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tf_utils.py
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tf_utils.py
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from __future__ import print_function
import math
import numpy as np
import tensorflow as tf
import time
import trimesh.transformations as tra
import os
GRIPPER_PC = np.load(
'gripper_models/panda_pc.npy', allow_pickle=True).item()['points']
GRIPPER_PC[:, 3] = 1.
def get_shape(x):
"""
Gets the shape of the tensor x.
"""
return x.get_shape().as_list()
def count_nan(x):
"""
Debug function: counts the nan values in tensor x.
"""
isnan = tf.cast(tf.is_nan(x), tf.int32)
isnan = tf.reshape(isnan, [-1])
return tf.reduce_sum(isnan)
def get_gripper_pc(batch_size, npoints, use_tf=True):
"""
Returns a numpy array or a tensor of shape (batch_size x npoints x 4).
Represents gripper with the sepcified number of points.
use_tf: switches between output tensor or numpy array.
"""
output = np.copy(GRIPPER_PC)
if npoints != -1:
assert(
npoints > 0 and npoints <= output.shape[0]), 'gripper_pc_npoint is too large {} > {}'.format(
npoints, output.shape[0])
output = output[:npoints]
output = np.expand_dims(output, 0)
else:
raise ValueError('npoints should not be -1.')
if use_tf:
output = tf.convert_to_tensor(output, tf.float32)
output = tf.tile(output, [batch_size, 1, 1])
return output
else:
output = np.tile(output, [batch_size, 1, 1])
return output
def get_control_point_tensor(batch_size, use_tf=True):
"""
Outputs a tensor of shape (batch_size x 6 x 3).
use_tf: switches between outputing a tensor and outputing a numpy array.
"""
control_points = np.load('gripper_control_points/panda.npy')[:, :3]
control_points = [[0, 0, 0], [0, 0, 0], control_points[0, :],
control_points[1, :], control_points[-2, :], control_points[-1, :]]
control_points = np.asarray(control_points, dtype=np.float32)
control_points = np.tile(
np.expand_dims(
control_points, 0), [
batch_size, 1, 1])
if use_tf:
return tf.convert_to_tensor(control_points)
return control_points
def transform_control_points(
gt_grasps,
batch_size,
mode='qt',
scope='transform_gt_control_points'):
"""
Transforms canonical points using gt_grasps.
mode = 'qt' expects gt_grasps to have (batch_size x 7) where each
element is concatenation of quaternion and translation for each
grasps.
mode = 'rt': expects to have shape (batch_size x 4 x 4) where
each element is 4x4 transformation matrix of each grasp.
"""
assert(mode == 'qt' or mode == 'rt'), mode
grasp_shape = get_shape(gt_grasps)
if mode == 'qt':
assert(len(grasp_shape) == 2), grasp_shape
assert(grasp_shape[-1] == 7), grasp_shape
with tf.variable_scope(scope):
control_points = get_control_point_tensor(batch_size)
num_control_points = get_shape(control_points)[1]
input_gt_grasps = gt_grasps
gt_grasps = tf.tile(
tf.expand_dims(
input_gt_grasps, 1), [
1, num_control_points, 1])
gt_q = tf.slice(
gt_grasps, [
0, 0, 0], [
get_shape(gt_grasps)[0], get_shape(gt_grasps)[1], 4])
gt_t = tf.slice(
gt_grasps, [
0, 0, 4], [
get_shape(gt_grasps)[0], get_shape(gt_grasps)[1], 3])
gt_control_points = rotate_point_by_quaternion(
control_points, gt_q)
gt_control_points += gt_t
return gt_control_points
else:
assert(len(grasp_shape) == 3), grasp_shape
assert(grasp_shape[1] == 4 and grasp_shape[2] == 4), grasp_shape
with tf.variable_scope(scope):
control_points = get_control_point_tensor(batch_size)
shape = get_shape(control_points)
ones = tf.ones((shape[0], shape[1], 1), dtype=tf.float32)
control_points = tf.concat((control_points, ones), -1)
return tf.matmul(
control_points,
gt_grasps,
transpose_a=False,
transpose_b=True)
def quaternion_mult(Q, R):
"""
Computes the multiplication of quaternions Q and R.
"""
Q_shape = Q.get_shape().as_list()
R_shape = R.get_shape().as_list()
assert (Q_shape[-1] == 4)
assert (R_shape[-1] == 4)
q = tf.split(Q, 4, axis=-1)
r = tf.split(R, 4, axis=-1)
outputs_list = [
r[0] * q[0] - r[1] * q[1] - r[2] * q[2] - r[3] * q[3],
r[0] * q[1] + r[1] * q[0] - r[2] * q[3] + r[3] * q[2],
r[0] * q[2] + r[1] * q[3] + r[2] * q[0] - r[3] * q[1],
r[0] * q[3] - r[1] * q[2] + r[2] * q[1] + r[3] * q[0]
]
outputs = tf.concat(outputs_list, axis=-1)
return outputs
def conj_quaternion(q):
"""
Conjugate of quaternion q.
"""
q_conj = tf.split(q, 4, axis=-1)
q_conj = tf.concat(
[q_conj[0], -q_conj[1], -q_conj[2], -q_conj[3]], axis=-1)
return q_conj
def rotate_point_by_quaternion(point, q):
"""
Takes in points with shape of (batch_size x n x 3) and quaternions with
shape of (batch_size x n x 4) and returns a tensor with shape of
(batch_size x n x 3) which is the rotation of the point with quaternion
q.
"""
shape = point.get_shape().as_list()
q_shape = q.get_shape().as_list()
assert(
len(shape) == 3), 'point shape = {} q shape = {}'.format(
shape, q_shape)
assert(shape[-1] == 3), 'point shape = {} q shape = {}'.format(shape, q_shape)
assert(
len(q_shape) == 3), 'point shape = {} q shape = {}'.format(
shape, q_shape)
assert(q_shape[-1] ==
4), 'point shape = {} q shape = {}'.format(shape, q_shape)
assert(
q_shape[1] == shape[1]), 'point shape = {} q shape = {}'.format(
shape, q_shape)
q_conj = conj_quaternion(q)
r = tf.concat(
[tf.zeros((shape[0], shape[1], 1), dtype=point.dtype), point], axis=-1)
final_point = quaternion_mult(quaternion_mult(q, r), q_conj)
final_output = tf.slice(final_point, [0, 0, 1], shape, name='sliceeeeeee')
return final_output
class QuaternionTest(tf.test.TestCase):
def test_mult(self):
np.random.seed(int(time.time()))
batch_size = 30
control_points = 50
a = np.random.rand(batch_size, control_points, 4)
b = np.random.rand(batch_size, control_points, 4)
norm_a = np.sqrt(np.sum(a * a, axis=-1))
norm_b = np.sqrt(np.sum(b * b, axis=-1))
a /= np.tile(np.expand_dims(norm_a, -1), [1, 1, 4])
b /= np.tile(np.expand_dims(norm_b, -1), [1, 1, 4])
output = np.zeros((batch_size, control_points, 4), dtype=np.float32)
for bindex in range(batch_size):
for c in range(control_points):
output[bindex, c, :] = tra.quaternion_multiply(
a[bindex, c, :], b[bindex, c, :])
ta = tf.convert_to_tensor(a)
tb = tf.convert_to_tensor(b)
tf_output = quaternion_mult(ta, tb)
ok = True
with self.test_session():
if np.all(np.abs(tf_output.eval() - output) < 1e-4):
print('----------> Mult passed')
else:
raise ValueError(
'did not match {} != {}'.format(
tf_output.eval(), output))
def test_rotation(self):
np.random.seed(int(time.time()))
batch_size = 30
control_points = 16
rot_matrix = np.zeros(
(batch_size, control_points, 3, 3), dtype=np.float32)
quat_matrix = np.zeros(
(batch_size, control_points, 4), dtype=np.float32)
points = np.random.rand(batch_size, control_points, 3)
rotated_points = np.random.rand(batch_size, control_points, 3)
for b in range(batch_size):
for c in range(control_points):
angles = np.random.uniform(
low=0, high=math.pi * 2., size=[3, ])
rot_matrix[b, c, :, :] = tra.euler_matrix(
angles[0], angles[1], angles[2])[:3, :3]
quat_matrix[b, c, :] = tra.quaternion_from_euler(
angles[0], angles[1], angles[2])
rotated_points[b, c, :] = np.matmul(
rot_matrix[b, c, :, :], points[b, c, :])
tf_rotated_points = rotate_point_by_quaternion(
tf.convert_to_tensor(
points, dtype=tf.float32), tf.convert_to_tensor(
quat_matrix, dtype=tf.float32))
with self.test_session():
if np.all(
np.abs(
tf_rotated_points.eval() -
rotated_points) < 1e-4):
print('----------> Rotation passed')
else:
raise ValueError(
'test rotatation did not match {} != {}'.format(
tf_rotated_points.eval(), rotated_points))
def tf_rotation_matrix(az, el, th, batched=False):
if batched:
cx = tf.cos(tf.reshape(az, [-1, 1]))
cy = tf.cos(tf.reshape(el, [-1, 1]))
cz = tf.cos(tf.reshape(th, [-1, 1]))
sx = tf.sin(tf.reshape(az, [-1, 1]))
sy = tf.sin(tf.reshape(el, [-1, 1]))
sz = tf.sin(tf.reshape(th, [-1, 1]))
ones = tf.ones_like(cx)
zeros = tf.zeros_like(cx)
rx = tf.concat([ones, zeros, zeros, zeros,
cx, -sx, zeros, sx, cx], axis=-1)
ry = tf.concat([cy, zeros, sy, zeros, ones,
zeros, -sy, zeros, cy], axis=-1)
rz = tf.concat([cz, -sz, zeros, sz, cz, zeros,
zeros, zeros, ones], axis=-1)
rx = tf.reshape(rx, [-1, 3, 3])
ry = tf.reshape(ry, [-1, 3, 3])
rz = tf.reshape(rz, [-1, 3, 3])
return tf.matmul(rz, tf.matmul(ry, rx))
else:
cx = tf.cos(az)
cy = tf.cos(el)
cz = tf.cos(th)
sx = tf.sin(az)
sy = tf.sin(el)
sz = tf.sin(th)
rx = tf.to_float(
tf.stack([[1., 0., 0.], [0, cx, -sx], [0, sx, cx]], axis=0))
ry = tf.to_float(
tf.stack([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]], axis=0))
rz = tf.to_float(
tf.stack([[cz, -sz, 0], [sz, cz, 0], [0, 0, 1]], axis=0))
return tf.matmul(rz, tf.matmul(ry, rx))
if __name__ == '__main__':
tf.test.main()