def gaussianKernels(gs):
global program
convs = [("gauss%s" % len(a),a) for a in gs]
convsres = [("gauss%s_res" % len(a),a) for a in gs]
program = kernels.loadProgram(interfaces.convolvesep,convs=convs)
krnls = [getattr(program, name) for (name, conv) in convs]
for i, (name, conv) in enumerate(convs):
krnls[i].res = getattr(program,convsres[i][0])
krnls[i].width = len(conv)
return krnls
def test_gradient():
import time
a = np.random.sample((1000,1000)).astype(np.float32)
t = time.time()
b = np.gradient(a)
print "Numpy seconds", time.time()-t
for engine in (kernels.GPU_ENGINE, kernels.CPU_ENGINE):
program = kernels.loadProgram(interfaces.gradient, engine=engine)
t = time.time()
c = program.gradient(a, 1)
print "Engine %s seconds %s" % (engine,time.time()-t)
import numpy as np
from rpc import interfaces, kernels
program = kernels.loadProgram(interfaces.operators,operators=[("add","+"), ("sub","-"),("mul","*"),("div","/")])
add = program.add
add_res = program.add_res
sub = program.sub
sub_res = program.sub_res
mul = program.mul
mul_res = program.mul_res
div = program.div
div_res = program.div_res
def test_operators():
# 1+0 -> 1
a = np.ones((100,100), dtype=np.float32)
b = np.zeros_like(a)
c = add(a,b)
d = np.empty_like(a)
add_res(a,b,d)
assert c.sum() == c.size*1.0
program.read(d)
assert a.sum() == c.sum()
assert a.sum() == d.sum()
# 1-1 == 0
b[:,:] = 1.0
c = sub(a,b)
d[:,:]=10
sub_res(a,b,d)
program.read(d)
return np.array(im2, np.float32).reshape(im.shape)
from rpc import kernels, interfaces
def splitChannels(rgb):
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return r.copy(), g.copy(), b.copy()
def joinChannels(r, g, b):
shape = list(r.shape)
shape.append(3)
rgb = np.empty(tuple(shape), dtype=r.dtype)
rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2] = r.copy(), g.copy(), b.copy()
return rgb
program = kernels.loadProgram(interfaces.hsi)
def rgb2hsi(r, g, b):
h, s, i, trace = program.rgb2hsi(r, g, b) #@UnusedVariable
return h, s, i
def hsi2rgb(h, s, i):
r,g,b = program.hsi2rgb(h,s,i)
return r,g,b
if __name__ == "__main__":
from utils import showArray
r = np.empty((256, 256), dtype=np.float32)
g = np.empty_like(r)
b = np.empty_like(r)
for i in range(256):
r[i, :] = i
import numpy as np
from rpc import kernels, interfaces
from utils import showArray
xy = kernels.loadProgram(interfaces.xy)
a = np.zeros((402,798),dtype=np.int32)
addr,x,y,label = xy.addr(a)
showArray("addr",addr)
showArray("x",x)
showArray("y", y)
showArray("label", label)
'''
Created on Jul 22, 2011
@author: seant
'''
import numpy as np
from rpc import kernels, interfaces
program = kernels.loadProgram(interfaces.gradient, engine=kernels.GPU_ENGINE)
gradientcl = program.gradient
gradient_res = program.gradient_res
def gradient(image, reach=1):
grad,theta = gradientcl(image, reach)
# TODO: gradientCL should not be returning nans, and is
theta[np.where(np.isnan(theta))] = 0.0
grad[np.where(np.isnan(grad))] = 0.0
return grad, theta
def test_gradient():
import time
a = np.random.sample((1000,1000)).astype(np.float32)
t = time.time()
b = np.gradient(a)
print "Numpy seconds", time.time()-t
for engine in (kernels.GPU_ENGINE, kernels.CPU_ENGINE):
program = kernels.loadProgram(interfaces.gradient, engine=engine)
t = time.time()
c = program.gradient(a, 1)
'''
Provides 2d median filter for 2d arrays
Created on Jul 22, 2011
'''
import numpy as np
from rpc import kernels, interfaces
# Provision this program with just one median filter, fixed length of 9
program = kernels.loadProgram(interfaces.median3x3, width=9, steps=[9])
median3x3cl = program.median3x3
def median3x3slow(image, iterations=1):
while iterations > 0:
image = median3x3cl(image)
iterations -= 1
return image.copy()
def median3x3fast(image, iterations=1):
if iterations == 1:
# One pass through
return median3x3cl(image)
input = image
output = np.zeros_like(input)
if iterations == 2:
# Send in data, don't retrieve
program.first(input, output)
input,output = output,input
# Don't send in, retrieve data
program.last(input, output)
return output
# Send in data, no retrieve
from rpc import kernels, interfaces import numpy import numpy.linalg as la import time # We don't _need_ specify the dtype: that's done in the interface a = numpy.random.rand(50000) b = numpy.random.rand(50000) t = time.time() # We don't need to manage a compiler and linker, that can be done for us demo = kernels.loadProgram(interfaces.demo) # We don't need to manage buffers, that can be done for us a_plus_b = demo.sum(a,b) print(la.norm(a_plus_b - (a+b)), la.norm(a_plus_b)) print "Elapsed:", time.time() - t # And there are interesting stats available print "Stats", demo
# with minor changes to move to numpy from the obsolete Numeric
import numpy as np
import time
# You can choose a calculation routine below (calc_fractal), uncomment
# one of the three lines to test the three variations
# Speed notes are listed in the same place
# set width and height of window, more pixels take longer to calculate
w = 1024
h = 1024
# Use the rpc extension to define and load the kernel as a callable.
from rpc import kernels, interfaces
calc_fractal_opencl = kernels.loadProgram(interfaces.mandelbrot,engine=kernels.CPU_ENGINE).mandelbrot
def calc_fractal_serial(q, maxiter):
# calculate z using numpy
# this routine unrolls calc_fractal_numpy as an intermediate
# step to the creation of calc_fractal_opencl
# it runs slower than calc_fractal_numpy
z = np.zeros(q.shape, np.complex64)
output = np.resize(np.array(0,), q.shape)
for i in range(len(q)):
for iter in range(maxiter):
z[i] = z[i]*z[i] + q[i]
if abs(z[i]) > 2.0:
q[i] = 0+0j
