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optical_flow_warp_fwd.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from tensorflow.python.platform import app
import numpy as np
import pdb
def transformerFwd(U,
flo,
out_size,
name='SpatialTransformerFwd',
backprop=False,
**kwargs):
"""Forward Warping Layer described in
'Occlusion Aware Unsupervised Learning of Optical Flow by Yang Wang et al'
Parameters
----------
U : float
The output of a convolutional net should have the
shape [num_batch, height, width, num_channels].
flo: float
The optical flow used for forward warping
having the shape of [num_batch, height, width, 2].
backprop: boolean
Indicates whether to back-propagate through forward warping layer
out_size: tuple of two ints
The size of the output of the network (height, width)
"""
def _repeat(x, n_repeats):
with tf.variable_scope('_repeat'):
rep = tf.transpose(
tf.expand_dims(
tf.ones(shape=tf.stack([int(n_repeats), ])), 1), [1, 0])
rep = tf.cast(rep, 'int32')
x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
return tf.reshape(x, [-1])
def _interpolate(im, x, y, out_size):
with tf.variable_scope('_interpolate'):
# constants
num_batch = tf.shape(im)[0]
height = tf.shape(im)[1]
width = tf.shape(im)[2]
channels = tf.shape(im)[3]
x = tf.cast(x, 'float32')
y = tf.cast(y, 'float32')
height_f = tf.cast(height, 'float32')
width_f = tf.cast(width, 'float32')
out_height = out_size[0]
out_width = out_size[1]
zero = tf.zeros([], dtype='int32')
max_y = tf.cast(tf.shape(im)[1] - 1, 'int32')
max_x = tf.cast(tf.shape(im)[2] - 1, 'int32')
# scale indices from [-1, 1] to [0, width/height]
x = (x + 1.0) * (width_f - 1) / 2.0
y = (y + 1.0) * (height_f - 1) / 2.0
# do sampling
x0 = tf.cast(tf.floor(x), 'int32')
x1 = x0 + 1
y0 = tf.cast(tf.floor(y), 'int32')
y1 = y0 + 1
x0_c = tf.clip_by_value(x0, zero, max_x)
x1_c = tf.clip_by_value(x1, zero, max_x)
y0_c = tf.clip_by_value(y0, zero, max_y)
y1_c = tf.clip_by_value(y1, zero, max_y)
dim2 = width
dim1 = width * height
base = _repeat(tf.range(num_batch) * dim1, out_height * out_width)
base_y0 = base + y0_c * dim2
base_y1 = base + y1_c * dim2
idx_a = base_y0 + x0_c
idx_b = base_y1 + x0_c
idx_c = base_y0 + x1_c
idx_d = base_y1 + x1_c
# use indices to lookup pixels in the flat image and restore
# channels dim
im_flat = tf.reshape(im, tf.stack([-1, channels]))
im_flat = tf.cast(im_flat, 'float32')
# and finally calculate interpolated values
x0_f = tf.cast(x0, 'float32')
x1_f = tf.cast(x1, 'float32')
y0_f = tf.cast(y0, 'float32')
y1_f = tf.cast(y1, 'float32')
wa = tf.expand_dims(((x1_f - x) * (y1_f - y)), 1)
wb = tf.expand_dims(((x1_f - x) * (y - y0_f)), 1)
wc = tf.expand_dims(((x - x0_f) * (y1_f - y)), 1)
wd = tf.expand_dims(((x - x0_f) * (y - y0_f)), 1)
zerof = tf.zeros_like(wa)
wa = tf.where(
tf.logical_and(tf.equal(x0_c, x0), tf.equal(y0_c, y0)), wa,
zerof)
wb = tf.where(
tf.logical_and(tf.equal(x0_c, x0), tf.equal(y1_c, y1)), wb,
zerof)
wc = tf.where(
tf.logical_and(tf.equal(x1_c, x1), tf.equal(y0_c, y0)), wc,
zerof)
wd = tf.where(
tf.logical_and(tf.equal(x1_c, x1), tf.equal(y1_c, y1)), wd,
zerof)
if not backprop:
zeros = tf.zeros(
shape=[
int(im.get_shape()[0]) * int(im.get_shape()[1]) *
int(im.get_shape()[2]), int(im.get_shape()[3])
],
dtype='float32')
output = tf.Variable(
zeros,
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
init = tf.assign(output, zeros)
# tf.scatter_add will not back-propagate gradients
with tf.control_dependencies([init]):
output = tf.scatter_add(output, idx_a, im_flat * wa)
output = tf.scatter_add(output, idx_b, im_flat * wb)
output = tf.scatter_add(output, idx_c, im_flat * wc)
output = tf.scatter_add(output, idx_d, im_flat * wd)
else:
shape = [
int(im.get_shape()[0]) * int(im.get_shape()[1]) *
int(im.get_shape()[2]), int(im.get_shape()[3])
]
output = tf.scatter_nd(tf.expand_dims(idx_a, -1), im_flat*wa, shape) + \
tf.scatter_nd(tf.expand_dims(idx_b, -1), im_flat*wb, shape) + \
tf.scatter_nd(tf.expand_dims(idx_c, -1), im_flat*wc, shape) + \
tf.scatter_nd(tf.expand_dims(idx_d, -1), im_flat*wd, shape)
return output
def _meshgrid(height, width):
with tf.variable_scope('_meshgrid'):
# This should be equivalent to:
# x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),
# np.linspace(-1, 1, height))
# ones = np.ones(np.prod(x_t.shape))
# grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])
x_t = tf.matmul(
tf.ones(shape=tf.stack([int(height), 1])),
tf.transpose(
tf.expand_dims(tf.linspace(-1.0, 1.0, int(width)), 1), [1, 0]))
y_t = tf.matmul(
tf.expand_dims(tf.linspace(-1.0, 1.0, int(height)), 1),
tf.ones(shape=tf.stack([1, int(width)])))
return x_t, y_t
def _transform(flo, input_dim, out_size):
with tf.variable_scope('_transform'):
num_batch = tf.shape(input_dim)[0]
height = tf.shape(input_dim)[1]
width = tf.shape(input_dim)[2]
num_channels = tf.shape(input_dim)[3]
# grid of (x_t, y_t, 1), eq (1) in ref [1]
height_f = tf.cast(height, 'float32')
width_f = tf.cast(width, 'float32')
out_height = out_size[0]
out_width = out_size[1]
x_s, y_s = _meshgrid(out_height, out_width)
x_s = tf.expand_dims(x_s, 0)
x_s = tf.tile(x_s, [num_batch, 1, 1])
y_s = tf.expand_dims(y_s, 0)
y_s = tf.tile(y_s, [num_batch, 1, 1])
x_t = x_s + flo[:, :, :, 0] / ((out_width - 1.0) / 2.0)
y_t = y_s + flo[:, :, :, 1] / ((out_height - 1.0) / 2.0)
x_t_flat = tf.reshape(x_t, [-1])
y_t_flat = tf.reshape(y_t, [-1])
input_transformed = _interpolate(input_dim, x_t_flat, y_t_flat, out_size)
output = tf.reshape(
input_transformed,
tf.stack([num_batch, int(out_height), int(out_width), num_channels]))
return output
with tf.variable_scope(name):
output = _transform(flo, U, out_size)
return output