
save_freq = 100
lr_update_freq = 100
CRF_para, iCRF_para, I_gl, B_gl, I_inv_gl, B_inv_gl = load_CRF()
train_fns = glob.glob(input_dir + '*.bmp')
origin_imgs = [None] * len(train_fns)
noise_imgs = [None] * len(train_fns)
noise_levels = [None] * len(train_fns)
for i in range(len(train_fns)):
origin_imgs[i] = []
noise_imgs[i] = []
noise_levels[i] = []
model, optimizer, cur_epoch = load_checkpoint(checkpoint_dir)
criterion = fixed_loss()
criterion = criterion.cuda()
for epoch in range(cur_epoch, 201):
cnt=0
losses = AverageMeter()
optimizer = adjust_learning_rate(optimizer, epoch, lr_update_freq)
model.train()
for ind in np.random.permutation(len(train_fns)):
train_fn = train_fns[ind]
if not len(origin_imgs[ind]):
origin_img = cv2.imread(train_fn)
origin_img = origin_img[:,:,::-1] / 255.0
origin_imgs[ind] = np.array(origin_img).astype('float32')
# re-add noise
if epoch % save_freq == 0:
noise_imgs[ind] = []
noise_levels[ind] = []
if len(noise_imgs[ind]) < 1:
noise_img, noise_level = AddRealNoise(origin_imgs[ind][:, :, :], CRF_para, iCRF_para, I_gl, B_gl, I_inv_gl, B_inv_gl)
noise_imgs[ind].append(noise_img)
noise_levels[ind].append(noise_level)
st = time.time()
for nind in np.random.permutation(len(noise_imgs[ind])):
temp_origin_img = origin_imgs[ind]
temp_noise_img = noise_imgs[ind][nind]
temp_noise_level = noise_levels[ind][nind]
if np.random.randint(2, size=1)[0] == 1:
temp_origin_img = np.flip(temp_origin_img, axis=1)
temp_noise_img = np.flip(temp_noise_img, axis=1)
temp_noise_level = np.flip(temp_noise_level, axis=1)
if np.random.randint(2, size=1)[0] == 1:
temp_origin_img = np.flip(temp_origin_img, axis=0)
temp_noise_img = np.flip(temp_noise_img, axis=0)
temp_noise_level = np.flip(temp_noise_level, axis=0)
if np.random.randint(2, size=1)[0] == 1:
temp_origin_img = np.transpose(temp_origin_img, (1, 0, 2))
temp_noise_img = np.transpose(temp_noise_img, (1, 0, 2))
temp_noise_level = np.transpose(temp_noise_level, (1, 0, 2))
temp_noise_img_chw = hwc_to_chw(temp_noise_img)
temp_origin_img_chw = hwc_to_chw(temp_origin_img)
temp_noise_level_chw = hwc_to_chw(temp_noise_level)
cnt += 1
st = time.time()
input_var = torch.from_numpy(temp_noise_img_chw.copy()).type(torch.FloatTensor).unsqueeze(0)
target_var = torch.from_numpy(temp_origin_img_chw.copy()).type(torch.FloatTensor).unsqueeze(0)
noise_level_var = torch.from_numpy(temp_noise_level_chw.copy()).type(torch.FloatTensor).unsqueeze(0)
input_var, target_var, noise_level_var = input_var.cuda(), target_var.cuda(), noise_level_var.cuda()
noise_level_est, output = model(input_var)
loss = criterion(output, target_var, noise_level_est, noise_level_var, 1)
losses.update(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('[{0}][{1}]\t'
'lr: {lr:.5f}\t'
'Loss: {loss.val:.4f} ({loss.avg:.4f})\t'
'Time: {time:.3f}'.format(
epoch, cnt,
lr=optimizer.param_groups[-1]['lr'],
loss=losses,
time=time.time()-st))
if epoch % save_freq == 0:
if not os.path.isdir(result_dir + '%04d'%epoch):
os.makedirs(result_dir + '%04d'%epoch)
output_np = output.squeeze().cpu().detach().numpy()
output_np = chw_to_hwc(np.clip(output_np, 0, 1))
temp = np.concatenate((temp_origin_img, temp_noise_img, output_np), axis=1)
scipy.misc.toimage(temp*255, high=255, low=0, cmin=0, cmax=255).save(result_dir + '%04d/train_%d_%d.jpg'%(epoch, ind, nind))
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict()}, is_best=0)