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main_AttSets.py
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main_AttSets.py
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import tensorflow as tf
import os
import shutil
import sys
import scipy.io
sys.path.append('..')
import tools as tools
import numpy as np
batch_size = 4
img_res = 127
vox_res32 = 32
total_mv = 24
GPU0 = '0'
re_train=False
single_view_train = False
multi_view_train = False
#####################################
config={}
config['batch_size'] = batch_size
config['total_mv'] = total_mv
#config['cat_names'] = ['02691156','02828884','02933112','02958343','03001627','03211117',
# '03636649','03691459','04090263','04256520','04379243','04401088','04530566']
config['cat_names'] = ['03001627']
for name in config['cat_names']:
config['X_rgb_'+name] = './Data_sample/ShapeNetRendering/'+name+'/'
config['Y_vox_'+name] = './Data_sample/ShapeNetVox32/'+name+'/'
#####################################
def attsets_fc(x, out_ele_num, name):
in_ele_num = tf.shape(x)[1]
in_ele_len = int(x.get_shape()[2])
out_ele_len = in_ele_len
####################
x_1st = x
x_1st_tp = tf.reshape(x_1st, [-1, in_ele_len])
weights_1st = tools.Ops.fc(x_1st_tp, out_d=out_ele_num*out_ele_len, name=name+'_1st')
########## option 1
weights_1st = weights_1st
########## option 2
#weights_1st = tf.nn.tanh(weights_1st)
####################
weights_1st = tf.reshape(weights_1st, [-1, in_ele_num, out_ele_num, out_ele_len])
weights_1st = tf.nn.softmax(weights_1st, 1)
x_1st = tf.tile(x_1st[:,:,None,:], [1,1,out_ele_num,1])
x_1st = x_1st*weights_1st
x_1st = tf.reduce_sum(x_1st, axis=1)
x_1st = tf.reshape(x_1st, [-1, out_ele_num*out_ele_len])
return x_1st, weights_1st
#####################################
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/'
print ('re_train:', re_train)
if os.path.exists(self.test_res_dir):
if re_train:
print ('test_res_dir and files kept!')
else:
shutil.rmtree(self.test_res_dir)
os.makedirs(self.test_res_dir)
print ('test_res_dir: deleted and then created!')
else:
os.makedirs(self.test_res_dir)
print ('test_res_dir: created!')
if os.path.exists(self.train_mod_dir):
if re_train:
if os.path.exists(self.train_mod_dir + 'model.cptk.data-00000-of-00001'):
print ('model found! will be reused!')
else:
print ('model not found! error!')
#exit()
else:
shutil.rmtree(self.train_mod_dir)
os.makedirs(self.train_mod_dir)
print ('train_mod_dir: deleted and then created!')
else:
os.makedirs(self.train_mod_dir)
print ('train_mod_dir: created!')
if os.path.exists(self.train_sum_dir):
if re_train:
print ('train_sum_dir and files kept!')
else:
shutil.rmtree(self.train_sum_dir)
os.makedirs(self.train_sum_dir)
print ('train_sum_dir: deleted and then created!')
else:
os.makedirs(self.train_sum_dir)
print ('train_sum_dir: created!')
if os.path.exists(self.test_sum_dir):
if re_train:
print ('test_sum_dir and files kept!')
else:
shutil.rmtree(self.test_sum_dir)
os.makedirs(self.test_sum_dir)
print ('test_sum_dir: deleted and then created!')
else:
os.makedirs(self.test_sum_dir)
print ('test_sum_dir: created!')
def base_r2n2(self, X_rgb):
im_num = tf.shape(X_rgb)[1]
[_, _, d1, d2, cc] = X_rgb.get_shape()
X_rgb = tf.reshape(X_rgb, [-1, int(d1), int(d2), int(cc)])
en_c = [96, 128, 256, 256, 256, 256]
l1 = tools.Ops.xxlu(tools.Ops.conv2d(X_rgb, k=7, out_c=en_c[0], str=1, name='l1'), label='lrelu')
l2 = tools.Ops.xxlu(tools.Ops.conv2d(l1, k=3, out_c=en_c[0], str=1, name='l2'), label='lrelu')
l2 = tools.Ops.maxpool2d(l2, k=2, s=2, name='l2_p')
l3 = tools.Ops.xxlu(tools.Ops.conv2d(l2, k=3, out_c=en_c[1], str=1, name='l3'), label='lrelu')
l4 = tools.Ops.xxlu(tools.Ops.conv2d(l3, k=3, out_c=en_c[1], str=1, name='l4'), label='lrelu')
l22 = tools.Ops.conv2d(l2, k=1, out_c=en_c[1], str=1, name='l22')
l4 = l4 + l22
l4 = tools.Ops.maxpool2d(l4, k=2, s=2, name='l4_p')
l5 = tools.Ops.xxlu(tools.Ops.conv2d(l4, k=3, out_c=en_c[2], str=1, name='l5'), label='lrelu')
l6 = tools.Ops.xxlu(tools.Ops.conv2d(l5, k=3, out_c=en_c[2], str=1, name='l6'), label='lrelu')
l44 = tools.Ops.conv2d(l4, k=1, out_c=en_c[2], str=1, name='l44')
l6 = l6 + l44
l6 = tools.Ops.maxpool2d(l6, k=2, s=2, name='l6_p')
l7 = tools.Ops.xxlu(tools.Ops.conv2d(l6, k=3, out_c=en_c[3], str=1, name='l7'), label='lrelu')
l8 = tools.Ops.xxlu(tools.Ops.conv2d(l7, k=3, out_c=en_c[3], str=1, name='l8'), label='lrelu')
l8 = tools.Ops.maxpool2d(l8, k=2, s=2, name='l8_p')
l9 = tools.Ops.xxlu(tools.Ops.conv2d(l8, k=3, out_c=en_c[4], str=1, name='l9'), label='lrelu')
l10 = tools.Ops.xxlu(tools.Ops.conv2d(l9, k=3, out_c=en_c[4], str=1, name='l10'), label='lrelu')
l88 = tools.Ops.conv2d(l8, k=1, out_c=en_c[4], str=1, name='l88')
l10 = l10 + l88
l10 = tools.Ops.maxpool2d(l10, k=2, s=2, name='l10_p')
l11 = tools.Ops.xxlu(tools.Ops.conv2d(l10, k=3, out_c=en_c[5], str=1, name='l11'), label='lrelu')
l12 = tools.Ops.xxlu(tools.Ops.conv2d(l11, k=3, out_c=en_c[5], str=1, name='l12'), label='lrelu')
l1010 = tools.Ops.conv2d(l10, k=1, out_c=en_c[5], str=1, name='l1010_p')
l12 = l12 + l1010
l12 = tools.Ops.maxpool2d(l12, k=2, s=2, name='l12_p')
[_, d1, d2, cc] = l12.get_shape()
l12 = tf.reshape(l12, [-1, int(d1) * int(d2) * int(cc)])
fc = tools.Ops.xxlu(tools.Ops.fc(l12, out_d=1024, name='lfc1'), label='lrelu')
#### use fc attention
input = tf.reshape(fc, [-1, im_num, 1024])
latent_3d, weights = attsets_fc(input, out_ele_num=1, name='att')
####
latent_3d = tools.Ops.xxlu(tools.Ops.fc(latent_3d, out_d=4*4*4*128, name='lfc2'), label='lrelu')
latent_3d = tf.reshape(latent_3d, [-1, 4, 4, 4, 128])
####
de_c = [128, 128, 128, 64, 32, 1]
d1 = tools.Ops.xxlu(tools.Ops.deconv3d(latent_3d, k=3, out_c=de_c[1], str=2, name='ld1'), label='lrelu')
d2 = tools.Ops.xxlu(tools.Ops.deconv3d(d1, k=3, out_c=de_c[1], str=1, name='ld2'), label='lrelu')
d00 = tools.Ops.deconv3d(latent_3d, k=1, out_c=de_c[1], str=2, name='ld00')
d2 = d2 + d00
d3 = tools.Ops.xxlu(tools.Ops.deconv3d(d2, k=3, out_c=de_c[2], str=2, name='ld3'), label='lrelu')
d4 = tools.Ops.xxlu(tools.Ops.deconv3d(d3, k=3, out_c=de_c[2], str=1, name='ld4'), label='lrelu')
d22 = tools.Ops.deconv3d(d2, k=1, out_c=de_c[2], str=2, name='ld22')
d4 = d4 + d22
d5 = tools.Ops.xxlu(tools.Ops.deconv3d(d4, k=3, out_c=de_c[3], str=2, name='ld5'), label='lrelu')
d6 = tools.Ops.xxlu(tools.Ops.deconv3d(d5, k=3, out_c=de_c[3], str=1, name='ld6'), label='lrelu')
d44 = tools.Ops.deconv3d(d4, k=1, out_c=de_c[3], str=2, name='ld44')
d6 = d6 + d44
d7 = tools.Ops.xxlu(tools.Ops.deconv3d(d6, k=3, out_c=de_c[4], str=1, name='ld7'), label='lrelu')
d8 = tools.Ops.xxlu(tools.Ops.deconv3d(d7, k=3, out_c=de_c[4], str=1, name='ld8'), label='lrelu')
d77 = tools.Ops.xxlu(tools.Ops.deconv3d(d7, k=3, out_c=de_c[4], str=1, name='ld77'), label='lrelu')
d8 = d8 + d77
d11 = tools.Ops.deconv3d(d8, k=3, out_c=de_c[5], str=1, name='ld11')
y = tf.nn.sigmoid(d11)
y = tf.reshape(y, [-1, vox_res32, vox_res32, vox_res32])
return y, weights
def build_graph(self):
img_res = 127
vox_res = 32
self.X_rgb = tf.placeholder(shape=[None, None, img_res, img_res, 3], dtype=tf.float32)
self.Y_vox = tf.placeholder(shape=[None, vox_res, vox_res, vox_res], dtype=tf.float32)
self.lr = tf.placeholder(tf.float32)
with tf.variable_scope('r2n'):
self.Y_pred, self.weights = self.base_r2n2(self.X_rgb)
with tf.device('/gpu:' + GPU0):
### rec loss
Y_vox_ = tf.reshape(self.Y_vox, shape=[-1, vox_res ** 3])
Y_pred_ = tf.reshape(self.Y_pred, shape=[-1, vox_res ** 3])
self.rec_loss = tf.reduce_mean(-tf.reduce_mean(Y_vox_ * tf.log(Y_pred_ + 1e-8), reduction_indices=[1]) -
tf.reduce_mean((1 - Y_vox_) * tf.log(1 - Y_pred_ + 1e-8),reduction_indices=[1]))
sum_rec_loss = tf.summary.scalar('rec_loss', self.rec_loss)
self.sum_merged = sum_rec_loss
base_var = [var for var in tf.trainable_variables() if var.name.startswith('r2n/l')]
att_var = [var for var in tf.trainable_variables() if var.name.startswith('r2n/att')]
self.base_optim = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.rec_loss, var_list=base_var)
self.att_optim = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.rec_loss, var_list=att_var)
print ("total weights:",tools.Ops.variable_count())
self.saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = GPU0
self.sess = tf.Session(config=config)
self.sum_writer_train = tf.summary.FileWriter(self.train_sum_dir, self.sess.graph)
self.sum_writer_test = tf.summary.FileWriter(self.test_sum_dir, self.sess.graph)
#######################
path = self.train_mod_dir
#path = './Model_released/' # retrain the released model
if os.path.isfile(path + 'model.cptk.data-00000-of-00001'):
print ("restoring saved model!")
self.saver.restore(self.sess, path + 'model.cptk')
else:
self.sess.run(tf.global_variables_initializer())
return 0
def train(self, data):
for epoch in range(0, 50, 1):
train_view_num = 24 ##!!!!!!!!!!!
data.shuffle_train_files(epoch, train_mv=train_view_num)
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_rgb_bat, Y_vox_bat = data.load_X_Y_train_next_batch(train_mv=train_view_num)
##### option 1: seperate train, seperate optimize
if epoch<=30:
single_view_train=True
multi_view_train=False
else:
single_view_train=False
multi_view_train=True
##### optiion 2: joint train, seperate optimize
#single_view_train = True
#multi_view_train = True
########### single view train
if single_view_train:
rgb = np.reshape(X_rgb_bat,[batch_size*train_view_num, 1, 127,127,3])
vox = np.tile(Y_vox_bat[:,None,:,:,:],[1,train_view_num,1,1,1])
vox = np.reshape(vox, [batch_size*train_view_num, 32,32,32])
_, rec_loss_c, sum_train = self.sess.run([self.base_optim,self.rec_loss,self.sum_merged],
feed_dict={self.X_rgb: rgb, self.Y_vox: vox, self.lr: 0.0001})
print ('ep:', epoch, 'i:', i, 'train single rec loss:', rec_loss_c)
########## multi view train
if multi_view_train:
rec_loss_c, _, sum_train = self.sess.run([self.rec_loss, self.att_optim, self.sum_merged],
feed_dict={self.X_rgb: X_rgb_bat, self.Y_vox: Y_vox_bat,self.lr: 0.0001})
print ('ep:', epoch, 'i:', i, 'train multi rec loss:', rec_loss_c)
############
if i % 100 == 0:
self.sum_writer_train.add_summary(sum_train, epoch * total_train_batch_num + i)
#### testing
if i % 400 == 0 :
X_rgb_batch, Y_vox_batch = data.load_X_Y_test_next_batch(test_mv=1)
rec_loss_te, Y_vox_test_pred, att_pred, sum_test = \
self.sess.run([self.rec_loss, self.Y_pred,self.weights, self.sum_merged],
feed_dict={self.X_rgb: X_rgb_batch, self.Y_vox: Y_vox_batch})
X_rgb_batch = X_rgb_batch.astype(np.float16)
Y_vox_batch = Y_vox_batch.astype(np.float16)
Y_vox_test_pred = Y_vox_test_pred.astype(np.float16)
att_pred = att_pred.astype(np.float16)
to_save = {'X_test':X_rgb_batch,'Y_test_pred':Y_vox_test_pred,'att_pred':att_pred,'Y_test_true':Y_vox_batch}
scipy.io.savemat(self.test_res_dir+'X_Y_pred_'+str(epoch).zfill(2)+'_'+str(i).zfill(5)+'.mat',to_save,do_compression=True)
self.sum_writer_test.add_summary(sum_test, epoch * total_train_batch_num + i)
print ('ep:', epoch, 'i:', i, 'test rec loss:', rec_loss_te)
#### model saving
if i % 200 == 0 and i > 0:
self.saver.save(self.sess, save_path=self.train_mod_dir + 'model.cptk')
print ('epoch:', epoch, 'i:', i, 'model saved!')
#### full testing
# ...
##########
if __name__ =='__main__':
net = Network()
net.build_graph()
data = tools.Data(config)
net.train(data)