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main_3D-RecGAN.py
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main_3D-RecGAN.py
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import os
import shutil
import tensorflow as tf
import scipy.io
import tools
resolution = 64
batch_size = 8
GPU0 = '1'
###############################################################
config={}
config['train_names'] = ['chair']
for name in config['train_names']:
config['X_train_'+name] = './Data/'+name+'/train_25d/voxel_grids_64/'
config['Y_train_'+name] = './Data/'+name+'/train_3d/voxel_grids_64/'
config['test_names']=['chair']
for name in config['test_names']:
config['X_test_'+name] = './Data/'+name+'/test_25d/voxel_grids_64/'
config['Y_test_'+name] = './Data/'+name+'/test_3d/voxel_grids_64/'
config['resolution'] = resolution
config['batch_size'] = batch_size
################################################################
class Network:
def __init__(self):
self.train_mod_dir = './train_mod/'
self.train_sum_dir = './train_sum/'
self.test_res_dir = './test_res/'
self.test_sum_dir = './test_sum/'
if os.path.exists(self.test_res_dir):
shutil.rmtree(self.test_res_dir)
print ('test_res_dir: deleted and then created!')
os.makedirs(self.test_res_dir)
if os.path.exists(self.train_mod_dir):
shutil.rmtree(self.train_mod_dir)
print ('train_mod_dir: deleted and then created!')
os.makedirs(self.train_mod_dir)
if os.path.exists(self.train_sum_dir):
shutil.rmtree(self.train_sum_dir)
print ('train_sum_dir: deleted and then created!')
os.makedirs(self.train_sum_dir)
if os.path.exists(self.test_sum_dir):
shutil.rmtree(self.test_sum_dir)
print ('test_sum_dir: deleted and then created!')
os.makedirs(self.test_sum_dir)
def ae_u(self,X):
with tf.device('/gpu:'+GPU0):
X = tf.reshape(X,[batch_size,resolution,resolution,resolution,1])
##### encode
c_e = [1, 64, 128, 256, 512]
s_e = [0, 1, 1, 1, 1]
layers_e = []
layers_e.append(X)
for i in range(1, 5, 1):
layer = tools.Ops.conv3d(layers_e[-1], k=4, out_c=c_e[i], str=s_e[i], name='e' + str(i))
layer = tools.Ops.maxpool3d(tools.Ops.xxlu(layer,name='lrelu'), k=2, s=2, pad='SAME')
layers_e.append(layer)
##### fc
bat, d1, d2, d3, cc = [int(d) for d in layers_e[-1].get_shape()]
lfc = tf.reshape(layers_e[-1], [bat, -1])
lfc = tools.Ops.xxlu(tools.Ops.fc(lfc, out_d=5000, name='fc1'), name='relu')
with tf.device('/gpu:'+GPU0):
lfc = tools.Ops.xxlu(tools.Ops.fc(lfc, out_d=d1 * d2 * d3 * cc, name='fc2'),name='relu')
lfc = tf.reshape(lfc, [bat, d1, d2, d3, cc])
##### decode
c_d = [0, 256, 128, 64, 1]
s_d = [0, 2, 2, 2, 2, 2]
layers_d = []
layers_d.append(lfc)
for j in range(1, 5, 1):
u_net = True
if u_net:
layer = tf.concat([layers_d[-1], layers_e[-j]], axis=4)
layer = tools.Ops.deconv3d(layer, k=4, out_c=c_d[j], str=s_d[j], name='d' + str(len(layers_d)))
else:
layer = tools.Ops.deconv3d(layers_d[-1], k=4, out_c=c_d[j], str=s_d[j], name='d' + str(len(layers_d)))
if j != 4:
layer = tools.Ops.xxlu(layer,name='relu')
layers_d.append(layer)
vox_sig = tf.sigmoid(layers_d[-1])
vox_sig_modified = tf.maximum(vox_sig,0.01)
return vox_sig, vox_sig_modified
def dis(self, X, Y):
with tf.device('/gpu:'+GPU0):
X = tf.reshape(X,[batch_size,resolution,resolution,resolution,1])
Y = tf.reshape(Y,[batch_size,resolution,resolution,resolution,1])
layer = tf.concat([X,Y],axis=4)
c_d = [1,64,128,256,512]
s_d = [0,2,2,2,2]
layers_d =[]
layers_d.append(layer)
for i in range(1,5,1):
layer = tools.Ops.conv3d(layers_d[-1],k=4,out_c=c_d[i],str=s_d[i],name='d'+str(i))
if i!=4:
layer = tools.Ops.xxlu(layer, name='lrelu')
layers_d.append(layer)
y = tf.reshape(layers_d[-1],[batch_size,-1])
return tf.nn.sigmoid(y)
def train(self, data):
X = tf.placeholder(shape=[batch_size, resolution, resolution, resolution, 1], dtype=tf.float32)
Y = tf.placeholder(shape=[batch_size, resolution, resolution, resolution, 1], dtype=tf.float32)
lr = tf.placeholder(tf.float32)
with tf.variable_scope('ae'):
Y_pred, Y_pred_modi = self.ae_u(X)
with tf.variable_scope('dis'):
XY_real_pair = self.dis(X, Y)
with tf.variable_scope('dis',reuse=True):
XY_fake_pair = self.dis(X, Y_pred)
with tf.device('/gpu:'+GPU0):
################################ ae loss
Y_ = tf.reshape(Y,shape=[batch_size,-1])
Y_pred_modi_ = tf.reshape(Y_pred_modi,shape=[batch_size,-1])
w = 0.85
ae_loss = tf.reduce_mean( -tf.reduce_mean(w*Y_*tf.log(Y_pred_modi_ + 1e-8),reduction_indices=[1]) -
tf.reduce_mean((1-w)*(1-Y_)*tf.log(1-Y_pred_modi_ + 1e-8), reduction_indices=[1]) )
sum_ae_loss = tf.summary.scalar('ae_loss', ae_loss)
################################ wgan loss
gan_g_loss = -tf.reduce_mean(XY_fake_pair)
gan_d_loss_no_gp = tf.reduce_mean(XY_fake_pair) - tf.reduce_mean(XY_real_pair)
sum_gan_g_loss = tf.summary.scalar('gan_g_loss',gan_g_loss)
sum_gan_d_loss_no_gp = tf.summary.scalar('gan_d_loss_no_gp',gan_d_loss_no_gp)
alpha = tf.random_uniform(shape=[batch_size,resolution**3],minval=0.0,maxval=1.0)
Y_pred_ = tf.reshape(Y_pred,shape=[batch_size,-1])
differences_ = Y_pred_ -Y_
interpolates = Y_ + alpha*differences_
with tf.variable_scope('dis',reuse=True):
XY_fake_intep = self.dis(X, interpolates)
gradients = tf.gradients(XY_fake_intep,[interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients),reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes-1.0)**2)
sum_gp = tf.summary.scalar('wgan_gp', gradient_penalty)
gan_d_loss_gp = gan_d_loss_no_gp+10*gradient_penalty
################################# ae + gan loss
gan_g_w = 5
ae_w = 100-gan_g_w
ae_gan_g_loss = ae_w * ae_loss + gan_g_w * gan_g_loss
with tf.device('/gpu:' + GPU0):
ae_var = [var for var in tf.trainable_variables() if var.name.startswith('ae')]
dis_var = [var for var in tf.trainable_variables() if var.name.startswith('dis')]
ae_g_optim = tf.train.AdamOptimizer(learning_rate=lr, beta1=0.9, beta2=0.999, epsilon=1e-8).minimize(ae_gan_g_loss, var_list=ae_var)
dis_optim = tf.train.AdamOptimizer(learning_rate=lr,beta1=0.9,beta2=0.999,epsilon=1e-8).minimize(gan_d_loss_gp,var_list=dis_var)
print tools.Ops.variable_count()
sum_merged = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = GPU0
with tf.Session(config=config) as sess:
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir, sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir)
if os.path.isfile(self.train_mod_dir + 'model.cptk.data-00000-of-00001'):
print ('restoring saved model')
saver.restore(sess, self.train_mod_dir + 'model.cptk')
else:
sess.run(tf.global_variables_initializer())
for epoch in range(15):
data.shuffle_X_Y_files(label='train')
total_train_batch_num = data.total_train_batch_num
print ('total_train_batch_num:', total_train_batch_num)
for i in range(total_train_batch_num):
#### training
X_train_batch, Y_train_batch = data.load_X_Y_voxel_grids_train_next_batch()
sess.run([dis_optim], feed_dict={X: X_train_batch, Y: Y_train_batch, lr: 0.0001})
sess.run([ae_g_optim], feed_dict={X: X_train_batch, Y: Y_train_batch, lr: 0.0005})
ae_loss_c,gan_g_loss_c,gan_d_loss_no_gp_c,gan_d_loss_gp_c,sum_train = sess.run(
[ae_loss, gan_g_loss, gan_d_loss_no_gp, gan_d_loss_gp,sum_merged],feed_dict={X: X_train_batch, Y: Y_train_batch})
if i%100==0:
sum_writer_train.add_summary(sum_train, epoch * total_train_batch_num + i)
print ('epoch:', epoch, 'i:', i, 'train ae loss:', ae_loss_c,'gan g loss:', gan_g_loss_c,
'gan d loss no gp:',gan_d_loss_no_gp_c, 'gan d loss gp:', gan_d_loss_gp_c)
#### testing
if i % 300 == 0 and epoch % 1 == 0:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_grids_test_next_batch(fix_sample=False)
ae_loss_t,gan_g_loss_t,gan_d_loss_no_gp_t,gan_d_loss_gp_t, Y_test_pred = sess.run(
[ae_loss, gan_g_loss, gan_d_loss_no_gp,gan_d_loss_gp, Y_pred],feed_dict={X: X_test_batch, Y: Y_test_batch})
to_save = {'X_test': X_test_batch, 'Y_test_pred': Y_test_pred,'Y_test_true': Y_test_batch}
scipy.io.savemat(self.test_res_dir + 'X_Y_pred_' + str(epoch).zfill(2) + '_' + str(i).zfill(4) + '.mat', to_save, do_compression=True)
print ('epoch:', epoch, 'i:', i, 'test ae loss:', ae_loss_t, 'gan g loss:',gan_g_loss_t,
'gan d loss no gp:', gan_d_loss_no_gp_t, 'gan d loss gp:', gan_d_loss_gp_t)
#### full testing
# ...
#### model saving
if i %500==0 and i>0 and epoch % 1 == 0:
saver.save(sess, save_path=self.train_mod_dir +'model.cptk')
print "epoch:", epoch, " i:", i, " model saved!"
if __name__ == "__main__":
data = tools.Data(config)
net = Network()
net.train(data)