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model.py
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model.py
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from __future__ import print_function
import os
import time
import random
from PIL import Image
import tensorflow as tf
import numpy as np
from utils import *
def concat(layers):
return tf.concat(layers, axis=3)
def DecomNet(input_im, layer_num, channel=64, kernel_size=3):
input_max = tf.reduce_max(input_im, axis=3, keepdims=True)
input_im = concat([input_max, input_im])
with tf.variable_scope('DecomNet', reuse=tf.AUTO_REUSE):
conv = tf.layers.conv2d(input_im, channel, kernel_size * 3, padding='same', activation=None, name="shallow_feature_extraction")
for idx in range(layer_num):
conv = tf.layers.conv2d(conv, channel, kernel_size, padding='same', activation=tf.nn.relu, name='activated_layer_%d' % idx)
conv = tf.layers.conv2d(conv, 4, kernel_size, padding='same', activation=None, name='recon_layer')
R = tf.sigmoid(conv[:,:,:,0:3])
L = tf.sigmoid(conv[:,:,:,3:4])
return R, L
def RelightNet(input_L, input_R, channel=64, kernel_size=3):
input_im = concat([input_R, input_L])
with tf.variable_scope('RelightNet'):
conv0 = tf.layers.conv2d(input_im, channel, kernel_size, padding='same', activation=None)
conv1 = tf.layers.conv2d(conv0, channel, kernel_size, strides=2, padding='same', activation=tf.nn.relu)
conv2 = tf.layers.conv2d(conv1, channel, kernel_size, strides=2, padding='same', activation=tf.nn.relu)
conv3 = tf.layers.conv2d(conv2, channel, kernel_size, strides=2, padding='same', activation=tf.nn.relu)
up1 = tf.image.resize_nearest_neighbor(conv3, (tf.shape(conv2)[1], tf.shape(conv2)[2]))
deconv1 = tf.layers.conv2d(up1, channel, kernel_size, padding='same', activation=tf.nn.relu) + conv2
up2 = tf.image.resize_nearest_neighbor(deconv1, (tf.shape(conv1)[1], tf.shape(conv1)[2]))
deconv2= tf.layers.conv2d(up2, channel, kernel_size, padding='same', activation=tf.nn.relu) + conv1
up3 = tf.image.resize_nearest_neighbor(deconv2, (tf.shape(conv0)[1], tf.shape(conv0)[2]))
deconv3 = tf.layers.conv2d(up3, channel, kernel_size, padding='same', activation=tf.nn.relu) + conv0
deconv1_resize = tf.image.resize_nearest_neighbor(deconv1, (tf.shape(deconv3)[1], tf.shape(deconv3)[2]))
deconv2_resize = tf.image.resize_nearest_neighbor(deconv2, (tf.shape(deconv3)[1], tf.shape(deconv3)[2]))
feature_gather = concat([deconv1_resize, deconv2_resize, deconv3])
feature_fusion = tf.layers.conv2d(feature_gather, channel, 1, padding='same', activation=None)
output = tf.layers.conv2d(feature_fusion, 1, 3, padding='same', activation=None)
return output
class lowlight_enhance(object):
def __init__(self, sess):
self.sess = sess
self.DecomNet_layer_num = 5
# build the model
self.input_low = tf.placeholder(tf.float32, [None, None, None, 3], name='input_low')
self.input_high = tf.placeholder(tf.float32, [None, None, None, 3], name='input_high')
[R_low, I_low] = DecomNet(self.input_low, layer_num=self.DecomNet_layer_num)
[R_high, I_high] = DecomNet(self.input_high, layer_num=self.DecomNet_layer_num)
I_delta = RelightNet(I_low, R_low)
I_low_3 = concat([I_low, I_low, I_low])
I_high_3 = concat([I_high, I_high, I_high])
I_delta_3 = concat([I_delta, I_delta, I_delta])
self.output_R_low = R_low
self.output_I_low = I_low_3
self.output_I_delta = I_delta_3
self.output_S = R_low * I_delta_3
# loss
self.recon_loss_low = tf.reduce_mean(tf.abs(R_low * I_low_3 - self.input_low))
self.recon_loss_high = tf.reduce_mean(tf.abs(R_high * I_high_3 - self.input_high))
self.recon_loss_mutal_low = tf.reduce_mean(tf.abs(R_high * I_low_3 - self.input_low))
self.recon_loss_mutal_high = tf.reduce_mean(tf.abs(R_low * I_high_3 - self.input_high))
self.equal_R_loss = tf.reduce_mean(tf.abs(R_low - R_high))
self.relight_loss = tf.reduce_mean(tf.abs(R_low * I_delta_3 - self.input_high))
self.Ismooth_loss_low = self.smooth(I_low, R_low)
self.Ismooth_loss_high = self.smooth(I_high, R_high)
self.Ismooth_loss_delta = self.smooth(I_delta, R_low)
self.loss_Decom = self.recon_loss_low + self.recon_loss_high + 0.001 * self.recon_loss_mutal_low + 0.001 * self.recon_loss_mutal_high + 0.1 * self.Ismooth_loss_low + 0.1 * self.Ismooth_loss_high + 0.01 * self.equal_R_loss
self.loss_Relight = self.relight_loss + 3 * self.Ismooth_loss_delta
self.lr = tf.placeholder(tf.float32, name='learning_rate')
optimizer = tf.train.AdamOptimizer(self.lr, name='AdamOptimizer')
self.var_Decom = [var for var in tf.trainable_variables() if 'DecomNet' in var.name]
self.var_Relight = [var for var in tf.trainable_variables() if 'RelightNet' in var.name]
self.train_op_Decom = optimizer.minimize(self.loss_Decom, var_list = self.var_Decom)
self.train_op_Relight = optimizer.minimize(self.loss_Relight, var_list = self.var_Relight)
self.sess.run(tf.global_variables_initializer())
self.saver_Decom = tf.train.Saver(var_list = self.var_Decom)
self.saver_Relight = tf.train.Saver(var_list = self.var_Relight)
print("[*] Initialize model successfully...")
def gradient(self, input_tensor, direction):
self.smooth_kernel_x = tf.reshape(tf.constant([[0, 0], [-1, 1]], tf.float32), [2, 2, 1, 1])
self.smooth_kernel_y = tf.transpose(self.smooth_kernel_x, [1, 0, 2, 3])
if direction == "x":
kernel = self.smooth_kernel_x
elif direction == "y":
kernel = self.smooth_kernel_y
return tf.abs(tf.nn.conv2d(input_tensor, kernel, strides=[1, 1, 1, 1], padding='SAME'))
def ave_gradient(self, input_tensor, direction):
return tf.layers.average_pooling2d(self.gradient(input_tensor, direction), pool_size=3, strides=1, padding='SAME')
def smooth(self, input_I, input_R):
input_R = tf.image.rgb_to_grayscale(input_R)
return tf.reduce_mean(self.gradient(input_I, "x") * tf.exp(-10 * self.ave_gradient(input_R, "x")) + self.gradient(input_I, "y") * tf.exp(-10 * self.ave_gradient(input_R, "y")))
def evaluate(self, epoch_num, eval_low_data, sample_dir, train_phase):
print("[*] Evaluating for phase %s / epoch %d..." % (train_phase, epoch_num))
for idx in range(len(eval_low_data)):
input_low_eval = np.expand_dims(eval_low_data[idx], axis=0)
if train_phase == "Decom":
result_1, result_2 = self.sess.run([self.output_R_low, self.output_I_low], feed_dict={self.input_low: input_low_eval})
if train_phase == "Relight":
result_1, result_2 = self.sess.run([self.output_S, self.output_I_delta], feed_dict={self.input_low: input_low_eval})
save_images(os.path.join(sample_dir, 'eval_%s_%d_%d.png' % (train_phase, idx + 1, epoch_num)), result_1, result_2)
def train(self, train_low_data, train_high_data, eval_low_data, batch_size, patch_size, epoch, lr, sample_dir, ckpt_dir, eval_every_epoch, train_phase):
assert len(train_low_data) == len(train_high_data)
numBatch = len(train_low_data) // int(batch_size)
# load pretrained model
if train_phase == "Decom":
train_op = self.train_op_Decom
train_loss = self.loss_Decom
saver = self.saver_Decom
elif train_phase == "Relight":
train_op = self.train_op_Relight
train_loss = self.loss_Relight
saver = self.saver_Relight
load_model_status, global_step = self.load(saver, ckpt_dir)
if load_model_status:
iter_num = global_step
start_epoch = global_step // numBatch
start_step = global_step % numBatch
print("[*] Model restore success!")
else:
iter_num = 0
start_epoch = 0
start_step = 0
print("[*] Not find pretrained model!")
print("[*] Start training for phase %s, with start epoch %d start iter %d : " % (train_phase, start_epoch, iter_num))
start_time = time.time()
image_id = 0
for epoch in range(start_epoch, epoch):
for batch_id in range(start_step, numBatch):
# generate data for a batch
batch_input_low = np.zeros((batch_size, patch_size, patch_size, 3), dtype="float32")
batch_input_high = np.zeros((batch_size, patch_size, patch_size, 3), dtype="float32")
for patch_id in range(batch_size):
h, w, _ = train_low_data[image_id].shape
x = random.randint(0, h - patch_size)
y = random.randint(0, w - patch_size)
rand_mode = random.randint(0, 7)
batch_input_low[patch_id, :, :, :] = data_augmentation(train_low_data[image_id][x : x+patch_size, y : y+patch_size, :], rand_mode)
batch_input_high[patch_id, :, :, :] = data_augmentation(train_high_data[image_id][x : x+patch_size, y : y+patch_size, :], rand_mode)
image_id = (image_id + 1) % len(train_low_data)
if image_id == 0:
tmp = list(zip(train_low_data, train_high_data))
random.shuffle(list(tmp))
train_low_data, train_high_data = zip(*tmp)
# train
_, loss = self.sess.run([train_op, train_loss], feed_dict={self.input_low: batch_input_low, \
self.input_high: batch_input_high, \
self.lr: lr[epoch]})
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
# evalutate the model and save a checkpoint file for it
if (epoch + 1) % eval_every_epoch == 0:
self.evaluate(epoch + 1, eval_low_data, sample_dir=sample_dir, train_phase=train_phase)
self.save(saver, iter_num, ckpt_dir, "RetinexNet-%s" % train_phase)
print("[*] Finish training for phase %s." % train_phase)
def save(self, saver, iter_num, ckpt_dir, model_name):
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
print("[*] Saving model %s" % model_name)
saver.save(self.sess, \
os.path.join(ckpt_dir, model_name), \
global_step=iter_num)
def load(self, saver, ckpt_dir):
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
full_path = tf.train.latest_checkpoint(ckpt_dir)
try:
global_step = int(full_path.split('/')[-1].split('-')[-1])
except ValueError:
global_step = None
saver.restore(self.sess, full_path)
return True, global_step
else:
print("[*] Failed to load model from %s" % ckpt_dir)
return False, 0
def test(self, test_low_data, test_high_data, test_low_data_names, save_dir, decom_flag):
tf.global_variables_initializer().run()
print("[*] Reading checkpoint...")
load_model_status_Decom, _ = self.load(self.saver_Decom, './model/Decom')
load_model_status_Relight, _ = self.load(self.saver_Relight, './model/Relight')
if load_model_status_Decom and load_model_status_Relight:
print("[*] Load weights successfully...")
print("[*] Testing...")
for idx in range(len(test_low_data)):
print(test_low_data_names[idx])
[_, name] = os.path.split(test_low_data_names[idx])
suffix = name[name.find('.') + 1:]
name = name[:name.find('.')]
input_low_test = np.expand_dims(test_low_data[idx], axis=0)
[R_low, I_low, I_delta, S] = self.sess.run([self.output_R_low, self.output_I_low, self.output_I_delta, self.output_S], feed_dict = {self.input_low: input_low_test})
if decom_flag == 1:
save_images(os.path.join(save_dir, name + "_R_low." + suffix), R_low)
save_images(os.path.join(save_dir, name + "_I_low." + suffix), I_low)
save_images(os.path.join(save_dir, name + "_I_delta." + suffix), I_delta)
save_images(os.path.join(save_dir, name + "_S." + suffix), S)