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utils.py
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utils.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import cv2
import copy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle, Circle
import h5py
import os
import torch
from torch.autograd import Variable
def rand_int(lo, hi):
return np.random.randint(lo, hi)
def rand_float(lo, hi):
return np.random.rand() * (hi - lo) + lo
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def to_variable(tensor, use_gpu, requires_grad=False):
if use_gpu:
return Variable(torch.FloatTensor(tensor).cuda(),
requires_grad=requires_grad)
else:
return Variable(torch.FloatTensor(tensor),
requires_grad=requires_grad)
def visualize_point_clouds(point_clouds, c=['b', 'r'], view=None, store=False, store_path=''):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_aspect('equal')
frame = plt.gca()
frame.axes.xaxis.set_ticklabels([])
frame.axes.yaxis.set_ticklabels([])
frame.axes.zaxis.set_ticklabels([])
for i in range(len(point_clouds)):
points = point_clouds[i]
ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=c[i], s=10, alpha=0.3)
X, Y, Z = point_clouds[0][:, 0], point_clouds[0][:, 1], point_clouds[0][:, 2]
max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max()
Xb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][0].flatten() + 0.5*(X.max()+X.min())
Yb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][1].flatten() + 0.5*(Y.max()+Y.min())
Zb = 0.5*max_range*np.mgrid[-1:2:2,-1:2:2,-1:2:2][2].flatten() + 0.5*(Z.max()+Z.min())
# Comment or uncomment following both lines to test the fake bounding box:
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
ax.grid(False)
if view is None:
view = 0, 0
ax.view_init(view[0], view[1])
plt.show()
# plt.pause(5)
if store:
fig.savefig(store_path, bbox_inches='tight')
def sample_control_RiceGrip():
dis = np.random.rand() * 0.5
angle = np.random.rand() * np.pi * 2.
x = np.cos(angle) * dis
z = np.sin(angle) * dis
d = np.random.rand() * 0.3 + 0.7 # (0.6, 0.9)
return x, z, d
def sample_control_FluidShake(x_box, time_step, dt):
control = np.zeros(time_step)
v_box = 0.
for step in range(time_step):
control[step] = v_box
x_box += v_box * dt
v_box += rand_float(-0.15, 0.15) - x_box * 0.1
return control
def quatFromAxisAngle(axis, angle):
axis /= np.linalg.norm(axis)
half = angle * 0.5
w = np.cos(half)
sin_theta_over_two = np.sin(half)
axis *= sin_theta_over_two
quat = np.array([axis[0], axis[1], axis[2], w])
return quat
def quatFromAxisAngle_var(axis, angle):
axis /= torch.norm(axis)
half = angle * 0.5
w = torch.cos(half)
sin_theta_over_two = torch.sin(half)
axis *= sin_theta_over_two
quat = torch.cat([axis, w])
# print("quat size", quat.size())
return quat
def calc_shape_states_RiceGrip(t, dt, shape_state_dim, gripper_config):
rest_gripper_dis = 1.8
x, z, d = gripper_config
s = (rest_gripper_dis - d) / 2.
half_rest_gripper_dis = rest_gripper_dis / 2.
time = max(0., t) * 5
lastTime = max(0., t - dt) * 5
states = np.zeros((2, shape_state_dim))
dis = np.sqrt(x**2 + z**2)
angle = np.array([-z / dis, x / dis])
quat = quatFromAxisAngle(np.array([0., 1., 0.]), np.arctan(x / z))
e_0 = np.array([x + z * half_rest_gripper_dis / dis, z - x * half_rest_gripper_dis / dis])
e_1 = np.array([x - z * half_rest_gripper_dis / dis, z + x * half_rest_gripper_dis / dis])
e_0_curr = e_0 + angle * np.sin(time) * s
e_1_curr = e_1 - angle * np.sin(time) * s
e_0_last = e_0 + angle * np.sin(lastTime) * s
e_1_last = e_1 - angle * np.sin(lastTime) * s
states[0, :3] = np.array([e_0_curr[0], 0.6, e_0_curr[1]])
states[0, 3:6] = np.array([e_0_last[0], 0.6, e_0_last[1]])
states[0, 6:10] = quat
states[0, 10:14] = quat
states[1, :3] = np.array([e_1_curr[0], 0.6, e_1_curr[1]])
states[1, 3:6] = np.array([e_1_last[0], 0.6, e_1_last[1]])
states[1, 6:10] = quat
states[1, 10:14] = quat
return states
def calc_shape_states_RiceGrip_var(t, dt, gripper_config):
rest_gripper_dis = Variable(torch.FloatTensor([1.8]).cuda())
x, z, d = gripper_config[0:1], gripper_config[1:2], gripper_config[2:3]
s = (rest_gripper_dis - d) / 2.
half_rest_gripper_dis = rest_gripper_dis / 2.
time = max(0., t) * 5
lastTime = max(0., t - dt) * 5
dis = torch.sqrt(x**2 + z**2)
angle = torch.cat([-z / dis, x / dis])
quat = quatFromAxisAngle_var(Variable(torch.FloatTensor([0., 1., 0.]).cuda()), torch.atan(x / z))
e_0 = torch.cat([x + z * half_rest_gripper_dis / dis, z - x * half_rest_gripper_dis / dis])
e_1 = torch.cat([x - z * half_rest_gripper_dis / dis, z + x * half_rest_gripper_dis / dis])
e_0_curr = e_0 + angle * np.sin(time) * s
e_1_curr = e_1 - angle * np.sin(time) * s
e_0_last = e_0 + angle * np.sin(lastTime) * s
e_1_last = e_1 - angle * np.sin(lastTime) * s
y = Variable(torch.FloatTensor([0.6]).cuda())
states_0 = torch.cat([e_0_curr[0:1], y, e_0_curr[1:2], e_0_last[0:1], y, e_0_last[1:2], quat, quat])
states_1 = torch.cat([e_1_curr[0:1], y, e_1_curr[1:2], e_1_last[0:1], y, e_1_last[1:2], quat, quat])
# print(states_0.requires_grad, states_1.requires_grad)
# print("gripper #0:", states_0.size())
# print("gripper #1:", states_1.size())
return torch.cat([states_0.view(1, -1), states_1.view(1, -1)], 0)
def calc_box_init_FluidShake(dis_x, dis_z, height, border):
center = np.array([0., 0., 0.])
quat = np.array([1., 0., 0., 0.])
boxes = []
# floor
halfEdge = np.array([dis_x/2., border/2., dis_z/2.])
boxes.append([halfEdge, center, quat])
# left wall
halfEdge = np.array([border/2., (height+border)/2., dis_z/2.])
boxes.append([halfEdge, center, quat])
# right wall
boxes.append([halfEdge, center, quat])
# back wall
halfEdge = np.array([(dis_x+border*2)/2., (height+border)/2., border/2.])
boxes.append([halfEdge, center, quat])
# front wall
boxes.append([halfEdge, center, quat])
return boxes
def calc_shape_states_FluidShake(x_curr, x_last, box_dis, height, border):
dis_x, dis_z = box_dis
quat = np.array([1., 0., 0., 0.])
states = np.zeros((5, 14))
states[0, :3] = np.array([x_curr, border/2., 0.])
states[0, 3:6] = np.array([x_last, border/2., 0.])
states[1, :3] = np.array([x_curr-(dis_x+border)/2., (height+border)/2., 0.])
states[1, 3:6] = np.array([x_last-(dis_x+border)/2., (height+border)/2., 0.])
states[2, :3] = np.array([x_curr+(dis_x+border)/2., (height+border)/2., 0.])
states[2, 3:6] = np.array([x_last+(dis_x+border)/2., (height+border)/2., 0.])
states[3, :3] = np.array([x_curr, (height+border)/2., -(dis_z+border)/2.])
states[3, 3:6] = np.array([x_last, (height+border)/2., -(dis_z+border)/2.])
states[4, :3] = np.array([x_curr, (height+border)/2., (dis_z+border)/2.])
states[4, 3:6] = np.array([x_last, (height+border)/2., (dis_z+border)/2.])
states[:, 6:10] = quat
states[:, 10:] = quat
return states
def calc_shape_states_FluidShake_var(x_curr, x_last, box_dis, height, border):
dis_x, dis_z = box_dis
dis_x = Variable(torch.FloatTensor([dis_x]).cuda())
dis_z = Variable(torch.FloatTensor([dis_z]).cuda())
height = Variable(torch.FloatTensor([height]).cuda())
border = Variable(torch.FloatTensor([border]).cuda())
zero = Variable(torch.FloatTensor([0.]).cuda())
quat = Variable(torch.FloatTensor([1., 0., 0., 0.]).cuda())
state_0 = torch.cat([
x_curr, border/2., zero, x_last, border/2., zero, quat, quat]).view(1, -1)
state_1 = torch.cat([
x_curr-(dis_x+border)/2., (height+border)/2., zero,
x_last-(dis_x+border)/2., (height+border)/2., zero,
quat, quat]).view(1, -1)
state_2 = torch.cat([
x_curr+(dis_x+border)/2., (height+border)/2., zero,
x_last+(dis_x+border)/2., (height+border)/2., zero,
quat, quat]).view(1, -1)
state_3 = torch.cat([
x_curr, (height+border)/2., -(dis_z+border)/2.,
x_last, (height+border)/2., -(dis_z+border)/2.,
quat, quat]).view(1, -1)
state_4 = torch.cat([
x_curr, (height+border)/2., (dis_z+border)/2.,
x_last, (height+border)/2., (dis_z+border)/2.,
quat, quat]).view(1, -1)
states = torch.cat([state_0, state_1, state_2, state_3, state_4], 0)
# print("states size", states.size())
return states
class ChamferLoss(torch.nn.Module):
def __init__(self):
super(ChamferLoss, self).__init__()
def chamfer_distance(self, x, y):
# x: [N, D]
# y: [M, D]
x = x.repeat(y.size(0), 1, 1) # x: [M, N, D]
x = x.transpose(0, 1) # x: [N, M, D]
y = y.repeat(x.size(0), 1, 1) # y: [N, M, D]
dis = torch.norm(torch.add(x, -y), 2, dim=2) # dis: [N, M]
dis_xy = torch.mean(torch.min(dis, dim=1)[0]) # dis_xy: mean over N
dis_yx = torch.mean(torch.min(dis, dim=0)[0]) # dis_yx: mean over M
return dis_xy + dis_yx
def __call__(self, pred, label):
return self.chamfer_distance(pred, label)