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CyclicGen_model.py
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CyclicGen_model.py
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"""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]