mirror-github-bspeice/cuburn
1
0
Fork 0
mirror of https://github.com/stevenrobertson/cuburn.git synced 2025-02-05 11:40:04 -05:00
Code Issues Actions Packages Projects Releases Wiki Activity

Use much more accurate filtsum estimation polynomials

Browse Source
This commit is contained in:
Steven Robertson 2011-06-12 17:37:57 -04:00
parent e9998c28da
commit f872baf844
2 changed files with 150 additions and 64 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

112
cuburn/code/filtering.py
View File

@ -124,6 +124,9 @@ void logscale(float4 *pixbuf, float4 *outbuf, float k1, float k2) {
outbuf[i] = pix;
}
#define MIN_SD 0.23299530
#define MAX_SD 4.33333333
__global__
void density_est(float4 *pixbuf, float4 *outbuf, float *denbuf,
float est_sd, float neg_est_curve, float est_min,
@ -147,61 +150,86 @@ void density_est(float4 *pixbuf, float4 *outbuf, float *denbuf,
in.z *= ls;
in.w *= ls;
// Base index of destination for writes
int si = (threadIdx.y + W2) * FW + threadIdx.x + W2;
// Calculate standard deviation of Gaussian kernel. The base SD is
// then scaled in inverse proportion to the density of the point
// being scaled.
float sd = est_sd * powf(den+1.0f, neg_est_curve);
// Clamp the final standard deviation. Things will go badly if the
// minimum is undershot.
sd = fmaxf(sd, est_min);
sd = fminf(MAX_SD, fmaxf(sd, est_min));
// This five-term polynomial approximates the sum of the filters
// with the clamping logic used here. See helpers/filt_err.py.
float filtsum;
filtsum = -0.20885075f * sd + 0.90557721f;
filtsum = filtsum * sd + 5.28363054f;
filtsum = filtsum * sd + -0.11733533f;
filtsum = filtsum * sd + 0.35670333f;
float filtscale = 1 / filtsum;
// Below a certain threshold, only one coeffecient would be
// retained anyway; we hop right to it.
if (sd <= MIN_SD) {
de_add(si, 0, 0, in);
} else {
// These polynomials approximates the sum of the filters
// with the clamping logic used here. See helpers/filt_err.py.
float filtsum;
if (sd < 0.75) {
filtsum = -352.25061035f;
filtsum = filtsum * sd + 1117.09680176f;
filtsum = filtsum * sd + -1372.48864746f;
filtsum = filtsum * sd + 779.15478516f;
filtsum = filtsum * sd + -164.04229736f;
filtsum = filtsum * sd + -12.04892635f;
filtsum = filtsum * sd + 9.04126644f;
filtsum = filtsum * sd + 0.10304667f;
} else {
filtsum = -0.00403376f;
filtsum = filtsum * sd + 0.06608720f;
filtsum = filtsum * sd + -0.38924992f;
filtsum = filtsum * sd + 0.84797901f;
filtsum = filtsum * sd + 0.34173131f;
filtsum = filtsum * sd + -4.67077589f;
filtsum = filtsum * sd + 14.34595776f;
filtsum = filtsum * sd + -5.80082798f;
filtsum = filtsum * sd + 1.54098487f;
}
float filtscale = 1.0f / filtsum;
// The reciprocal SD scaling coeffecient in the Gaussian exponent.
// exp(-x^2/(2*sd^2)) = exp2f(x^2*rsd)
float rsd = -0.5f * CUDART_L2E_F / (sd * sd);
// The reciprocal SD scaling coeffecient in the Gaussian
// exponent: exp(-x^2/(2*sd^2)) = exp2f(x^2*rsd)
float rsd = -0.5f * CUDART_L2E_F / (sd * sd);
int si = (threadIdx.y + W2) * FW + threadIdx.x + W2;
for (int jj = 0; jj <= W2; jj++) {
float jj2f = jj;
jj2f *= jj2f;
for (int jj = 0; jj <= W2; jj++) {
float jj2f = jj;
jj2f *= jj2f;
float iif = 0;
for (int ii = 0; ii <= jj; ii++) {
iif += 1;
float iif = 0;
for (int ii = 0; ii <= jj; ii++) {
float coeff = exp2f((jj2f + iif * iif) * rsd) * filtscale;
if (coeff < 0.0001f) break;
float coeff = exp2f((jj2f + iif * iif) * rsd)
* filtscale;
if (coeff < 0.0001f) break;
float4 scaled;
scaled.x = in.x * coeff;
scaled.y = in.y * coeff;
scaled.z = in.z * coeff;
scaled.w = in.w * coeff;
float4 scaled;
scaled.x = in.x * coeff;
scaled.y = in.y * coeff;
scaled.z = in.z * coeff;
scaled.w = in.w * coeff;
de_add(si, ii, jj, scaled);
if (jj == 0) continue;
de_add(si, ii, -jj, scaled);
if (ii != 0) {
de_add(si, -ii, jj, scaled);
de_add(si, -ii, -jj, scaled);
if (ii == jj) continue;
de_add(si, ii, jj, scaled);
if (jj == 0) continue;
de_add(si, ii, -jj, scaled);
if (ii != 0) {
de_add(si, -ii, jj, scaled);
de_add(si, -ii, -jj, scaled);
if (ii == jj) continue;
}
de_add(si, jj, ii, scaled);
de_add(si, -jj, ii, scaled);
if (ii == 0) continue;
de_add(si, -jj, -ii, scaled);
de_add(si, jj, -ii, scaled);
iif += 1;
// TODO: validate that the above avoids bank conflicts
}
de_add(si, jj, ii, scaled);
de_add(si, -jj, ii, scaled);
if (ii == 0) continue;
de_add(si, -jj, -ii, scaled);
de_add(si, jj, -ii, scaled);
// TODO: validate that the above avoids bank conflicts
}
}
}
@ -262,7 +290,7 @@ void density_est(float4 *pixbuf, float4 *outbuf, float *denbuf,
# (0.5/1.5)=1/3.
est_sd = np.float32(cp.estimator / 3.)
neg_est_curve = np.float32(-cp.estimator_curve)
est_min = np.float32(max(cp.estimator_minimum / 3., 0.4))
est_min = np.float32(cp.estimator_minimum / 3.)
fun = mod.get_function("density_est")
fun(abufd, obufd, dbufd, est_sd, neg_est_curve, est_min, k1, k2,
block=(32, 32, 1), grid=(self.features.acc_width/32, 1),

102
helpers/filt_err.py
View File

@ -9,38 +9,96 @@ F2 = int(FWIDTH/2)
# The maximum size of any one coeffecient to be retained
COEFF_EPS = 0.0001
dists = np.fromfunction(lambda i, j: np.hypot(i-F2, j-F2), (FWIDTH, FWIDTH))
dists = dists.flatten()
dists2d = np.fromfunction(lambda i, j: np.hypot(i-F2, j-F2), (FWIDTH, FWIDTH))
dists = dists2d.flatten()
# A flam3 estimator radius corresponds to a Gaussian filter with a standard
# deviation of 1/3 the radius. We choose 13 as an arbitrary upper bound for the
# max filter radius. Larger radii will work without
# max filter radius. The filter should reject larger radii.
MAX_SD = 13 / 3.
# The minimum estimator radius is 1. In flam3, this is effectively no
# filtering, but since the cutoff structure is defined by COEFF_EPS in cuburn,
# we undershoot it a bit to make the polyfit behave better at high densities.
MIN_SD = 0.3
# The minimum estimator radius can be set as low as 0, but below a certain
# radius only one coeffecient is retained. Since things get unstable near 0,
# we explicitly set a minimum threshold below which no coeffecients are
# retained.
MIN_SD = np.sqrt(-1 / (2 * np.log(COEFF_EPS)))
sds = np.logspace(np.log10(MIN_SD), np.log10(MAX_SD), num=100)
# Using two predicated three-term approximations is much more accurate than
# using a very large number of terms, due to nonlinear behavior at low SD.
# Everything above this SD uses one approximation; below, another.
SPLIT_SD = 0.75
# Calculate the filter sums at each coordinate
sums = []
for sd in sds:
coeffs = np.exp(dists**2 / (-2 * sd ** 2))
sums.append(np.sum(filter(lambda v: v / np.sum(coeffs) > COEFF_EPS, coeffs)))
print sums
# The lower endpoints are undershot by this proportion to reduce error
UNDERSHOOT = 0.98
sds_hi = np.linspace(SPLIT_SD * UNDERSHOOT, MAX_SD, num=1000)
sds_lo = np.linspace(MIN_SD * UNDERSHOOT, SPLIT_SD, num=1000)
print 'At MIN_SD = %g, these are the coeffs:' % MIN_SD
print np.exp(dists2d**2 / (-2 * MIN_SD ** 2))
def eval_sds(sds, name, nterms):
# Calculate the filter sums at each coordinate
sums = []
for sd in sds:
coeffs = np.exp(dists**2 / (-2 * sd ** 2))
# Note that this sum is the sum of all coordinates, though it should
# actually be the result of the polynomial approximation. We could do
# a feedback loop to improve accuracy, but I don't think the difference
# is worth worrying about.
sum = np.sum(coeffs)
sums.append(np.sum(filter(lambda v: v / sum > COEFF_EPS, coeffs)))
print 'Evaluating %s:' % name
poly, resid, rank, sing, rcond = np.polyfit(sds, sums, nterms, full=True)
print 'Fit for %s:' % name, poly, resid, rank, sing, rcond
return sums, poly
import matplotlib.pyplot as plt
poly, resid, rank, sing, rcond = np.polyfit(sds, sums, 4, full=True)
print poly, resid, rank, sing, rcond
sums_hi, poly_hi = eval_sds(sds_hi, 'hi', 8)
sums_lo, poly_lo = eval_sds(sds_lo, 'lo', 7)
num_undershoots = len(filter(lambda v: v < SPLIT_SD, sds_hi))
sds_hi = sds_hi[num_undershoots:]
sums_hi = sums_hi[num_undershoots:]
num_undershoots = len(filter(lambda v: v < MIN_SD, sds_lo))
sds_lo = sds_lo[num_undershoots:]
sums_lo = sums_lo[num_undershoots:]
polyf_hi = np.float32(poly_hi)
vals_hi = np.polyval(polyf_hi, sds_hi)
polyf_lo = np.float32(poly_lo)
vals_lo = np.polyval(polyf_lo, sds_lo)
def print_filt(filts):
print ' filtsum = %4.8ff;' % filts[0]
for f in filts[1:]:
print ' filtsum = filtsum * sd + % 16.8ff;' % f
print '\n\nFor your convenience:'
print '#define MIN_SD %.8f' % MIN_SD
print '#define MAX_SD %.8f' % MAX_SD
print 'if (sd < %g) {' % SPLIT_SD
print_filt(polyf_lo)
print '} else {'
print_filt(polyf_hi)
print '}'
sds = np.concatenate([sds_lo, sds_hi])
sums = np.concatenate([sums_lo, sums_hi])
vals = np.concatenate([vals_lo, vals_hi])
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(sds, sums)
ax.plot(sds, vals)
ax.set_xlabel('stdev')
ax.set_ylabel('filter sum')
ax = ax.twinx()
ax.plot(sds, [abs((s-v)/v) for s, v in zip(sums, vals)])
ax.set_ylabel('rel err')
polyf = np.float32(poly)
plt.plot(sds, sums)
plt.plot(sds, np.polyval(polyf, sds))
plt.show()
print np.polyval(poly, 1.1)
# TODO: calculate error more fully, verify all this logic