File size: 8,918 Bytes
2571f24 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | """Weights and derivatives of spline orders 0 to 7."""
import torch
from enum import Enum
from .jit_utils import square, cube, pow4, pow5, pow6, pow7
class InterpolationType(Enum):
nearest = zeroth = 0
linear = first = 1
quadratic = second = 2
cubic = third = 3
fourth = 4
fifth = 5
sixth = 6
seventh = 7
@torch.jit.script
class Spline:
def __init__(self, order: int = 1):
self.order = order
def weight(self, x):
w = self.fastweight(x)
zero = torch.zeros([1], dtype=x.dtype, device=x.device)
w = torch.where(x.abs() >= (self.order + 1)/2, zero, w)
return w
def fastweight(self, x):
if self.order == 0:
return torch.ones(x.shape, dtype=x.dtype, device=x.device)
x = x.abs()
if self.order == 1:
return 1 - x
if self.order == 2:
x_low = 0.75 - square(x)
x_up = 0.5 * square(1.5 - x)
return torch.where(x < 0.5, x_low, x_up)
if self.order == 3:
x_low = (x * x * (x - 2.) * 3. + 4.) / 6.
x_up = cube(2. - x) / 6.
return torch.where(x < 1., x_low, x_up)
if self.order == 4:
x_low = square(x)
x_low = x_low * (x_low * 0.25 - 0.625) + 115. / 192.
x_mid = x * (x * (x * (5. - x) / 6. - 1.25) + 5./24.) + 55./96.
x_up = pow4(x - 2.5) / 24.
return torch.where(x < 0.5, x_low, torch.where(x < 1.5, x_mid, x_up))
if self.order == 5:
x_low = square(x)
x_low = x_low * (x_low * (0.25 - x / 12.) - 0.5) + 0.55
x_mid = x * (x * (x * (x * (x / 24. - 0.375) + 1.25) - 1.75) + 0.625) + 0.425
x_up = pow5(3 - x) / 120.
return torch.where(x < 1., x_low, torch.where(x < 2., x_mid, x_up))
if self.order == 6:
x_low = square(x)
x_low = x_low * (x_low * (7./48. - x_low/36.) - 77./192.) + 5887./11520.
x_mid_low = (x * (x * (x * (x * (x * (x / 48. - 7./48.) + 0.328125)
- 35./288.) - 91./256.) - 7./768.) + 7861./15360.)
x_mid_up = (x * (x * (x * (x * (x * (7./60. - x / 120.) - 0.65625)
+ 133./72.) - 2.5703125) + 1267./960.) + 1379./7680.)
x_up = pow6(x - 3.5) / 720.
return torch.where(x < .5, x_low,
torch.where(x < 1.5, x_mid_low,
torch.where(x < 2.5, x_mid_up, x_up)))
if self.order == 7:
x_low = square(x)
x_low = (x_low * (x_low * (x_low * (x / 144. - 1./36.)
+ 1./9.) - 1./3.) + 151./315.)
x_mid_low = (x * (x * (x * (x * (x * (x * (0.05 - x/240.) - 7./30.)
+ 0.5) - 7./18.) - 0.1) - 7./90.) + 103./210.)
x_mid_up = (x * (x * (x * (x * (x * (x * (x / 720. - 1./36.)
+ 7./30.) - 19./18.) + 49./18.) - 23./6.) + 217./90.)
- 139./630.)
x_up = pow7(4 - x) / 5040.
return torch.where(x < 1., x_low,
torch.where(x < 2., x_mid_low,
torch.where(x < 3., x_mid_up, x_up)))
raise NotImplementedError
def grad(self, x):
if self.order == 0:
return torch.zeros(x.shape, dtype=x.dtype, device=x.device)
g = self.fastgrad(x)
zero = torch.zeros([1], dtype=x.dtype, device=x.device)
g = torch.where(x.abs() >= (self.order + 1)/2, zero, g)
return g
def fastgrad(self, x):
if self.order == 0:
return torch.zeros(x.shape, dtype=x.dtype, device=x.device)
return self._fastgrad(x.abs()).mul(x.sign())
def _fastgrad(self, x):
if self.order == 1:
return torch.ones(x.shape, dtype=x.dtype, device=x.device)
if self.order == 2:
return torch.where(x < 0.5, -2*x, x - 1.5)
if self.order == 3:
g_low = x * (x * 1.5 - 2)
g_up = -0.5 * square(2 - x)
return torch.where(x < 1, g_low, g_up)
if self.order == 4:
g_low = x * (square(x) - 1.25)
g_mid = x * (x * (x * (-2./3.) + 2.5) - 2.5) + 5./24.
g_up = cube(2. * x - 5.) / 48.
return torch.where(x < 0.5, g_low,
torch.where(x < 1.5, g_mid, g_up))
if self.order == 5:
g_low = x * (x * (x * (x * (-5./12.) + 1.)) - 1.)
g_mid = x * (x * (x * (x * (5./24.) - 1.5) + 3.75) - 3.5) + 0.625
g_up = pow4(x - 3.) / (-24.)
return torch.where(x < 1, g_low,
torch.where(x < 2, g_mid, g_up))
if self.order == 6:
g_low = square(x)
g_low = x * (g_low * (7./12.) - square(g_low) / 6. - 77./96.)
g_mid_low = (x * (x * (x * (x * (x * 0.125 - 35./48.) + 1.3125)
- 35./96.) - 0.7109375) - 7./768.)
g_mid_up = (x * (x * (x * (x * (x / (-20.) + 7./12.) - 2.625)
+ 133./24.) - 5.140625) + 1267./960.)
g_up = pow5(2*x - 7) / 3840.
return torch.where(x < 0.5, g_low,
torch.where(x < 1.5, g_mid_low,
torch.where(x < 2.5, g_mid_up,
g_up)))
if self.order == 7:
g_low = square(x)
g_low = x * (g_low * (g_low * (x * (7./144.) - 1./6.) + 4./9.) - 2./3.)
g_mid_low = (x * (x * (x * (x * (x * (x * (-7./240.) + 3./10.)
- 7./6.) + 2.) - 7./6.) - 1./5.) - 7./90.)
g_mid_up = (x * (x * (x * (x * (x * (x * (7./720.) - 1./6.)
+ 7./6.) - 38./9.) + 49./6.) - 23./3.) + 217./90.)
g_up = pow6(x - 4) / (-720.)
return torch.where(x < 1, g_low,
torch.where(x < 2, g_mid_low,
torch.where(x < 3, g_mid_up, g_up)))
raise NotImplementedError
def hess(self, x):
if self.order == 0:
return torch.zeros(x.shape, dtype=x.dtype, device=x.device)
h = self.fasthess(x)
zero = torch.zeros([1], dtype=x.dtype, device=x.device)
h = torch.where(x.abs() >= (self.order + 1)/2, zero, h)
return h
def fasthess(self, x):
if self.order in (0, 1):
return torch.zeros(x.shape, dtype=x.dtype, device=x.device)
x = x.abs()
if self.order == 2:
one = torch.ones([1], dtype=x.dtype, device=x.device)
return torch.where(x < 0.5, -2 * one, one)
if self.order == 3:
return torch.where(x < 1, 3. * x - 2., 2. - x)
if self.order == 4:
return torch.where(x < 0.5, 3. * square(x) - 1.25,
torch.where(x < 1.5, x * (-2. * x + 5.) - 2.5,
square(2. * x - 5.) / 8.))
if self.order == 5:
h_low = square(x)
h_low = - h_low * (x * (5./3.) - 3.) - 1.
h_mid = x * (x * (x * (5./6.) - 9./2.) + 15./2.) - 7./2.
h_up = 9./2. - x * (x * (x/6. - 3./2.) + 9./2.)
return torch.where(x < 1, h_low,
torch.where(x < 2, h_mid, h_up))
if self.order == 6:
h_low = square(x)
h_low = - h_low * (h_low * (5./6) - 7./4.) - 77./96.
h_mid_low = (x * (x * (x * (x * (5./8.) - 35./12.) + 63./16.)
- 35./48.) - 91./128.)
h_mid_up = -(x * (x * (x * (x/4. - 7./3.) + 63./8.) - 133./12.)
+ 329./64.)
h_up = (x * (x * (x * (x/24. - 7./12.) + 49./16.) - 343./48.)
+ 2401./384.)
return torch.where(x < 0.5, h_low,
torch.where(x < 1.5, h_mid_low,
torch.where(x < 2.5, h_mid_up,
h_up)))
if self.order == 7:
h_low = square(x)
h_low = h_low * (h_low*(x * (7./24.) - 5./6.) + 4./3.) - 2./3.
h_mid_low = - (x * (x * (x * (x * (x * (7./40.) - 3./2.) + 14./3.)
- 6.) + 7./3.) + 1./5.)
h_mid_up = (x * (x * (x * (x * (x * (7./120.) - 5./6.) + 14./3.)
- 38./3.) + 49./3.) - 23./3.)
h_up = - (x * (x * (x * (x * (x/120. - 1./6.) + 4./3.) - 16./3.)
+ 32./3.) - 128./15.)
return torch.where(x < 1, h_low,
torch.where(x < 2, h_mid_low,
torch.where(x < 3, h_mid_up,
h_up)))
raise NotImplementedError
|