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helpers.py
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helpers.py
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"""
Copyright 2018 Matthew Mirman
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 torch as torch
import os
import numbers
import math
if torch.cuda.is_available() and not 'NOCUDA' in os.environ:
print("using cuda")
cuda_async = True
device = torch.device("cuda")
use_cuda = True
dtype = lambda *args, **kargs: torch.cuda.DoubleTensor(*args, **kargs).cuda(non_blocking=cuda_async)
ltype = lambda *args, **kargs: torch.cuda.LongTensor(*args, **kargs).cuda(non_blocking=cuda_async)
btype = lambda *args, **kargs: torch.cuda.ByteTensor(*args, **kargs, device=device).cuda(non_blocking=cuda_async)
ones = lambda *args, **cargs: torch.ones(*args, **cargs, device=device, dtype=torch.double).cuda(non_blocking=cuda_async)
lones = lambda *args, **cargs: torch.ones(*args, **cargs, device=device).cuda(non_blocking=cuda_async)
ones_like = lambda *args, **cargs: torch.ones_like(*args, **cargs, device=device, dtype=torch.double).cuda(non_blocking=cuda_async)
zeros = lambda *args, **cargs: torch.zeros(*args, **cargs, device=device, dtype=torch.double).cuda(non_blocking=cuda_async)
lzeros = lambda *args, **cargs: torch.zeros(*args, **cargs, device=device).cuda(non_blocking=cuda_async)
eye = lambda *args, **cargs: torch.eye(*args, **cargs, device=device, dtype=torch.double).cuda(non_blocking=cuda_async)
rand = lambda *args, **cargs: torch.rand(*args, **cargs, device = device, dtype=torch.double).cuda(non_blocking=cuda_async)
randn = lambda *args, **cargs: torch.randn(*args, **cargs, device = device, dtype=torch.double).cuda(non_blocking=cuda_async)
linspace = lambda *args, **cargs: torch.linspace(*args, **cargs, dtype=torch.double).cuda(non_blocking=cuda_async)
print("set up cuda")
else:
print("not using cuda")
device = torch.device("cpu")
dtype = lambda *args, **kargs: torch.DoubleTensor(*args, **kargs)
ltype = lambda *args, **kargs: torch.LongTensor(*args, **kargs)
btype = lambda *args, **kargs: torch.ByteTensor(*args, **kargs)
linspace = lambda *args, **cargs: torch.linspace(*args, **cargs, dtype=torch.double)
rand = lambda *args, **cargs: torch.rand(*args, **cargs, device = device, dtype=torch.double)
randn = lambda *args, **cargs: torch.randn(*args, **cargs, device = device, dtype=torch.double)
ones = lambda *args, **cargs: torch.ones(*args, **cargs, device = device, dtype=torch.double)
lones = lambda *args, **cargs: torch.ones(*args, **cargs, device = device)
ones_like = lambda *args, **cargs: torch.ones_like(*args, **cargs, device = device, dtype=torch.double)
zeros = lambda *args, **cargs: torch.zeros(*args, **cargs, device = device, dtype=torch.double)
lzeros = lambda *args, **cargs: torch.zeros(*args, **cargs, device = device)
eye = lambda *args, **cargs: torch.eye(*args, **cargs, device = device, dtype=torch.double)
use_cuda = False
ub_zeros = lambda *args,**kargs: lzeros(*args,**kargs, dtype=torch.uint8)
b_zeros = lambda *args,**kargs: lzeros(*args,**kargs, dtype=torch.int8)
l_zeros = lambda *args,**kargs: lzeros(*args,**kargs, dtype=torch.int32)
def cudify(x):
if use_cuda:
return x.cuda(non_blocking=True)
return x
def place(cond, x):
r = cond.new_zeros(size = cond.shape)
r[cond] = x
return r
def extract(cond, x):
if isinstance(x, numbers.Number):
return x
else:
return x[cond]
def product(it):
if isinstance(it,int):
return it
product = 1
for x in it:
if x >= 0:
product *= x
return product
def max_shape(l,r):
return [ max(x,y) for x,y in zip(l,r) ]
class vec3(object):
def __init__(self, x, y, z):
(self.x, self.y, self.z) = (x, y, z)
def __mul__(self, other):
if isinstance(other, vec3):
return vec3(self.x * other.x, self.y * other.y, self.z * other.z)
else:
return vec3(self.x * other, self.y * other, self.z * other)
def __truediv__(self, other):
if isinstance(other, vec3):
return vec3(self.x / other.x, self.y / other.y, self.z / other.z)
else:
return vec3(self.x / other, self.y / other, self.z / other)
def __add__(self, other):
if not isinstance(other, vec3):
return vec3(self.x + other, self.y + other, self.z + other)
return vec3(self.x + other.x, self.y + other.y, self.z + other.z)
def __sub__(self, other):
return vec3(self.x - other.x, self.y - other.y, self.z - other.z)
def dot(self, other):
return (self.x * other.x) + (self.y * other.y) + (self.z * other.z)
def __abs__(self):
return self.dot(self)
def rgbNorm(self):
return self * (1 / (self.x + self.y + self.z))
def cross(self, other):
ax,ay,az = self.x, self.y, self.z
bx,by,bz = other.x, other.y, other.z
return vec3(ay * bz - az * by, az * bx - ax * bz, ax * by - ay * bx)
def norm(self):
l = abs(self)
mag = torch.sqrt(l) if isinstance(l, torch.Tensor) else math.sqrt(l)
if isinstance(mag, torch.Tensor):
mag = torch.where(mag == 0, dtype(1), mag.double())
else:
mag = 1 if mag == 0 else mag
return self * (1.0 / mag)
def luminance(self):
return (self.x + self.y + self.z) / 3
def components(self):
return (self.x, self.y, self.z)
def extract(self, cond):
return vec3(extract(cond, self.x),
extract(cond, self.y),
extract(cond, self.z))
def place(self, cond):
r = vec3(zeros(cond.shape), zeros(cond.shape), zeros(cond.shape))
r.x[cond] = self.x
r.y[cond] = self.y
r.z[cond] = self.z
return r
def div_or(self, b, alt):
return vec3(one_or_div(self.x, b, alt.x), one_or_div(self.y, b, alt.y), one_or_div(self.z, b, alt.z))
rgb = vec3
def one_or_div(a,b, o = None):
if isinstance(b, numbers.Number):
return a / b if b > 0 else 1
gtz = b > 0
if o is None:
o = ones(b.shape)
return torch.where(gtz, a / b , o)
def vec3u(x,s):
return vec3(ones(s),ones(s),ones(s)) * x
def vec3uCPU(x,s):
return vec3(ones(s).cpu(),ones(s).cpu(),ones(s).cpu()) * x