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224 lines
9.8 KiB
224 lines
9.8 KiB
# coding: utf-8
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from __future__ import print_function
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import os
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import time
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import random
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#from skimage import color
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from PIL import Image
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import tensorflow as tf
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import numpy as np
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from utils import *
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from model import *
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from glob import glob
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batch_size = 10
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patch_size = 48
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sess = tf.Session()
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#the input of decomposition net
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input_decom = tf.placeholder(tf.float32, [None, None, None, 3], name='input_decom')
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#the input of illumination adjustment net
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input_low_i = tf.placeholder(tf.float32, [None, None, None, 1], name='input_low_i')
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input_low_i_ratio = tf.placeholder(tf.float32, [None, None, None, 1], name='input_low_i_ratio')
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input_high_i = tf.placeholder(tf.float32, [None, None, None, 1], name='input_high_i')
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[R_decom, I_decom] = DecomNet_simple(input_decom)
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#the output of decomposition network
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decom_output_R = R_decom
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decom_output_I = I_decom
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#the output of illumination adjustment net
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output_i = Illumination_adjust_net(input_low_i, input_low_i_ratio)
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#define loss
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def grad_loss(input_i_low, input_i_high):
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x_loss = tf.square(gradient(input_i_low, 'x') - gradient(input_i_high, 'x'))
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y_loss = tf.square(gradient(input_i_low, 'y') - gradient(input_i_high, 'y'))
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grad_loss_all = tf.reduce_mean(x_loss + y_loss)
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return grad_loss_all
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loss_grad = grad_loss(output_i, input_high_i)
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loss_square = tf.reduce_mean(tf.square(output_i - input_high_i))# * ( 1 - input_low_r ))#* (1- input_low_i)))
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loss_adjust = loss_square + loss_grad
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lr = tf.placeholder(tf.float32, name='learning_rate')
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optimizer = tf.train.AdamOptimizer(learning_rate=lr, name='AdamOptimizer')
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var_Decom = [var for var in tf.trainable_variables() if 'DecomNet' in var.name]
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var_adjust = [var for var in tf.trainable_variables() if 'Illumination_adjust_net' in var.name]
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saver_adjust = tf.train.Saver(var_list=var_adjust)
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saver_Decom = tf.train.Saver(var_list = var_Decom)
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train_op_adjust = optimizer.minimize(loss_adjust, var_list = var_adjust)
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sess.run(tf.global_variables_initializer())
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print("[*] Initialize model successfully...")
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### load data
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### Based on the decomposition net, we first get the decomposed reflectance maps
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### and illumination maps, then train the adjust net.
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###train_data
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train_low_data = []
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train_high_data = []
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train_low_data_names = glob('./LOLdataset/our485/low/*.png')
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train_low_data_names.sort()
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train_high_data_names = glob('./LOLdataset/our485/high/*.png')
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train_high_data_names.sort()
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assert len(train_low_data_names) == len(train_high_data_names)
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print('[*] Number of training data: %d' % len(train_low_data_names))
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for idx in range(len(train_low_data_names)):
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low_im = load_images(train_low_data_names[idx])
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train_low_data.append(low_im)
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high_im = load_images(train_high_data_names[idx])
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train_high_data.append(high_im)
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pre_decom_checkpoint_dir = './checkpoint/decom_net_train/'
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ckpt_pre=tf.train.get_checkpoint_state(pre_decom_checkpoint_dir)
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if ckpt_pre:
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print('loaded '+ckpt_pre.model_checkpoint_path)
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saver_Decom.restore(sess,ckpt_pre.model_checkpoint_path)
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else:
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print('No pre_decom_net checkpoint!')
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#decomposed_low_r_data_480 = []
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decomposed_low_i_data_480 = []
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#decomposed_high_r_data_480 = []
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decomposed_high_i_data_480 = []
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for idx in range(len(train_low_data)):
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input_low = np.expand_dims(train_low_data[idx], axis=0)
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RR, II = sess.run([decom_output_R, decom_output_I], feed_dict={input_decom: input_low})
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RR0 = np.squeeze(RR)
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II0 = np.squeeze(II)
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print(RR0.shape, II0.shape)
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#decomposed_high_r_data_480.append(result_1_sq)
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decomposed_low_i_data_480.append(II0)
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for idx in range(len(train_high_data)):
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input_high = np.expand_dims(train_high_data[idx], axis=0)
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RR2, II2 = sess.run([decom_output_R, decom_output_I], feed_dict={input_decom: input_high})
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RR02 = np.squeeze(RR2)
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II02 = np.squeeze(II2)
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print(RR02.shape, II02.shape)
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#decomposed_high_r_data_480.append(result_1_sq)
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decomposed_high_i_data_480.append(II02)
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eval_adjust_low_i_data = decomposed_low_i_data_480[451:480]
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eval_adjust_high_i_data = decomposed_high_i_data_480[451:480]
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train_adjust_low_i_data = decomposed_low_i_data_480[0:450]
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train_adjust_high_i_data = decomposed_high_i_data_480[0:450]
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print('[*] Number of training data: %d' % len(train_adjust_high_i_data))
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learning_rate = 0.0001
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epoch = 2000
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eval_every_epoch = 200
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train_phase = 'adjustment'
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numBatch = len(train_adjust_low_i_data) // int(batch_size)
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train_op = train_op_adjust
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train_loss = loss_adjust
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saver = saver_adjust
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checkpoint_dir = './checkpoint/illumination_adjust_net_train/'
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if not os.path.isdir(checkpoint_dir):
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os.makedirs(checkpoint_dir)
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ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
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if ckpt:
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print('loaded '+ckpt.model_checkpoint_path)
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saver.restore(sess,ckpt.model_checkpoint_path)
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else:
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print("No adjustment net pre model!")
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start_step = 0
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start_epoch = 0
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iter_num = 0
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print("[*] Start training for phase %s, with start epoch %d start iter %d : " % (train_phase, start_epoch, iter_num))
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sample_dir = './illumination_adjust_net_train/'
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if not os.path.isdir(sample_dir):
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os.makedirs(sample_dir)
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start_time = time.time()
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image_id = 0
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for epoch in range(start_epoch, epoch):
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for batch_id in range(start_step, numBatch):
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batch_input_low_i_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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batch_input_high_i_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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batch_input_low_i = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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batch_input_high_i = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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input_low_i_rand = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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input_high_i_rand = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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input_low_i_rand_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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input_high_i_rand_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
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for patch_id in range(batch_size):
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i_low_data = train_adjust_low_i_data[image_id]
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i_low_expand = np.expand_dims(i_low_data, axis = 2)
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i_high_data = train_adjust_high_i_data[image_id]
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i_high_expand = np.expand_dims(i_high_data, axis = 2)
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h, w = train_adjust_low_i_data[image_id].shape
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x = random.randint(0, h - patch_size)
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y = random.randint(0, w - patch_size)
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i_low_data_crop = i_low_expand[x : x+patch_size, y : y+patch_size, :]
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i_high_data_crop = i_high_expand[x : x+patch_size, y : y+patch_size, :]
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rand_mode = np.random.randint(0, 7)
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batch_input_low_i[patch_id, :, :, :] = data_augmentation(i_low_data_crop , rand_mode)
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batch_input_high_i[patch_id, :, :, :] = data_augmentation(i_high_data_crop, rand_mode)
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ratio = np.mean(i_low_data_crop/(i_high_data_crop+0.0001))
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#print(ratio)
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i_low_data_ratio = np.ones([patch_size,patch_size])*(1/ratio+0.0001)
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i_low_ratio_expand = np.expand_dims(i_low_data_ratio , axis =2)
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i_high_data_ratio = np.ones([patch_size,patch_size])*(ratio)
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i_high_ratio_expand = np.expand_dims(i_high_data_ratio , axis =2)
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batch_input_low_i_ratio[patch_id, :, :, :] = i_low_ratio_expand
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batch_input_high_i_ratio[patch_id, :, :, :] = i_high_ratio_expand
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rand_mode = np.random.randint(0, 2)
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if rand_mode == 1:
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input_low_i_rand[patch_id, :, :, :] = batch_input_low_i[patch_id, :, :, :]
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input_high_i_rand[patch_id, :, :, :] = batch_input_high_i[patch_id, :, :, :]
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input_low_i_rand_ratio[patch_id, :, :, :] = batch_input_low_i_ratio[patch_id, :, :, :]
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input_high_i_rand_ratio[patch_id, :, :, :] = batch_input_high_i_ratio[patch_id, :, :, :]
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else:
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input_low_i_rand[patch_id, :, :, :] = batch_input_high_i[patch_id, :, :, :]
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input_high_i_rand[patch_id, :, :, :] = batch_input_low_i[patch_id, :, :, :]
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input_low_i_rand_ratio[patch_id, :, :, :] = batch_input_high_i_ratio[patch_id, :, :, :]
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input_high_i_rand_ratio[patch_id, :, :, :] = batch_input_low_i_ratio[patch_id, :, :, :]
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image_id = (image_id + 1) % len(train_adjust_low_i_data)
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_, loss = sess.run([train_op, train_loss], feed_dict={input_low_i: input_low_i_rand,input_low_i_ratio: input_low_i_rand_ratio,\
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input_high_i: input_high_i_rand, \
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lr: learning_rate})
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print("%s Epoch: [%2d] [%4d/%4d] time: %4.4f, loss: %.6f" \
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% (train_phase, epoch + 1, batch_id + 1, numBatch, time.time() - start_time, loss))
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iter_num += 1
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if (epoch + 1) % eval_every_epoch == 0:
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print("[*] Evaluating for phase %s / epoch %d..." % (train_phase, epoch + 1))
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for idx in range(10):
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rand_idx = idx#np.random.randint(26)
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input_uu_i = eval_adjust_low_i_data[rand_idx]
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input_low_eval_i = np.expand_dims(input_uu_i, axis=0)
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input_low_eval_ii = np.expand_dims(input_low_eval_i, axis=3)
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h, w = eval_adjust_low_i_data[idx].shape
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rand_ratio = np.random.random(1)*2
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input_uu_i_ratio = np.ones([h,w]) * rand_ratio
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input_low_eval_i_ratio = np.expand_dims(input_uu_i_ratio, axis=0)
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input_low_eval_ii_ratio = np.expand_dims(input_low_eval_i_ratio, axis=3)
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result_1 = sess.run(output_i, feed_dict={input_low_i: input_low_eval_ii, input_low_i_ratio: input_low_eval_ii_ratio})
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save_images(os.path.join(sample_dir, 'h_eval_%d_%d_%5f.png' % ( epoch + 1 , rand_idx + 1, rand_ratio)), input_uu_i, result_1)
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saver.save(sess, checkpoint_dir + 'model.ckpt')
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print("[*] Finish training for phase %s." % train_phase)
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