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train_real.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_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/real/'
checkpoint_dir = './checkpoint/real/'
result_dir = './result/real/'
ps = 512
save_freq = 100
lr_update_freq = 1000
train_fns = glob.glob(input_dir + 'Batch_*')
origin_imgs = [None] * len(train_fns)
noise_imgs = [None] * len(train_fns)
for i in range(len(train_fns)):
origin_imgs[i] = []
noise_imgs[i] = []
model, optimizer, cur_epoch = load_checkpoint(checkpoint_dir)
criterion = fixed_loss()
criterion = criterion.cuda()
for epoch in range(cur_epoch, 2001):
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]):
train_origin_fns = glob.glob(train_fn + '/*Reference.bmp')
train_noise_fns = glob.glob(train_fn + '/*Noisy.bmp')
origin_img = cv2.imread(train_origin_fns[0])
origin_img = origin_img[:,:,::-1] / 255.0
origin_imgs[ind] = np.array(origin_img).astype('float32')
for train_noise_fn in train_noise_fns:
noise_img = cv2.imread(train_noise_fn)
noise_img = noise_img[:,:,::-1] / 255.0
noise_img = np.array(noise_img).astype('float32')
noise_imgs[ind].append(noise_img)
st = time.time()
for nind in np.random.permutation(len(noise_imgs[ind])):
H = origin_imgs[ind].shape[0]
W = origin_imgs[ind].shape[1]
ps_temp = min(H, W, ps) - 1
xx = np.random.randint(0, W-ps_temp)
yy = np.random.randint(0, H-ps_temp)
temp_origin_img = origin_imgs[ind][yy:yy+ps_temp, xx:xx+ps_temp, :]
temp_noise_img = noise_imgs[ind][nind][yy:yy+ps_temp, xx:xx+ps_temp, :]
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)
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)
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_img_chw = hwc_to_chw(temp_noise_img)
temp_origin_img_chw = hwc_to_chw(temp_origin_img)
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)
input_var, target_var = input_var.cuda(), target_var.cuda()
noise_level_est, output = model(input_var)
loss = criterion(output, target_var, noise_level_est, 0, 0)
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)