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main_gan_init.py
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main_gan_init.py
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#! /usr/bin/python
# -*- coding: utf8 -*-
#import matplotlib.pyplot as plt
#import cv2
import os, time, pickle, random, time
from datetime import datetime
import numpy as np
from time import localtime, strftime
import logging
import tensorflow as tf
import tensorlayer as tl
from model import *
from utils import *
from config import config, log_config
###====================== HYPER-PARAMETERS ===========================###
## Adam
batch_size = config.TRAIN.batch_size
lr_init = config.TRAIN.lr_init
beta1 = config.TRAIN.beta1
## initialize G
n_epoch_init = 20
lr_decay = config.TRAIN.lr_decay
decay_every = 17
patch_size_l = 74
patch_size_h = 296
ni = int(np.sqrt(batch_size))
def modcrop(imgs, modulo):
tmpsz = imgs.shape
sz = tmpsz[0:2]
h = sz[0] - sz[0]%modulo
w = sz[1] - sz[1]%modulo
imgs = imgs[0:h+1, 0:w+1,:]
return imgs
def read_all_imgs(img_list, path='', n_threads=32):
""" Returns all images in array by given path and name of each image file. """
imgs = []
for idx in range(0, len(img_list), n_threads):
b_imgs_list = img_list[idx : idx + n_threads]
b_imgs = tl.prepro.threading_data(b_imgs_list, fn=get_imgs_fn, path=path)
# print(b_imgs.shape)
imgs.extend(b_imgs)
print('read %d from %s' % (len(imgs), path))
return imgs
def train():
## create folders to save result images and trained model
checkpoint_dir = config.TRAIN.checkpoint
tl.files.exists_or_mkdir(checkpoint_dir)
###====================== PRE-LOAD DATA ===========================###
train_hr_img_list = sorted(tl.files.load_file_list(path=config.TRAIN.hr_img_path, regx='.*.png', printable=False))
train_lr_img_list = sorted(tl.files.load_file_list(path=config.TRAIN.lr_img_path, regx='.*.png', printable=False))
valid_hr_img_list = sorted(tl.files.load_file_list(path=config.VALID.hr_img_path, regx='.*.bmp', printable=False))
valid_lr_img_list = sorted(tl.files.load_file_list(path=config.VALID.lr_img_path, regx='.*.bmp', printable=False))
###========================== DEFINE MODEL ============================###
## train inference
t_image = tf.placeholder('float32', [batch_size, patch_size_l, patch_size_l, 3], name='t_image_input')
t_target_image = tf.placeholder('float32', [batch_size, patch_size_h, patch_size_h, 3], name='t_target_image')
net_g= SRGAN_g(t_image, is_train=True, reuse=False)
## test inference
t_sample_image = tf.placeholder('float32', [5, 56, 56, 3], name='t_sample_image')
net_g_test = SRGAN_g(t_sample_image, is_train=False, reuse=True)
# ###========================== DEFINE TRAIN OPS ==========================###
mse_loss = tl.cost.mean_squared_error(net_g.outputs , t_target_image, is_mean=True)
tf.summary.scalar('mse_loss', mse_loss)
merged = tf.summary.merge_all()
g_vars = tl.layers.get_variables_with_name('SRGAN_g', True, True)
with tf.variable_scope('learning_rate'):
lr_v = tf.Variable(lr_init, trainable=False)
## Pretrain
g_optim_init_= tf.train.AdamOptimizer(lr_v, beta1=beta1)
g_optim_init = g_optim_init_.minimize(mse_loss, var_list=g_vars)
###========================== RESTORE MODEL =============================###
sess = tf.Session(config=tf.ConfigProto( log_device_placement=False))
train_writer = tf.summary.FileWriter(config.TRAIN.summary_g,sess.graph)
train_writer.add_graph(sess.graph)
tl.layers.initialize_global_variables(sess)
if tl.files.load_and_assign_npz(sess=sess, name=checkpoint_dir+'/g_{}.npz'.format('SRFeat'), network=net_g) is False:
tl.files.load_and_assign_npz(sess=sess, name=checkpoint_dir+'/g_{}_init.npz'.format('SRFeat'), network=net_g)
###============================= TRAINING ===============================###
## use first `batch_size` of train set to have a quick test during training
sample_imgs = read_all_imgs(valid_hr_img_list[0:5], path=config.VALID.hr_img_path, n_threads=5) # if no pre-load train set
sample_imgs_384 = tl.prepro.threading_data(sample_imgs, fn=scale_imgs_fn)
sample_imgs_96 = read_all_imgs(valid_hr_img_list[0:5], path=config.VALID.lr_img_path, n_threads=5)
sample_imgs_96 = tl.prepro.threading_data(sample_imgs_96, fn=scale_imgs_fn)
val_mset = tl.cost.mean_squared_error(net_g_test.outputs , sample_imgs_384, is_mean=True)
val_summary = tf.summary.scalar('val_mse', val_mset)
###========================= initialize G ====================###
## fixed learning rate
sess.run(tf.assign(lr_v, lr_init))
lr_hr_list = list(zip(train_hr_img_list,train_lr_img_list))
random.shuffle(lr_hr_list)
train_hr_img_list, train_lr_img_list = zip(*lr_hr_list)
i =0
print(" ** fixed learning rate: %f (for init G)" % lr_init)
for epoch in range(0, n_epoch_init+1):
# epoch_time = time.time()
total_mse_loss, n_iter = 0, 0
if epoch !=0 and (epoch % decay_every == 0):
new_lr_decay = lr_decay ** (epoch // decay_every)
sess.run(tf.assign(lr_v, lr_init * new_lr_decay))
log = " ** new learning rate: %f (for GAN)" % (lr_init * new_lr_decay)
print(log)
elif epoch == 0:
sess.run(tf.assign(lr_v, lr_init))
log = " ** init lr: %f decay_every_init: %d, lr_decay: %f (for GAN)" % (lr_init, decay_every, lr_decay)
print(log)
## If your machine cannot load all images into memory, you should use
## this one to load batch of images while training.
# random.shuffle(train_hr_img_list)
# for idx in range(0, len(train_hr_img_list), batch_size):
for idx in range(0, len(train_hr_img_list) -batch_size , batch_size):
step_time = time.time()
b_imgs_list = train_hr_img_list[idx : idx + batch_size]
b_imgs_list_lr = train_lr_img_list[idx : idx + batch_size]
# print(b_imgs_list)
b_imgs = tl.prepro.threading_data(b_imgs_list, fn=get_imgs_fn, path=config.TRAIN.hr_img_path)
b_imgs_96 = tl.prepro.threading_data(b_imgs_list_lr, fn=get_imgs_fn, path=config.TRAIN.lr_img_path)
b_imgs_384 = tl.prepro.threading_data(b_imgs, fn=scale_imgs_fn)
b_imgs_96 = tl.prepro.threading_data(b_imgs_96, fn=scale_imgs_fn)
## If your machine have enough memory, please pre-load the whole train set.
# for idx in range(0, len(train_hr_imgs), batch_size):
# step_time = time.time()
# b_imgs_384 = tl.prepro.threading_data(
# train_hr_imgs[idx : idx + batch_size],
# fn=crop_sub_imgs_fn, is_random=True,is_thread=True, patch_size=patch_size_h)
# b_imgs_96 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn,patch_size=patch_size_l)
#
# b_imgs_192 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn2,patch_size=patch_size_l*2)
## update G
summary,errM, out, _ = sess.run([merged,mse_loss,net_g.outputs, g_optim_init], {t_image: b_imgs_96, t_target_image: b_imgs_384})
train_writer.add_summary(summary,i)
print("Epoch [%2d/%2d] %4d time: %4.4fs, mse: %.8f " % (epoch, n_epoch_init, n_iter, time.time() - step_time, errM))
total_mse_loss += errM
n_iter += 1
## quick evaluation on train set
if (i != 0) and (i % 20 == 0):
out,val_,val_summ = sess.run([net_g_test.outputs,val_mset,val_summary], {t_sample_image: sample_imgs_96})#; print('gen sub-image:', out.shape, out.min(), out.max())
train_writer.add_summary(val_summ,i)
print("validate")
# save model
if (i != 0) and (i % 100 == 0):
tl.files.save_npz(net_g.all_params, name=checkpoint_dir+'/g_{}_init.npz'.format('SRFeat'), sess=sess)
i= i+1
train_writer.close()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='srgan')
parser.add_argument('--set',type=str,default='Set5')
args = parser.parse_args()
tl.global_flag['model'] = args.model
tl.global_flag['set'] = args.set
train()