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nobg_dm.py
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nobg_dm.py
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import time
import os
import utils.realtime_renderer as rtr
from utils.tf_utils import *
class mv3d():
def __init__(self, sess):
self.sess = sess
self.batch_size = 64
self.input_shape = [128, 128, 3]
self.output_shape = [128, 128, 4]
self.max_iter = 1000000
self.start_iter = 0
self.log_folder = "logs/nobg_dm"
self.train_samples_folder = "samples/nobg_dm/train"
self.test_samples_folder = "samples/nobg_dm/test"
self.snapshots_folder = "snapshots/nobg_dm"
self.rend = rtr.RealTimeRenderer(self.batch_size)
self.rend.load_model_names("data/cars_training.txt")
self.test_images1, self.test_images2,\
self.test_dm2, self.test_labels = load_test_set(True, False)
def buildModel(self):
self.images1 = tf.placeholder(tf.float32,
[self.batch_size] + self.input_shape,
name='input_images')
self.images2 = tf.placeholder(tf.float32,
[self.batch_size] + self.output_shape,
name='gt_images')
self.labels = tf.placeholder(tf.float32, [self.batch_size, 5],
name='labels')
# convolutional encoder
e0 = lrelu(conv2d_msra(self.images1, 32, 5, 5, 2, 2, "e0"))
e0_0 = lrelu(conv2d_msra(e0, 32, 5, 5, 1, 1, "e0_0"))
e1 = lrelu(conv2d_msra(e0_0, 32, 5, 5, 2, 2, "e1"))
e1_0 = lrelu(conv2d_msra(e1, 32, 5, 5, 1, 1, "e1_0"))
e2 = lrelu(conv2d_msra(e1_0, 64, 5, 5, 2, 2, "e2"))
e2_0 = lrelu(conv2d_msra(e2, 64, 5, 5, 1, 1, "e2_0"))
e3 = lrelu(conv2d_msra(e2_0, 128, 3, 3, 2, 2, "e3"))
e3_0 = lrelu(conv2d_msra(e3, 128, 3, 3, 1, 1, "e3_0"))
e4 = lrelu(conv2d_msra(e3_0, 256, 3, 3, 2, 2, "e4"))
e4_0 = lrelu(conv2d_msra(e4, 256, 3, 3, 1, 1, "e4_0"))
e4r = tf.reshape(e4_0, [self.batch_size, 4096])
e5 = lrelu(linear_msra(e4r, 4096, "fc1"))
# angle processing
a0 = lrelu(linear_msra(self.labels, 64, "a0"))
a1 = lrelu(linear_msra(a0, 64, "a1"))
a2 = lrelu(linear_msra(a1, 64, "a2"))
concated = tf.concat(1, [e5, a2])
# joint processing
a3 = lrelu(linear_msra(concated, 4096, "a3"))
a4 = lrelu(linear_msra(a3, 4096, "a4"))
a5 = lrelu(linear_msra(a4, 4096, "a5"))
a5r = tf.reshape(a5, [self.batch_size, 4, 4, 256])
# convolutional decoder
d4 = lrelu(deconv2d_msra(a5r, [self.batch_size, 8, 8, 128],
3, 3, 2, 2, "d4"))
d4_0 = lrelu(conv2d_msra(d4, 128, 3, 3, 1, 1, "d4_0"))
d3 = lrelu(deconv2d_msra(d4_0, [self.batch_size, 16, 16, 64],
3, 3, 2, 2, "d3"))
d3_0 = lrelu(conv2d_msra(d3, 64, 5, 5, 1, 1, "d3_0"))
d2 = lrelu(deconv2d_msra(d3_0, [self.batch_size, 32, 32, 32],
5, 5, 2, 2, "d2"))
d2_0 = lrelu(conv2d_msra(d2, 64, 5, 5, 1, 1, "d2_0"))
d1 = lrelu(deconv2d_msra(d2_0, [self.batch_size, 64, 64, 32],
5, 5, 2, 2, "d1"))
d1_0 = lrelu(conv2d_msra(d1, 32, 5, 5, 1, 1, "d1_0"))
self.gen = tf.nn.tanh(deconv2d_msra(d1_0,
[self.batch_size, 128, 128, 4],
5, 5, 2, 2, "d0"))
gt_cm = tf.slice(self.images2, [0, 0, 0, 0], [64, 128, 128, 3])
gt_dm = tf.slice(self.images2, [0, 0, 0, 3], [64, 128, 128, 1])
pr_cm = tf.slice(self.gen, [0, 0, 0, 0], [64, 128, 128, 3])
pr_dm = tf.slice(self.gen, [0, 0, 0, 3], [64, 128, 128, 1])
self.loss = euclidean_loss(gt_cm, pr_cm) +\
0.1 * l1_loss(gt_dm, pr_dm)
self.training_summ = tf.scalar_summary("training_loss", self.loss)
self.t_vars = tf.trainable_variables()
self.saver = tf.train.Saver(max_to_keep=20)
def generate_sample_set(self, path, im1, im2, gen, iter_num, dm=False):
save_images(gen[:, :, :, 0:3], [8, 8],
path + "/output_%s.png" % (iter_num))
if dm is True:
save_images(gen[:, :, :, 3], [8, 8],
path + "/dm_%s.png" % (iter_num), False)
save_images(np.array(im2)[:, :, :, 0:3], [8, 8],
path + '/tr_gt_%s.png' % (iter_num))
if dm is True:
save_images(np.array(im2)[:, :, :, 3], [8, 8],
path + '/dm_gt_%s.png' % (iter_num), False)
save_images(np.array(im1), [8, 8],
path + '/tr_input_%s.png' % (iter_num))
def restore(self):
self.start_iter = load_snapshot(self.saver, self.sess,
self.snapshots_folder)
def test(self, global_iter):
self.test_iter = 19
sm_path = os.path.join(self.test_samples_folder,
str(global_iter).zfill(8))
if not os.path.exists(sm_path):
os.mkdir(sm_path)
local_loss = 0.0
for i in range(0, self.test_iter):
batch_images1 = self.test_images1[i*self.batch_size:
(i+1)*self.batch_size]
cl_im = np.asarray(self.test_images2[i*self.batch_size:
(i+1)*self.batch_size])
dm_im = np.asarray(self.test_dm2[i*self.batch_size:
(i+1)*self.batch_size]).reshape(
(self.batch_size, 128, 128, 1))
batch_images2 = np.concatenate((cl_im, dm_im), axis=3)
batch_labels = self.test_labels[i*self.batch_size:
(i+1)*self.batch_size]
output = self.sess.run([self.gen, self.loss],
feed_dict={
self.images1: batch_images1,
self.images2: batch_images2,
self.labels: batch_labels})
self.generate_sample_set(sm_path, batch_images1,
batch_images2, output[0], i, True)
local_loss += float(output[1])
total_loss = local_loss / self.test_iter
print("[i: %s] [test loss: %.6f]" %
(global_iter, total_loss))
if self.writer is not None:
log_value(self.writer, total_loss, 'test_loss', global_iter)
def train(self):
optim = tf.train.AdamOptimizer(
0.0001, beta1=0.9).minimize(
self.loss, var_list=self.t_vars)
self.writer = tf.train.SummaryWriter(
self.log_folder, self.sess.graph_def)
tf.global_variables_initializer().run()
self.restore()
for i in range(self.start_iter, self.max_iter):
iteration_start_time = time.time()
cl1, dm1, cl2, dm2, lb = self.rend.render(100.0)
full_output = np.concatenate(
(cl2, dm2.reshape(self.batch_size, 128, 128, 1)), axis=3)
output = self.sess.run(
[optim, self.loss, self.training_summ],
feed_dict={self.images1: cl1,
self.images2: full_output,
self.labels: lb})
if np.mod(i, 400) == 0:
self.test(i)
sm_path = os.path.join(self.train_samples_folder,
str(i).zfill(8))
if not os.path.exists(sm_path):
os.mkdir(sm_path)
sm = self.sess.run(self.gen,
feed_dict={self.images1: cl1,
self.images2: full_output,
self.labels: lb})
self.generate_sample_set(sm_path, cl1,
full_output, sm, i, True)
if np.mod(i, 5000) == 1:
save_snapshot(self.saver, self.sess,
self.snapshots_folder, i)
if np.mod(i, 10) == 0:
summ_str = output[2]
self.writer.add_summary(summ_str, i)
print("[i: %s] [time: %s] [global time: %s] [train loss: %.6f]" %
(i, time.time() - iteration_start_time,
time.time() - global_start_time, output[1]))
global_start_time = time.time()
with tf.Session() as sess:
net = mv3d(sess)
net.buildModel()
# ---TEST---
net.restore()
net.test(0)
# ---TRAIN---
# net.train()