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train_Sony.py
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train_Sony.py
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#uniform content loss + adaptive threshold + per_class_input + recursive G
#improvement upon cqf37
from __future__ import division
import os,time,scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.layers.python.layers import initializers
import numpy as np
import pdb
import rawpy
import glob
input_dir = './dataset/Sony/short/'
gt_dir = './dataset/Sony/long/'
checkpoint_dir = './result_Sony/'
result_dir = './result_Sony/'
#get train and test IDs
train_fns = glob.glob(gt_dir + '0*.ARW')
train_ids = []
for i in range(len(train_fns)):
_, train_fn = os.path.split(train_fns[i])
train_ids.append(int(train_fn[0:5]))
test_fns = glob.glob(gt_dir + '/1*.ARW')
test_ids = []
for i in range(len(test_fns)):
_, test_fn = os.path.split(test_fns[i])
test_ids.append(int(test_fn[0:5]))
ps = 512 #patch size for training
save_freq = 500
DEBUG = 0
if DEBUG == 1:
save_freq = 2
train_ids = train_ids[0:5]
test_ids = test_ids[0:5]
def lrelu(x):
return tf.maximum(x*0.2,x)
def upsample_and_concat(x1, x2, output_channels, in_channels):
pool_size = 2
deconv_filter = tf.Variable(tf.truncated_normal( [pool_size, pool_size, output_channels, in_channels], stddev=0.02))
deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2) , strides=[1, pool_size, pool_size, 1] )
deconv_output = tf.concat([deconv, x2],3)
deconv_output.set_shape([None, None, None, output_channels*2])
return deconv_output
def network(input):
conv1=slim.conv2d(input,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv1_1')
conv1=slim.conv2d(conv1,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv1_2')
pool1=slim.max_pool2d(conv1, [2, 2], padding='SAME' )
conv2=slim.conv2d(pool1,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv2_1')
conv2=slim.conv2d(conv2,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv2_2')
pool2=slim.max_pool2d(conv2, [2, 2], padding='SAME' )
conv3=slim.conv2d(pool2,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv3_1')
conv3=slim.conv2d(conv3,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv3_2')
pool3=slim.max_pool2d(conv3, [2, 2], padding='SAME' )
conv4=slim.conv2d(pool3,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv4_1')
conv4=slim.conv2d(conv4,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv4_2')
pool4=slim.max_pool2d(conv4, [2, 2], padding='SAME' )
conv5=slim.conv2d(pool4,512,[3,3], rate=1, activation_fn=lrelu,scope='g_conv5_1')
conv5=slim.conv2d(conv5,512,[3,3], rate=1, activation_fn=lrelu,scope='g_conv5_2')
up6 = upsample_and_concat( conv5, conv4, 256, 512 )
conv6=slim.conv2d(up6, 256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv6_1')
conv6=slim.conv2d(conv6,256,[3,3], rate=1, activation_fn=lrelu,scope='g_conv6_2')
up7 = upsample_and_concat( conv6, conv3, 128, 256 )
conv7=slim.conv2d(up7, 128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv7_1')
conv7=slim.conv2d(conv7,128,[3,3], rate=1, activation_fn=lrelu,scope='g_conv7_2')
up8 = upsample_and_concat( conv7, conv2, 64, 128 )
conv8=slim.conv2d(up8, 64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv8_1')
conv8=slim.conv2d(conv8,64,[3,3], rate=1, activation_fn=lrelu,scope='g_conv8_2')
up9 = upsample_and_concat( conv8, conv1, 32, 64 )
conv9=slim.conv2d(up9, 32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv9_1')
conv9=slim.conv2d(conv9,32,[3,3], rate=1, activation_fn=lrelu,scope='g_conv9_2')
conv10=slim.conv2d(conv9,12,[1,1], rate=1, activation_fn=None, scope='g_conv10')
out = tf.depth_to_space(conv10,2)
return out
def pack_raw(raw):
#pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512,0)/ (16383 - 512) #subtract the black level
im = np.expand_dims(im,axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2,0:W:2,:],
im[0:H:2,1:W:2,:],
im[1:H:2,1:W:2,:],
im[1:H:2,0:W:2,:]), axis=2)
return out
sess=tf.Session()
in_image=tf.placeholder(tf.float32,[None,None,None,4])
gt_image=tf.placeholder(tf.float32,[None,None,None,3])
out_image=network(in_image)
G_loss=tf.reduce_mean(tf.abs(out_image - gt_image))
t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss,var_list=[var for var in t_vars if var.name.startswith('g_')])
saver=tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
#Raw data takes long time to load. Keep them in memory after loaded.
gt_images=[None]*6000
input_images = {}
input_images['300'] = [None]*len(train_ids)
input_images['250'] = [None]*len(train_ids)
input_images['100'] = [None]*len(train_ids)
g_loss = np.zeros((5000,1))
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
learning_rate = 1e-4
for epoch in range(lastepoch,4001):
if os.path.isdir("result/%04d"%epoch):
continue
cnt=0
if epoch > 2000:
learning_rate = 1e-5
for ind in np.random.permutation(len(train_ids)):
# get the path from image id
train_id = train_ids[ind]
in_files = glob.glob(input_dir + '%05d_00*.ARW'%train_id)
in_path = in_files[np.random.random_integers(0,len(in_files)-1)]
_, in_fn = os.path.split(in_path)
gt_files = glob.glob(gt_dir + '%05d_00*.ARW'%train_id)
gt_path = gt_files[0]
_, gt_fn = os.path.split(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure/in_exposure,300)
st=time.time()
cnt+=1
if input_images[str(ratio)[0:3]][ind] is None:
raw = rawpy.imread(in_path)
input_images[str(ratio)[0:3]][ind] = np.expand_dims(pack_raw(raw),axis=0) *ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
gt_images[ind] = np.expand_dims(np.float32(im/65535.0),axis = 0)
#crop
H = input_images[str(ratio)[0:3]][ind].shape[1]
W = input_images[str(ratio)[0:3]][ind].shape[2]
xx = np.random.randint(0,W-ps)
yy = np.random.randint(0,H-ps)
input_patch = input_images[str(ratio)[0:3]][ind][:,yy:yy+ps,xx:xx+ps,:]
gt_patch = gt_images[ind][:,yy*2:yy*2+ps*2,xx*2:xx*2+ps*2,:]
if np.random.randint(2,size=1)[0] == 1: # random flip
input_patch = np.flip(input_patch, axis=1)
gt_patch = np.flip(gt_patch, axis=1)
if np.random.randint(2,size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=0)
gt_patch = np.flip(gt_patch, axis=0)
if np.random.randint(2,size=1)[0] == 1: # random transpose
input_patch = np.transpose(input_patch, (0,2,1,3))
gt_patch = np.transpose(gt_patch, (0,2,1,3))
input_patch = np.minimum(input_patch,1.0)
_,G_current,output=sess.run([G_opt,G_loss,out_image],feed_dict={in_image:input_patch,gt_image:gt_patch,lr:learning_rate})
output = np.minimum(np.maximum(output,0),1)
g_loss[ind]=G_current
print("%d %d Loss=%.3f Time=%.3f"%(epoch,cnt,np.mean(g_loss[np.where(g_loss)]),time.time()-st))
if epoch%save_freq==0:
if not os.path.isdir(result_dir + '%04d'%epoch):
os.makedirs(result_dir + '%04d'%epoch)
temp = np.concatenate((gt_patch[0,:,:,:],output[0,:,:,:]),axis=1)
scipy.misc.toimage(temp*255, high=255, low=0, cmin=0, cmax=255).save(result_dir + '%04d/%05d_00_train_%d.jpg'%(epoch,train_id,ratio))
saver.save(sess, checkpoint_dir + 'model.ckpt')