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Saving unsuccessful separable filtering code
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@ -63,4 +63,127 @@ void logfilt(float4 *pixbuf, float k1, float k2,
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}
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"""
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class DensityEst(HunkOCode):
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"""
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NOTE: for now, this *must* be invoked with a block size of (32,16,1), and
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a grid size of (W/32) for vertical filtering or (H/32) for horizontal.
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It will probably fail for images that are not multiples of 32.
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"""
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def __init__(self, features, cp):
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self.features, self.cp = features, cp
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@property
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def defs(self):
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return self.defs_tmpl.substitute(features=self.features, cp=self.cp)
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defs_tmpl = Template("""
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#define W 15 // Filter width (regardless of standard deviation chosen)
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#define W2 7 // Half of filter width, rounded down
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#define NW 16 // Number of warps in each set of points
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#define FW 30 // Width of local result storage per-lane (NW+W2+W2)
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#define BX 32 // The size of a block's X dimension (== 1 warp)
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__global__
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void density_est(float4 *pixbuf, float *denbuf, int vertical) {
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__shared__ float r[BX*FW], g[BX*FW], b[BX*FW], a[BX*FW];
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int ibase; // The index of the first element within this lane's strip
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int imax; // The maximum offset from the first element in the strip
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int istride; // Number of indices until next point to filter
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if (vertical) {
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ibase = threadIdx.x + blockIdx.x * BX;
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imax = {{features.acc_height}};
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istride = {{features.acc_stride}};
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} else {
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ibase = (blockIdx.x * BX + threadIdx.x) * {{features.acc_stride}};
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imax = {{features.acc_width}};
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istride = 1;
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}
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for (int i = threadIdx.x + BX*threadIdx.y; i < BX*FW; i += NW * BX)
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r[i] = g[i] = b[i] = a[i] = 0.0f;
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for (int i = threadIdx.y; i < imax; i += NW) {
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int idx = ibase+i*istride;
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float4 in = pixbuf[idx];
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float den = denbuf[idx];
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int j = (threadIdx.y + W2) * 32 + threadIdx.x;
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float sd = {{0.35 * cp.estimator}} / powf(den+1.0f, {{cp.estimator_curve}});
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{{if cp.estimator_minimum > 1}}
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sd = fmaxf(sd, {{cp.estimator_minimum}});
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{{endif}}
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sd *= sd;
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// TODO: log scaling here? matches flam3, but, ick
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// TODO: investigate harm caused by varying standard deviation in a
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// separable environment
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float coeff = rsqrtf(2.0f*M_PI*sd*sd);
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atomicAdd(r+j, in.x * coeff);
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atomicAdd(g+j, in.y * coeff);
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atomicAdd(b+j, in.z * coeff);
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atomicAdd(a+j, in.w * coeff);
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sd = -0.5/sd;
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// #pragma unroll
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for (int k = 1; k <= W2; k++) {
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float scale = exp(sd*k*k)*coeff;
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idx = j+k*32;
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atomicAdd(r+idx, in.x * scale);
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atomicAdd(g+idx, in.y * scale);
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atomicAdd(b+idx, in.z * scale);
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atomicAdd(a+idx, in.w * scale);
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idx = j-k*32;
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atomicAdd(r+idx, in.x * scale);
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atomicAdd(g+idx, in.y * scale);
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atomicAdd(b+idx, in.z * scale);
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atomicAdd(a+idx, in.w * scale);
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}
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__syncthreads();
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float4 out;
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j = threadIdx.y * BX + threadIdx.x;
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out.x = r[j];
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out.y = g[j];
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out.z = b[j];
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out.w = a[j];
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idx = ibase+(i-W2)*istride;
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if (idx > 0)
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pixbuf[idx] = out;
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__syncthreads();
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// TODO: shift instead of copying
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idx = threadIdx.x + BX * threadIdx.y;
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if (threadIdx.y < NW-2) {
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r[idx] = r[idx + BX*NW];
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g[idx] = g[idx + BX*NW];
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b[idx] = b[idx + BX*NW];
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a[idx] = a[idx + BX*NW];
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}
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__syncthreads();
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r[idx + BX*(NW-2)] = 0.0f;
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g[idx + BX*(NW-2)] = 0.0f;
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b[idx + BX*(NW-2)] = 0.0f;
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a[idx + BX*(NW-2)] = 0.0f;
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__syncthreads();
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}
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}
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""")
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def invoke(self, mod, abufd, dbufd):
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fun = mod.get_function("density_est")
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t = fun(abufd, dbufd, np.int32(0),
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block=(32, 16, 1), grid=(self.features.acc_height/32,1),
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time_kernel=True)
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print "Horizontal density estimation: %g" % t
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t = fun(abufd, dbufd, np.int32(1),
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block=(32, 16, 1), grid=(self.features.acc_width/32,1),
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time_kernel=True)
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print "Vertical density estimation: %g" % t
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@ -160,11 +160,13 @@ def render(features, cps):
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seeds = mwc.MWC.make_seeds(512 * nsteps)
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iter = IterCode(features)
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code = assemble_code(BaseCode, mwc.MWC, iter.packer, iter, filter.ColorClip)
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de = filter.DensityEst(features, cps[0])
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code = assemble_code(BaseCode, mwc.MWC, iter.packer, iter,
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filter.ColorClip, de)
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for lno, line in enumerate(code.split('\n')):
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print '%3d %s' % (lno, line)
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mod = SourceModule(code, keep=True,
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mod = SourceModule(code,
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options=['-use_fast_math', '-maxrregcount', '32'])
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cps_as_array = (Genome * len(cps))()
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@ -222,6 +224,8 @@ def render(features, cps):
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area = features.width * features.height / cp.ppu ** 2
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k2 = 1 / (area * cp.adj_density)
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de.invoke(mod, abufd, dbufd)
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fun = mod.get_function("logfilt")
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t = fun(abufd, f(k1), f(k2),
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f(1 / cp.gamma), f(cp.vibrancy), f(cp.highlight_power),
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@ -229,10 +233,10 @@ def render(features, cps):
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print "Completed color filtering in %g seconds" % t
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abuf = cuda.from_device_like(abufd, abuf)
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dbuf = cuda.from_device_like(dbufd, dbuf)
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return abuf, dbuf
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# TODO: find a better place to stick this code
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class MemBench(HunkOCode):
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decls = """
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@ -112,6 +112,9 @@ class Features(object):
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self.width = genomes[0].width
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self.height = genomes[0].height
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self.acc_width = genomes[0].width
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self.acc_height = genomes[0].height
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self.acc_stride = genomes[0].width
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class XFormFeatures(object):
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def __init__(self, xforms, xform_id):
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