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train_sr.py
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train_sr.py
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from __future__ import division
import os,time,cv2,scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import matplotlib.pyplot as plt
from networks import build_discriminator
from skimage.measure import compare_ssim as ssim
from skimage.measure import compare_psnr as psnr
from networks import *
from utils import *
import scipy.stats as st
import argparse,sys
parser = argparse.ArgumentParser()
parser.add_argument("--task", default="pre-trained", help="path to folder containing the model")
parser.add_argument("--data_dir", default="./Dataset/ISTD_Dataset/", help="path to real dataset")
parser.add_argument("--save_model_freq", default=1, type=int, help="frequency to save model")
parser.add_argument("--use_gpu", default=0, type=int, help="which gpu to use")
parser.add_argument("--use_da", default=0.5, type=float, help="[0~1], the precentage of synthesized dataset")
parser.add_argument("--is_hyper", default=1, type=int, help="use hypercolumn or not")
parser.add_argument("--is_training", default=1, help="training or testing")
parser.add_argument("--continue_training", action="store_true", help="search for checkpoint in the subfolder specified by `task` argument")
ARGS = parser.parse_args()
task='logs/'+ARGS.task
is_training=ARGS.is_training==1
continue_training=ARGS.continue_training
hyper=ARGS.is_hyper==1
current_best = 65535
maxepoch=151
EPS = 1e-12
channel = 64 # number of feature channels to build the model, set to 64
vgg_19_path = scipy.io.loadmat('./Models/imagenet-vgg-verydeep-19.mat')
test_w,test_h = 640,480
if ARGS.use_gpu<0:
os.environ['CUDA_VISIBLE_DEVICES'] = ''
else:
os.environ['CUDA_VISIBLE_DEVICES']=str(ARGS.use_gpu)
train_real_root=[ARGS.data_dir]
# set up the model and define the graph
with tf.variable_scope(tf.get_variable_scope()):
input=tf.placeholder(tf.float32,shape=[None,None,None,3])
target=tf.placeholder(tf.float32,shape=[None,None,None,3])
gtmask = tf.placeholder(tf.float32,shape=[None,None,None,1])
# build the model
shadow_free_image,predicted_mask=build_aggasatt_joint(input,channel,vgg_19_path=vgg_19_path)
loss_mask = tf.reduce_mean(tf.keras.losses.binary_crossentropy(gtmask,tf.nn.sigmoid(predicted_mask)))
# Perceptual Loss
loss_percep = compute_percep_loss(shadow_free_image, target,vgg_19_path=vgg_19_path)
# Adversarial Loss
with tf.variable_scope("discriminator"):
predict_real,pred_real_dict = build_discriminator(input,target)
with tf.variable_scope("discriminator", reuse=True):
predict_fake,pred_fake_dict = build_discriminator(input,shadow_free_image)
d_loss=(tf.reduce_mean(-(tf.log(predict_real + EPS) + tf.log(1 - predict_fake + EPS)))) * 0.5
g_loss=tf.reduce_mean(-tf.log(predict_fake + EPS))
loss = loss_percep*0.2 + loss_mask
train_vars = tf.trainable_variables()
d_vars = [var for var in train_vars if 'discriminator' in var.name]
g_vars = [var for var in train_vars if 'g_' in var.name]
g_opt=tf.train.AdamOptimizer(learning_rate=0.0002).minimize(loss*100+g_loss, var_list=g_vars) # optimizer for the generator
d_opt=tf.train.AdamOptimizer(learning_rate=0.0001).minimize(d_loss,var_list=d_vars) # optimizer for the discriminator
for var in tf.trainable_variables():
print("Listing trainable variables ... ")
print(var)
saver=tf.train.Saver(max_to_keep=None)
if not os.path.isdir(task):
os.makedirs(task)
######### Session #########
sess=tf.Session()
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state(task)
print("[i] contain checkpoint: ", ckpt)
if ckpt and continue_training:
saver_restore=tf.train.Saver([var for var in tf.trainable_variables()])
print('loaded '+ckpt.model_checkpoint_path)
saver_restore.restore(sess,ckpt.model_checkpoint_path)
# test doesn't need to load discriminator
elif not is_training:
saver_restore=tf.train.Saver([var for var in tf.trainable_variables() if 'discriminator' not in var.name])
print('loaded '+ckpt.model_checkpoint_path)
saver_restore.restore(sess,ckpt.model_checkpoint_path)
sys.stdout.flush()
if is_training:
# please follow the dataset directory setup in README
input_images_path=prepare_data(train_real_root,stage=['train_A']) # no reflection ground truth for real images
syn_images=prepare_data(train_real_root,stage=['synC'])
print("[i] Total %d training images, first path of real image is %s." % (len(input_images_path), input_images_path[0]))
num_train=len(input_images_path)+len(syn_images)
all_l=np.zeros(num_train, dtype=float)
all_percep=np.zeros(num_train, dtype=float)
all_grad=np.zeros(num_train, dtype=float)
all_g=np.zeros(num_train, dtype=float)
for epoch in range(1,maxepoch):
input_images_ids,target_images_ids=[None]*num_train,[None]*num_train
epoch_st = time.time()
if os.path.isdir("%s/%04d"%(task,epoch)):
continue
cnt=0
for id in np.random.permutation(num_train):
st=time.time()
if input_images_ids[id] is None:
_id=id%len(input_images_path)
running_idx = (epoch-1)*num_train+cnt
magic = np.random.rand()
current_img_id = ''
inputimg = cv2.imread(input_images_path[_id],-1)
neww=np.random.randint(256, 480) # w is the longer width[]
newh=round((neww/inputimg.shape[1])*inputimg.shape[0])
if magic < ARGS.use_da: #choose from fake images
is_syn = True
current_img_id = random.sample(syn_images,1)[0]
iminput,imtarget,maskgt = parpare_image_syn(current_img_id,(neww,newh),da=True,stage='synC')
else:
is_syn = False
current_img_id = input_images_path[_id]
iminput,imtarget,maskgt = parpare_image(current_img_id,(neww,newh),da=True,stage=['_M','_C','_B'])
# alternate training, update discriminator every two iterations
if cnt%2==0:
fetch_list=[d_opt]
# update D
_=sess.run(fetch_list,feed_dict={input:iminput,target:imtarget,gtmask:maskgt})
# update G
fetch_list=[g_opt,shadow_free_image,d_loss,g_loss,loss,loss_percep]
_,imoutput,current_d,current_g,current,current_percep=\
sess.run(fetch_list,feed_dict={input:iminput,target:imtarget,gtmask:maskgt})
all_l[id]=current
all_percep[id]=current_percep
all_g[id]=current_g
g_mean=np.mean(all_g[np.where(all_g)])
if running_idx% 500==0:
print("iter: %d %d || D: %.2f || G: %.2f %.2f || mean all: %.2f || percp: %.2f %.2f || time: %.2f"%
(epoch,cnt,current_d,current_g,g_mean,
np.mean(all_l[np.where(all_l)]),
current_percep, np.mean(all_percep[np.where(all_percep)]),
time.time()-st))
fileid = os.path.splitext(os.path.basename(input_images_path[_id]))[0]
imoutput=decode_image(imoutput)
iminput=decode_image(iminput)
imtarget=decode_image(imtarget)
cv2.imwrite("%s/%s_%s.png"%(task, running_idx, fileid),np.concatenate((iminput,imoutput,imtarget),axis=1))
cnt+=1
input_images_ids[id]=1.
target_images_ids[id]=1.
print('epoch %s use %s'%(epoch,time.time()-epoch_st))
# save model and images every epoch
if epoch % ARGS.save_model_freq == 0:
saver.save(sess,"%s/lasted_model.ckpt"%task)
sys.stdout.flush()
else:
subtask=task.replace('/','_') # if you want to save different testset separately
stage='test_A'
for val_path in prepare_data([ARGS.data_dir],stage=[stage]):
if not os.path.isfile(val_path):
continue
iminput,imtarget,maskgt = parpare_image(val_path,(test_w,test_h))
st=time.time()
imoutput=sess.run([shadow_free_image],feed_dict={input:iminput})
print("Test time %.3f for image %s"%(time.time()-st, val_path))
imtarget=decode_image(imtarget)
imoutput=decode_image(imoutput)
if not os.path.isdir("./results/%s"%(subtask+stage)):
os.makedirs("./results/%s"%(subtask+stage))
cv2.imwrite("./results/%s/%s.png"%(subtask+stage,os.path.splitext(os.path.basename(val_path))[0]),imoutput) # output transmission layer