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demo_256p.py
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demo_256p.py
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from __future__ import division
import os,helper,time,scipy.io
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
def lrelu(x):
return tf.maximum(0.2*x,x)
def build_net(ntype,nin,nwb=None,name=None):
if ntype=='conv':
return tf.nn.relu(tf.nn.conv2d(nin,nwb[0],strides=[1,1,1,1],padding='SAME',name=name)+nwb[1])
elif ntype=='pool':
return tf.nn.avg_pool(nin,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def get_weight_bias(vgg_layers,i):
weights=vgg_layers[i][0][0][2][0][0]
weights=tf.constant(weights)
bias=vgg_layers[i][0][0][2][0][1]
bias=tf.constant(np.reshape(bias,(bias.size)))
return weights,bias
def build_vgg19(input,reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
net={}
vgg_rawnet=scipy.io.loadmat('VGG_Model/imagenet-vgg-verydeep-19.mat')
vgg_layers=vgg_rawnet['layers'][0]
net['input']=input-np.array([123.6800, 116.7790, 103.9390]).reshape((1,1,1,3))
net['conv1_1']=build_net('conv',net['input'],get_weight_bias(vgg_layers,0),name='vgg_conv1_1')
net['conv1_2']=build_net('conv',net['conv1_1'],get_weight_bias(vgg_layers,2),name='vgg_conv1_2')
net['pool1']=build_net('pool',net['conv1_2'])
net['conv2_1']=build_net('conv',net['pool1'],get_weight_bias(vgg_layers,5),name='vgg_conv2_1')
net['conv2_2']=build_net('conv',net['conv2_1'],get_weight_bias(vgg_layers,7),name='vgg_conv2_2')
net['pool2']=build_net('pool',net['conv2_2'])
net['conv3_1']=build_net('conv',net['pool2'],get_weight_bias(vgg_layers,10),name='vgg_conv3_1')
net['conv3_2']=build_net('conv',net['conv3_1'],get_weight_bias(vgg_layers,12),name='vgg_conv3_2')
net['conv3_3']=build_net('conv',net['conv3_2'],get_weight_bias(vgg_layers,14),name='vgg_conv3_3')
net['conv3_4']=build_net('conv',net['conv3_3'],get_weight_bias(vgg_layers,16),name='vgg_conv3_4')
net['pool3']=build_net('pool',net['conv3_4'])
net['conv4_1']=build_net('conv',net['pool3'],get_weight_bias(vgg_layers,19),name='vgg_conv4_1')
net['conv4_2']=build_net('conv',net['conv4_1'],get_weight_bias(vgg_layers,21),name='vgg_conv4_2')
net['conv4_3']=build_net('conv',net['conv4_2'],get_weight_bias(vgg_layers,23),name='vgg_conv4_3')
net['conv4_4']=build_net('conv',net['conv4_3'],get_weight_bias(vgg_layers,25),name='vgg_conv4_4')
net['pool4']=build_net('pool',net['conv4_4'])
net['conv5_1']=build_net('conv',net['pool4'],get_weight_bias(vgg_layers,28),name='vgg_conv5_1')
net['conv5_2']=build_net('conv',net['conv5_1'],get_weight_bias(vgg_layers,30),name='vgg_conv5_2')
net['conv5_3']=build_net('conv',net['conv5_2'],get_weight_bias(vgg_layers,32),name='vgg_conv5_3')
net['conv5_4']=build_net('conv',net['conv5_3'],get_weight_bias(vgg_layers,34),name='vgg_conv5_4')
net['pool5']=build_net('pool',net['conv5_4'])
return net
def recursive_generator(label,sp):
dim=512 if sp>=128 else 1024
if sp==4:
input=label
else:
downsampled=tf.image.resize_area(label,(sp//2,sp),align_corners=False)
input=tf.concat([tf.image.resize_bilinear(recursive_generator(downsampled,sp//2),(sp,sp*2),align_corners=True),label],3)
net=slim.conv2d(input,dim,[3,3],rate=1,normalizer_fn=slim.layer_norm,activation_fn=lrelu,scope='g_'+str(sp)+'_conv1')
net=slim.conv2d(net,dim,[3,3],rate=1,normalizer_fn=slim.layer_norm,activation_fn=lrelu,scope='g_'+str(sp)+'_conv2')
if sp==256:
net=slim.conv2d(net,27,[1,1],rate=1,activation_fn=None,scope='g_'+str(sp)+'_conv100')
net=(net+1.0)/2.0*255.0
split0,split1,split2=tf.split(tf.transpose(net,perm=[3,1,2,0]),num_or_size_splits=3,axis=0)
net=tf.concat([split0,split1,split2],3)
return net
def compute_error(real,fake,label):
return tf.reduce_mean(label*tf.expand_dims(tf.reduce_mean(tf.abs(fake-real),reduction_indices=[3]),-1),reduction_indices=[1,2])#diversity loss
#os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp')
#os.environ['CUDA_VISIBLE_DEVICES']=str(np.argmax([int(x.split()[2]) for x in open('tmp','r').readlines()]))#select a GPU with maximum available memory
#os.system('rm tmp')
sess=tf.Session()
is_training=False
sp=256#spatial resolution: 256x512
with tf.variable_scope(tf.get_variable_scope()):
label=tf.placeholder(tf.float32,[None,None,None,20])
real_image=tf.placeholder(tf.float32,[None,None,None,3])
fake_image=tf.placeholder(tf.float32,[None,None,None,3])
generator=recursive_generator(label,sp)
weight=tf.placeholder(tf.float32)
vgg_real=build_vgg19(real_image)
vgg_fake=build_vgg19(generator,reuse=True)
p0=compute_error(vgg_real['input'],vgg_fake['input'],label)
p1=compute_error(vgg_real['conv1_2'],vgg_fake['conv1_2'],label)/1.6
p2=compute_error(vgg_real['conv2_2'],vgg_fake['conv2_2'],tf.image.resize_area(label,(sp//2,sp)))/2.3
p3=compute_error(vgg_real['conv3_2'],vgg_fake['conv3_2'],tf.image.resize_area(label,(sp//4,sp//2)))/1.8
p4=compute_error(vgg_real['conv4_2'],vgg_fake['conv4_2'],tf.image.resize_area(label,(sp//8,sp//4)))/2.8
p5=compute_error(vgg_real['conv5_2'],vgg_fake['conv5_2'],tf.image.resize_area(label,(sp//16,sp//8)))*10/0.8#weights lambda are collected at 100th epoch
content_loss=p0+p1+p2+p3+p4+p5
G_loss=tf.reduce_sum(tf.reduce_min(content_loss,reduction_indices=0))*0.999+tf.reduce_sum(tf.reduce_mean(content_loss,reduction_indices=0))*0.001
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss,var_list=[var for var in tf.trainable_variables() if var.name.startswith('g_')])
saver=tf.train.Saver(max_to_keep=1000)
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state("result_256p")
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
if is_training:
g_loss=np.zeros(3000,dtype=float)
input_images=[None]*3000
label_images=[None]*3000
for epoch in range(1,201):
if os.path.isdir("result_256p/%04d"%epoch):
continue
cnt=0
for ind in np.random.permutation(2975)+1:
st=time.time()
cnt+=1
if input_images[ind] is None:
label_images[ind]=helper.get_semantic_map("data/cityscapes/Label256Full/%08d.png"%ind)#training label
input_images[ind]=np.expand_dims(np.float32(scipy.misc.imread("data/cityscapes/RGB256Full/%08d.png"%ind)),axis=0)#training image
_,G_current,l0,l1,l2,l3,l4,l5=sess.run([G_opt,G_loss,p0,p1,p2,p3,p4,p5],feed_dict={label:np.concatenate((label_images[ind],np.expand_dims(1-np.sum(label_images[ind],axis=3),axis=3)),axis=3),real_image:input_images[ind],lr:1e-4})#may try lr:min(1e-6*np.power(1.1,epoch-1),1e-4 if epoch>100 else 1e-3) in case lr:1e-4 is not good
g_loss[ind]=G_current
print("%d %d %.2f %.2f %.2f %.2f %.2f %.2f %.2f %.2f"%(epoch,cnt,np.mean(g_loss[np.where(g_loss)]),np.mean(l0),np.mean(l1),np.mean(l2),np.mean(l3),np.mean(l4),np.mean(l5),time.time()-st))
os.makedirs("result_256p/%04d"%epoch)
target=open("result_256p/%04d/score.txt"%epoch,'w')
target.write("%f"%np.mean(g_loss[np.where(g_loss)]))
target.close()
saver.save(sess,"result_256p/model.ckpt")
if epoch%20==0:
saver.save(sess,"result_256p/%04d/model.ckpt"%epoch)
for ind in range(100001,100051):
if not os.path.isfile("data/cityscapes/Label256Full/%08d.png"%ind):#test label
continue
semantic=helper.get_semantic_map("data/cityscapes/Label256Full/%08d.png"%ind)#test label
output=sess.run(generator,feed_dict={label:np.concatenate((semantic,np.expand_dims(1-np.sum(semantic,axis=3),axis=3)),axis=3)})
output=np.minimum(np.maximum(output,0.0),255.0)
upper=np.concatenate((output[0,:,:,:],output[1,:,:,:],output[2,:,:,:]),axis=1)
middle=np.concatenate((output[3,:,:,:],output[4,:,:,:],output[5,:,:,:]),axis=1)
bottom=np.concatenate((output[6,:,:,:],output[7,:,:,:],output[8,:,:,:]),axis=1)
scipy.misc.toimage(np.concatenate((upper,middle,bottom),axis=0),cmin=0,cmax=255).save("result_256p/%04d/%06d_output.jpg"%(epoch,ind))
if not os.path.isdir("result_256p/final"):
os.makedirs("result_256p/final")
for ind in range(100001,100501):
if not os.path.isfile("data/cityscapes/Label256Full/%08d.png"%ind):#test label
continue
semantic=helper.get_semantic_map("data/cityscapes/Label256Full/%08d.png"%ind)#test label
output=sess.run(generator,feed_dict={label:np.concatenate((semantic,np.expand_dims(1-np.sum(semantic,axis=3),axis=3)),axis=3)})
output=np.minimum(np.maximum(output, 0.0), 255.0)
upper=np.concatenate((output[0,:,:,:],output[1,:,:,:],output[2,:,:,:]),axis=1)
middle=np.concatenate((output[3,:,:,:],output[4,:,:,:],output[5,:,:,:]),axis=1)
bottom=np.concatenate((output[6,:,:,:],output[7,:,:,:],output[8,:,:,:]),axis=1)
scipy.misc.toimage(np.concatenate((upper,middle,bottom),axis=0),cmin=0,cmax=255).save("result_256p/final/%06d_output.jpg"%ind)