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VDSR.py
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VDSR.py
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import os, glob, re, signal, sys, argparse, threading, time
from random import shuffle
import random
import tensorflow as tf
from PIL import Image
import numpy as np
import scipy.io
from MODEL import model
from PSNR import psnr
from TEST import test_VDSR
#from MODEL_FACTORIZED import model_factorized
DATA_PATH = "./data/train/"
IMG_SIZE = (41, 41)
BATCH_SIZE = 64
BASE_LR = 0.0001
LR_RATE = 0.1
LR_STEP_SIZE = 120
MAX_EPOCH = 120
USE_QUEUE_LOADING = True
parser = argparse.ArgumentParser()
parser.add_argument("--model_path")
args = parser.parse_args()
model_path = args.model_path
TEST_DATA_PATH = "./data/test/"
def get_train_list(data_path):
l = glob.glob(os.path.join(data_path,"*"))
print len(l)
l = [f for f in l if re.search("^\d+.mat$", os.path.basename(f))]
print len(l)
train_list = []
for f in l:
if os.path.exists(f):
if os.path.exists(f[:-4]+"_2.mat"): train_list.append([f, f[:-4]+"_2.mat"])
if os.path.exists(f[:-4]+"_3.mat"): train_list.append([f, f[:-4]+"_3.mat"])
if os.path.exists(f[:-4]+"_4.mat"): train_list.append([f, f[:-4]+"_4.mat"])
return train_list
def get_image_batch(train_list,offset,batch_size):
target_list = train_list[offset:offset+batch_size]
input_list = []
gt_list = []
cbcr_list = []
for pair in target_list:
input_img = scipy.io.loadmat(pair[1])['patch']
gt_img = scipy.io.loadmat(pair[0])['patch']
input_list.append(input_img)
gt_list.append(gt_img)
input_list = np.array(input_list)
input_list.resize([BATCH_SIZE, IMG_SIZE[1], IMG_SIZE[0], 1])
gt_list = np.array(gt_list)
gt_list.resize([BATCH_SIZE, IMG_SIZE[1], IMG_SIZE[0], 1])
return input_list, gt_list, np.array(cbcr_list)
def get_test_image(test_list, offset, batch_size):
target_list = test_list[offset:offset+batch_size]
input_list = []
gt_list = []
for pair in target_list:
mat_dict = scipy.io.loadmat(pair[1])
input_img = None
if mat_dict.has_key("img_2"): input_img = mat_dict["img_2"]
elif mat_dict.has_key("img_3"): input_img = mat_dict["img_3"]
elif mat_dict.has_key("img_4"): input_img = mat_dict["img_4"]
else: continue
gt_img = scipy.io.loadmat(pair[0])['img_raw']
input_list.append(input_img[:,:,0])
gt_list.append(gt_img[:,:,0])
return input_list, gt_list
if __name__ == '__main__':
train_list = get_train_list(DATA_PATH)
if not USE_QUEUE_LOADING:
print "not use queue loading, just sequential loading..."
### WITHOUT ASYNCHRONOUS DATA LOADING ###
train_input = tf.placeholder(tf.float32, shape=(BATCH_SIZE, IMG_SIZE[0], IMG_SIZE[1], 1))
train_gt = tf.placeholder(tf.float32, shape=(BATCH_SIZE, IMG_SIZE[0], IMG_SIZE[1], 1))
### WITHOUT ASYNCHRONOUS DATA LOADING ###
else:
print "use queue loading"
### WITH ASYNCHRONOUS DATA LOADING ###
train_input_single = tf.placeholder(tf.float32, shape=(IMG_SIZE[0], IMG_SIZE[1], 1))
train_gt_single = tf.placeholder(tf.float32, shape=(IMG_SIZE[0], IMG_SIZE[1], 1))
q = tf.FIFOQueue(10000, [tf.float32, tf.float32], [[IMG_SIZE[0], IMG_SIZE[1], 1], [IMG_SIZE[0], IMG_SIZE[1], 1]])
enqueue_op = q.enqueue([train_input_single, train_gt_single])
train_input, train_gt = q.dequeue_many(BATCH_SIZE)
### WITH ASYNCHRONOUS DATA LOADING ###
shared_model = tf.make_template('shared_model', model)
#train_output, weights = model(train_input)
train_output, weights = shared_model(train_input)
loss = tf.reduce_sum(tf.nn.l2_loss(tf.subtract(train_output, train_gt)))
for w in weights:
loss += tf.nn.l2_loss(w)*1e-4
tf.summary.scalar("loss", loss)
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(BASE_LR, global_step*BATCH_SIZE, len(train_list)*LR_STEP_SIZE, LR_RATE, staircase=True)
tf.summary.scalar("learning rate", learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)#tf.train.MomentumOptimizer(learning_rate, 0.9)
opt = optimizer.minimize(loss, global_step=global_step)
saver = tf.train.Saver(weights, max_to_keep=0)
shuffle(train_list)
config = tf.ConfigProto()
#config.operation_timeout_in_ms=10000
with tf.Session(config=config) as sess:
#TensorBoard open log with "tensorboard --logdir=logs"
if not os.path.exists('logs'):
os.mkdir('logs')
merged = tf.summary.merge_all()
file_writer = tf.summary.FileWriter('logs', sess.graph)
tf.initialize_all_variables().run()
if model_path:
print "restore model..."
saver.restore(sess, model_path)
print "Done"
### WITH ASYNCHRONOUS DATA LOADING ###
def load_and_enqueue(coord, file_list, enqueue_op, train_input_single, train_gt_single, idx=0, num_thread=1):
count = 0;
length = len(file_list)
try:
while not coord.should_stop():
i = count % length;
input_img = scipy.io.loadmat(file_list[i][1])['patch'].reshape([IMG_SIZE[0], IMG_SIZE[1], 1])
gt_img = scipy.io.loadmat(file_list[i][0])['patch'].reshape([IMG_SIZE[0], IMG_SIZE[1], 1])
sess.run(enqueue_op, feed_dict={train_input_single:input_img, train_gt_single:gt_img})
count+=1
except Exception as e:
print "stopping...", idx, e
### WITH ASYNCHRONOUS DATA LOADING ###
threads = []
def signal_handler(signum,frame):
sess.run(q.close(cancel_pending_enqueues=True))
coord.request_stop()
coord.join(threads)
print "Done"
sys.exit(1)
original_sigint = signal.getsignal(signal.SIGINT)
signal.signal(signal.SIGINT, signal_handler)
if USE_QUEUE_LOADING:
# create threads
num_thread=20
coord = tf.train.Coordinator()
for i in range(num_thread):
length = len(train_list)/num_thread
t = threading.Thread(target=load_and_enqueue, args=(coord, train_list[i*length:(i+1)*length],enqueue_op, train_input_single, train_gt_single, i, num_thread))
threads.append(t)
t.start()
print "num thread:" , len(threads)
for epoch in xrange(0, MAX_EPOCH):
max_step=len(train_list)//BATCH_SIZE
for step in range(max_step):
_,l,output,lr, g_step, summary = sess.run([opt, loss, train_output, learning_rate, global_step, merged])
print "[epoch %2.4f] loss %.4f\t lr %.5f"%(epoch+(float(step)*BATCH_SIZE/len(train_list)), np.sum(l)/BATCH_SIZE, lr)
file_writer.add_summary(summary, step+epoch*max_step)
#print "[epoch %2.4f] loss %.4f\t lr %.5f\t norm %.2f"%(epoch+(float(step)*BATCH_SIZE/len(train_list)), np.sum(l)/BATCH_SIZE, lr, norm)
saver.save(sess, "./checkpoints/VDSR_adam_epoch_%03d.ckpt" % epoch ,global_step=global_step)
else:
for epoch in xrange(0, MAX_EPOCH):
for step in range(len(train_list)//BATCH_SIZE):
offset = step*BATCH_SIZE
input_data, gt_data, cbcr_data = get_image_batch(train_list, offset, BATCH_SIZE)
feed_dict = {train_input: input_data, train_gt: gt_data}
_,l,output,lr, g_step = sess.run([opt, loss, train_output, learning_rate, global_step], feed_dict=feed_dict)
print "[epoch %2.4f] loss %.4f\t lr %.5f"%(epoch+(float(step)*BATCH_SIZE/len(train_list)), np.sum(l)/BATCH_SIZE, lr)
del input_data, gt_data, cbcr_data
saver.save(sess, "./checkpoints/VDSR_const_clip_0.01_epoch_%03d.ckpt" % epoch ,global_step=global_step)