-
Notifications
You must be signed in to change notification settings - Fork 26
/
Copy pathCyclicGen_model.py
135 lines (104 loc) · 5.84 KB
/
CyclicGen_model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
"""Implements a voxel flow model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
from utils.loss_utils import l1_loss, l2_loss, vae_loss
from utils.geo_layer_utils import vae_gaussian_layer
from utils.geo_layer_utils import bilinear_interp
from utils.geo_layer_utils import meshgrid
FLAGS = tf.app.flags.FLAGS
epsilon = 0.001
class Voxel_flow_model(object):
def __init__(self, is_train=True, is_extrapolation=False):
self.is_train = is_train
self.is_extrapolation = is_extrapolation
def inference(self, input_images):
"""Inference on a set of input_images.
Args:
"""
return self._build_model(input_images)
def total_var(self, images):
pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
tot_var = (tf.reduce_mean(tf.sqrt(tf.square(pixel_dif1) + epsilon**2)) + tf.reduce_mean(tf.sqrt(tf.square(pixel_dif2) + epsilon**2)))
return tot_var
def loss(self, predictions, targets):
"""Compute the necessary loss for training.
Args:
Returns:
"""
# self.reproduction_loss = l1_loss(predictions, targets)
self.reproduction_loss = tf.reduce_mean(tf.sqrt(tf.square(predictions - targets) + epsilon**2))
self.motion_loss = self.total_var(self.flow)
self.mask_loss = self.total_var(self.mask)
# return [self.reproduction_loss, self.prior_loss]
return self.reproduction_loss + 0.01 * self.motion_loss + 0.005 * self.mask_loss
def l1loss(self, predictions, targets):
self.reproduction_loss = l1_loss(predictions, targets)
return self.reproduction_loss
def _build_model(self, input_images):
with slim.arg_scope([slim.conv2d],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0001)):
# Define network
batch_norm_params = {
'decay': 0.9997,
'epsilon': 0.001,
'is_training': self.is_train,
}
with slim.arg_scope([slim.batch_norm], is_training=self.is_train, updates_collections=None):
with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params):
x0 = slim.conv2d(input_images, 64, [5, 5], stride=1, scope='conv1')
# with tf.name_scope('conv1') as scope:
# kernel = tf.Variable(tf.truncated_normal([5, 5, 8, 64], dtype=tf.float32, stddev=1e-1), name='weights')
# conv = tf.nn.atrous_conv2d(input_images, kernel, 2, padding='SAME')
# x0 = tf.nn.relu(conv, name=scope)
net = slim.max_pool2d(x0, [2, 2], scope='pool1')
x1 = slim.conv2d(net, 128, [5, 5], stride=1, scope='conv2')
net = slim.max_pool2d(x1, [2, 2], scope='pool2')
x2 = slim.conv2d(net, 256, [3, 3], stride=1, scope='conv3')
net = slim.max_pool2d(x2, [2, 2], scope='pool3')
net = slim.conv2d(net, 256, [3, 3], stride=1, scope='conv4')
net = tf.image.resize_bilinear(net, [x2.get_shape().as_list()[1], x2.get_shape().as_list()[2]])
net = slim.conv2d(tf.concat([net, x2], -1), 256, [3, 3], stride=1, scope='conv5')
net = tf.image.resize_bilinear(net, [x1.get_shape().as_list()[1], x1.get_shape().as_list()[2]])
net = slim.conv2d(tf.concat([net, x1], -1), 128, [3, 3], stride=1, scope='conv6')
net = tf.image.resize_bilinear(net, [x0.get_shape().as_list()[1], x0.get_shape().as_list()[2]])
y0 = slim.conv2d(tf.concat([net, x0], -1), 64, [5, 5], stride=1, scope='conv7')
net = slim.conv2d(y0, 3, [5, 5], stride=1, activation_fn=tf.tanh,
normalizer_fn=None, scope='conv8')
net_copy = net
flow = net[:, :, :, 0:2]
mask = tf.expand_dims(net[:, :, :, 2], 3)
self.flow = flow
grid_x, grid_y = meshgrid(x0.get_shape().as_list()[1], x0.get_shape().as_list()[2])
grid_x = tf.tile(grid_x, [FLAGS.batch_size, 1, 1])
grid_y = tf.tile(grid_y, [FLAGS.batch_size, 1, 1])
flow = 0.5 * flow
flow_ratio = tf.constant([255.0 / (x0.get_shape().as_list()[2]-1), 255.0 / (x0.get_shape().as_list()[1]-1)])
flow = flow * tf.expand_dims(tf.expand_dims(tf.expand_dims(flow_ratio, 0), 0), 0)
if self.is_extrapolation:
coor_x_1 = grid_x + flow[:, :, :, 0] * 2
coor_y_1 = grid_y + flow[:, :, :, 1] * 2
coor_x_2 = grid_x + flow[:, :, :, 0]
coor_y_2 = grid_y + flow[:, :, :, 1]
else:
coor_x_1 = grid_x + flow[:, :, :, 0]
coor_y_1 = grid_y + flow[:, :, :, 1]
coor_x_2 = grid_x - flow[:, :, :, 0]
coor_y_2 = grid_y - flow[:, :, :, 1]
output_1 = bilinear_interp(input_images[:, :, :, 0:3], coor_x_1, coor_y_1, 'interpolate')
output_2 = bilinear_interp(input_images[:, :, :, 3:6], coor_x_2, coor_y_2, 'interpolate')
self.warped_img1 = output_1
self.warped_img2 = output_2
self.warped_flow1 = bilinear_interp(-flow[:, :, :, 0:3]*0.5, coor_x_1, coor_y_1, 'interpolate')
self.warped_flow2 = bilinear_interp(flow[:, :, :, 0:3]*0.5, coor_x_2, coor_y_2, 'interpolate')
mask = 0.5 * (1.0 + mask)
self.mask = mask
mask = tf.tile(mask, [1, 1, 1, 3])
net = tf.multiply(mask, output_1) + tf.multiply(1.0 - mask, output_2)
return [net, net_copy]