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train_fw_net.py
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train_fw_net.py
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# -*- coding: utf-8 -*-
"""
Python script for training FW-Net
@author: Ke Sun
@e-mail: sunk@mail.ustc.edu.cn
@Date: Feb 26, 2018
"""
import matplotlib
matplotlib.use('Agg')
import numpy as np
from PIL import Image
import caffe
from multiprocessing import Process, Queue, freeze_support
import logging
from random import *
from scipy.io import loadmat
import argparse
def cal_PSNR(pred,gt):
diff = np.array(gt,dtype=np.float32) - np.array(pred,dtype=np.float32)
mse = np.multiply(diff,diff).sum() / diff.size
psnr = 10*np.log10(255.*255./mse)
return psnr
def test_valid(net,sigma):
valid_img_path = '/data8/sunk/FW/deno_data/bsd68_noise_%d/' % sigma
valid_gt_path = '/data8/sunk/FW/deno_data/bsd68_gt/'
aver_psnr, loss = 0.0,0.0
psnr_L = []
num = 19
for i in xrange(1,num+1):
im_Name = 'im%04d' % i
valid_img = loadmat(valid_img_path+im_Name+'.mat')['z']
valid_gt = Image.open(valid_gt_path+('test%03d'%i)+'.png')
valid_gt = np.asarray(valid_gt,dtype=np.float32)
img_H = valid_img.shape[0]
img_W = valid_img.shape[1]
net.blobs['data'].reshape(1,1,img_H,img_W)
net.blobs['data'].data[...] = valid_img
net.blobs['label'].reshape(1,1,img_H,img_W)
net.blobs['label'].data[...] = valid_img
net.forward()
pred = net.blobs['map_fc'].data
pred = pred * 255.
pred[pred>255] = 255
pred[pred<0] = 0
pred = np.array(pred,dtype=np.int)
psnr = cal_PSNR(pred,valid_gt)
psnr_L.append(psnr)
aver_psnr = aver_psnr + psnr / num
psnr_set5 = np.sum(np.array(psnr_L)[0:5]) / 5.
psnr_set14 = np.sum(np.array(psnr_L)[5:19]) / 14
return psnr_L, aver_psnr, psnr_set5, psnr_set14
# randomly crop fixed-size patch for training
# gray-image
def rand_crop(img,gt,crop_H,crop_W,x=-1,y=-1):
ori_Img = np.array(img,dtype=np.float32).copy()
ori_gt = np.array(gt,dtype=np.float32).copy()
img_H = ori_Img.shape[0]
img_W = ori_Img.shape[1]
if x==-1 or y==-1:
x = randint(0,img_W-crop_W-1)
y = randint(0,img_H-crop_H-1)
crop_Img = ori_Img[y:y+crop_H,x:x+crop_W]
crop_gt = ori_gt[y:y+crop_H,x:x+crop_W]
return crop_Img,crop_gt
# generate training data
def genData(Data_Q,Label_Q,batchsize,crop_H,crop_W,sigma):
name_list = range(1,433)
shuffle(name_list)
data_L = np.zeros((batchsize,1,crop_H,crop_W))
target_L = np.zeros((batchsize,1,crop_H,crop_W))
count = 0
key = 0
while True:
if Data_Q.full() != True:
img = loadmat('/data8/sunk/FW/deno_data/BSD432_sgm_%d/im%04d.mat' % (sigma,name_list[key]))['z']
gt = loadmat('/data8/sunk/FW/deno_data/BSD432_gt/im%04d.mat' % name_list[key])['y']
crop_img,crop_gt = rand_crop(img,gt,crop_H,crop_W)
data_L[count,0,:,:] = crop_img
target_L[count,0,:,:] = crop_gt
count += 1
if count == batchsize:
Data_Q.put(data_L)
Label_Q.put(target_L)
count = 0
key += 1
if key == 432:
shuffle(name_list)
key = 0
def train(args):
# generate and store training data online
Data_Q = Queue(50)
Label_Q = Queue(50)
pData = Process(target=genData,
args=(Data_Q,Label_Q,args.batchsize, args.crop_H,args.crop_W, args.sigma))
pData.start()
caffe.set_mode_gpu()
caffe.set_device(args.GPU_ID)
logging.basicConfig(filename=args.log,level=logging.INFO)
solver = caffe.AdamSolver(args.solver)
# initialization
if args.init_model:
logging.info('loading'+args.init_model)
solver.net.copy_from(args.init_model)
logging.info('loaded')
solver.net.params['init_norm'][0].data[...] = args.init_p
solver.net.params['init_pool'][0].data[...] = args.init_p
norm_param_ul = ['norm_%d' % i for i in xrange(1,args.T)]
pool_param_ul = ['pool_%d' % i for i in xrange(1,args.T)]
for i in xrange(args.T-1):
solver.net.params[norm_param_ul[i]][0].data[...] = args.init_p
solver.net.params[pool_param_ul[i]][0].data[...] = args.init_p
solver.net.blobs['data'].reshape(args.batchsize,1,args.crop_H,args.crop_W)
solver.net.blobs['label'].reshape(args.batchsize,1,args.crop_H,args.crop_W)
# training process
epoch = args.num_epochs
epoch_iters = 750
step_sum = epoch_iters*epoch+1
step = 0
valid_acc = 0
aver_psnr = 0.0
best_psnr = 0.0
best_iter = 0
best_each_psnr = [0 for i in xrange(19)]
while step < step_sum:
if Data_Q.empty() != True:
# training
solver.net.blobs['data'].data[...] = Data_Q.get()
solver.net.blobs['label'].data[...] = Label_Q.get()
solver.step(1)
p = solver.net.params['init_norm'][0].data[...]
log_out = 'iter: %d p: %.4f loss: %f' % (step,p,solver.net.blobs['loss'].data)
logging.info(log_out)
step += 1
# evaluated on validation or test set.
if step % epoch_iters == 0:
psnr, aver_psnr, psnr_set5, psnr_set14 = test_valid(solver.net,args.sigma)
if (aver_psnr > best_psnr):
best_psnr = aver_psnr
best_each_psnr = psnr
best_iter = step
log_out = 'test: iter: %d aver_psnr/best_psnr %f/%f psnr_set5: %f psnr_set14: %f\n' % (step,aver_psnr,best_psnr,psnr_set5,psnr_set14)
logging.info(log_out)
log_psnr = 'test: iter: %d ' % step
log_best_psnr = 'best: iter: %d ' % best_iter
for j in xrange(19):
log_psnr = log_psnr + '%d: %f ' % (j,psnr[j-1])
log_best_psnr = log_best_psnr + '%d: %f ' % (j,best_each_psnr[j-1])
logging.info(log_psnr)
logging.info(log_best_psnr)
solver.net.blobs['data'].reshape(args.batchsize,1,args.crop_H,args.crop_W)
solver.net.blobs['label'].reshape(args.batchsize,1,args.crop_H,args.crop_W)
if __name__ == '__main__':
freeze_support()
parser = argparse.ArgumentParser(description="fw_denoising",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fw_parser = parser.add_argument_group('settings')
fw_parser.add_argument('--GPU_ID', type=int, default=0,
help='ID number of GPU card.')
fw_parser.add_argument('--solver', type=str,
help='the solver file, e.g. fw_deno_solver.prototxt')
fw_parser.add_argument('--T', type=int, default=12,
help='number of layers in the neural network.')
fw_parser.add_argument('--init_p', type=float, default=1.5,
help='the initial value of prior p')
fw_parser.add_argument('--num_epochs', type=int, default=100,
help='max num of epochs')
fw_parser.add_argument('--batchsize', type=int, default=64,
help='the batch size')
fw_parser.add_argument('--init_model', type=str, default=None,
help='the pre-trained model.')
fw_parser.add_argument('--crop_H', type=int, default=42,
help='the height of training patch.')
fw_parser.add_argument('--crop_W', type=int, default=42,
help='the width of training patch.')
fw_parser.add_argument('--log', type=str, default='fw_train.log',
help='the path of log file.')
fw_parser.add_argument('--sgm', type=int, default=25,
help='noise level: 15, 25 or 50.')
fw_args = parser.parse_args()
train(fw_args)