-
Notifications
You must be signed in to change notification settings - Fork 59
/
adjustment_net_train.py
225 lines (187 loc) · 9.88 KB
/
adjustment_net_train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
# coding: utf-8
from __future__ import print_function
import os
import time
import random
#from skimage import color
from PIL import Image
#import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
from utils import *
from model import *
from glob import glob
batch_size = 10
patch_size = 48
sess = tf.Session()
#the input of decomposition net
input_decom = tf.placeholder(tf.float32, [None, None, None, 3], name='input_decom')
#the input of illumination adjustment net
input_low_i = tf.placeholder(tf.float32, [None, None, None, 1], name='input_low_i')
input_low_i_ratio = tf.placeholder(tf.float32, [None, None, None, 1], name='input_low_i_ratio')
input_high_i = tf.placeholder(tf.float32, [None, None, None, 1], name='input_high_i')
[R_decom, I_decom] = DecomNet_simple(input_decom)
#the output of decomposition network
decom_output_R = R_decom
decom_output_I = I_decom
#the output of illumination adjustment net
output_i = Illumination_adjust_net(input_low_i, input_low_i_ratio)
#define loss
def grad_loss(input_i_low, input_i_high):
x_loss = tf.square(gradient(input_i_low, 'x') - gradient(input_i_high, 'x'))
y_loss = tf.square(gradient(input_i_low, 'y') - gradient(input_i_high, 'y'))
grad_loss_all = tf.reduce_mean(x_loss + y_loss)
return grad_loss_all
loss_grad = grad_loss(output_i, input_high_i)
loss_square = tf.reduce_mean(tf.square(output_i - input_high_i))# * ( 1 - input_low_r ))#* (1- input_low_i)))
loss_adjust = loss_square + loss_grad
lr = tf.placeholder(tf.float32, name='learning_rate')
optimizer = tf.train.AdamOptimizer(learning_rate=lr, name='AdamOptimizer')
var_Decom = [var for var in tf.trainable_variables() if 'DecomNet' in var.name]
var_adjust = [var for var in tf.trainable_variables() if 'Illumination_adjust_net' in var.name]
saver_adjust = tf.train.Saver(var_list=var_adjust)
saver_Decom = tf.train.Saver(var_list = var_Decom)
train_op_adjust = optimizer.minimize(loss_adjust, var_list = var_adjust)
sess.run(tf.global_variables_initializer())
print("[*] Initialize model successfully...")
### load data
### Based on the decomposition net, we first get the decomposed reflectance maps
### and illumination maps, then train the adjust net.
###train_data
train_low_data = []
train_high_data = []
train_low_data_names = glob('./LOLdataset/our485/low/*.png')
train_low_data_names.sort()
train_high_data_names = glob('./LOLdataset/our485/high/*.png')
train_high_data_names.sort()
assert len(train_low_data_names) == len(train_high_data_names)
print('[*] Number of training data: %d' % len(train_low_data_names))
for idx in range(len(train_low_data_names)):
low_im = load_images(train_low_data_names[idx])
train_low_data.append(low_im)
high_im = load_images(train_high_data_names[idx])
train_high_data.append(high_im)
pre_decom_checkpoint_dir = './checkpoint/decom_net_retrain/'
ckpt_pre=tf.train.get_checkpoint_state(pre_decom_checkpoint_dir)
if ckpt_pre:
print('loaded '+ckpt_pre.model_checkpoint_path)
saver_Decom.restore(sess,ckpt_pre.model_checkpoint_path)
else:
print('No pre_decom_net checkpoint!')
#decomposed_low_r_data_480 = []
decomposed_low_i_data_480 = []
#decomposed_high_r_data_480 = []
decomposed_high_i_data_480 = []
for idx in range(len(train_low_data)):
input_low = np.expand_dims(train_low_data[idx], axis=0)
RR, II = sess.run([decom_output_R, decom_output_I], feed_dict={input_decom: input_low})
RR0 = np.squeeze(RR)
II0 = np.squeeze(II)
print(RR0.shape, II0.shape)
#decomposed_high_r_data_480.append(result_1_sq)
decomposed_low_i_data_480.append(II0)
for idx in range(len(train_high_data)):
input_high = np.expand_dims(train_high_data[idx], axis=0)
RR2, II2 = sess.run([decom_output_R, decom_output_I], feed_dict={input_decom: input_high})
RR02 = np.squeeze(RR2)
II02 = np.squeeze(II2)
print(RR02.shape, II02.shape)
#decomposed_high_r_data_480.append(result_1_sq)
decomposed_high_i_data_480.append(II02)
eval_adjust_low_i_data = decomposed_low_i_data_480[451:480]
eval_adjust_high_i_data = decomposed_high_i_data_480[451:480]
train_adjust_low_i_data = decomposed_low_i_data_480[0:450]
train_adjust_high_i_data = decomposed_high_i_data_480[0:450]
print('[*] Number of training data: %d' % len(train_adjust_high_i_data))
learning_rate = 0.0001
epoch = 2000
eval_every_epoch = 200
train_phase = 'adjustment'
numBatch = len(train_adjust_low_i_data) // int(batch_size)
train_op = train_op_adjust
train_loss = loss_adjust
saver = saver_adjust
checkpoint_dir = './checkpoint/illumination_adjust_net_retrain/'
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
else:
print("No adjustment net pre model!")
start_step = 0
start_epoch = 0
iter_num = 0
print("[*] Start training for phase %s, with start epoch %d start iter %d : " % (train_phase, start_epoch, iter_num))
sample_dir = './illumination_adjust_net_train/'
if not os.path.isdir(sample_dir):
os.makedirs(sample_dir)
start_time = time.time()
image_id = 0
for epoch in range(start_epoch, epoch):
for batch_id in range(start_step, numBatch):
batch_input_low_i_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
batch_input_high_i_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
batch_input_low_i = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
batch_input_high_i = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
input_low_i_rand = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
input_high_i_rand = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
input_low_i_rand_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
input_high_i_rand_ratio = np.zeros((batch_size, patch_size, patch_size, 1), dtype="float32")
for patch_id in range(batch_size):
i_low_data = train_adjust_low_i_data[image_id]
i_low_expand = np.expand_dims(i_low_data, axis = 2)
i_high_data = train_adjust_high_i_data[image_id]
i_high_expand = np.expand_dims(i_high_data, axis = 2)
h, w = train_adjust_low_i_data[image_id].shape
x = random.randint(0, h - patch_size)
y = random.randint(0, w - patch_size)
i_low_data_crop = i_low_expand[x : x+patch_size, y : y+patch_size, :]
i_high_data_crop = i_high_expand[x : x+patch_size, y : y+patch_size, :]
rand_mode = np.random.randint(0, 7)
batch_input_low_i[patch_id, :, :, :] = data_augmentation(i_low_data_crop , rand_mode)
batch_input_high_i[patch_id, :, :, :] = data_augmentation(i_high_data_crop, rand_mode)
ratio = np.mean(i_low_data_crop/(i_high_data_crop+0.0001))
#print(ratio)
i_low_data_ratio = np.ones([patch_size,patch_size])*(1/ratio+0.0001)
i_low_ratio_expand = np.expand_dims(i_low_data_ratio , axis =2)
i_high_data_ratio = np.ones([patch_size,patch_size])*(ratio)
i_high_ratio_expand = np.expand_dims(i_high_data_ratio , axis =2)
batch_input_low_i_ratio[patch_id, :, :, :] = i_low_ratio_expand
batch_input_high_i_ratio[patch_id, :, :, :] = i_high_ratio_expand
rand_mode = np.random.randint(0, 2)
if rand_mode == 1:
input_low_i_rand[patch_id, :, :, :] = batch_input_low_i[patch_id, :, :, :]
input_high_i_rand[patch_id, :, :, :] = batch_input_high_i[patch_id, :, :, :]
input_low_i_rand_ratio[patch_id, :, :, :] = batch_input_low_i_ratio[patch_id, :, :, :]
input_high_i_rand_ratio[patch_id, :, :, :] = batch_input_high_i_ratio[patch_id, :, :, :]
else:
input_low_i_rand[patch_id, :, :, :] = batch_input_high_i[patch_id, :, :, :]
input_high_i_rand[patch_id, :, :, :] = batch_input_low_i[patch_id, :, :, :]
input_low_i_rand_ratio[patch_id, :, :, :] = batch_input_high_i_ratio[patch_id, :, :, :]
input_high_i_rand_ratio[patch_id, :, :, :] = batch_input_low_i_ratio[patch_id, :, :, :]
image_id = (image_id + 1) % len(train_adjust_low_i_data)
_, 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,\
input_high_i: input_high_i_rand, \
lr: learning_rate})
print("%s Epoch: [%2d] [%4d/%4d] time: %4.4f, loss: %.6f" \
% (train_phase, epoch + 1, batch_id + 1, numBatch, time.time() - start_time, loss))
iter_num += 1
if (epoch + 1) % eval_every_epoch == 0:
print("[*] Evaluating for phase %s / epoch %d..." % (train_phase, epoch + 1))
for idx in range(10):
rand_idx = idx#np.random.randint(26)
input_uu_i = eval_adjust_low_i_data[rand_idx]
input_low_eval_i = np.expand_dims(input_uu_i, axis=0)
input_low_eval_ii = np.expand_dims(input_low_eval_i, axis=3)
h, w = eval_adjust_low_i_data[idx].shape
rand_ratio = np.random.random(1)*2
input_uu_i_ratio = np.ones([h,w]) * rand_ratio
input_low_eval_i_ratio = np.expand_dims(input_uu_i_ratio, axis=0)
input_low_eval_ii_ratio = np.expand_dims(input_low_eval_i_ratio, axis=3)
result_1 = sess.run(output_i, feed_dict={input_low_i: input_low_eval_ii, input_low_i_ratio: input_low_eval_ii_ratio})
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)
saver.save(sess, checkpoint_dir + 'model.ckpt')
print("[*] Finish training for phase %s." % train_phase)