Add DXT1n and CTX1 CUDA compressors.
castano
16 years ago
parent
c7fcc3ef4b
commit
5dbfb20b60
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// Copyright NVIDIA Corporation 2007 -- Ignacio Castano <icastano@nvidia.com>
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// Math functions and operators to be used with vector types.
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#ifndef CUDAMATH_H
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#define CUDAMATH_H
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#include <float.h>
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inline __device__ __host__ float3 operator *(float3 a, float3 b)
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{
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return make_float3(a.x*b.x, a.y*b.y, a.z*b.z);
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}
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inline __device__ __host__ float3 operator *(float f, float3 v)
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{
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return make_float3(v.x*f, v.y*f, v.z*f);
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}
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inline __device__ __host__ float3 operator *(float3 v, float f)
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{
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return make_float3(v.x*f, v.y*f, v.z*f);
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}
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inline __device__ __host__ float3 operator +(float3 a, float3 b)
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{
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return make_float3(a.x+b.x, a.y+b.y, a.z+b.z);
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}
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inline __device__ __host__ void operator +=(float3 & b, float3 a)
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{
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b.x += a.x;
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b.y += a.y;
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b.z += a.z;
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}
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inline __device__ __host__ float3 operator -(float3 a, float3 b)
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{
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return make_float3(a.x-b.x, a.y-b.y, a.z-b.z);
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}
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inline __device__ __host__ void operator -=(float3 & b, float3 a)
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{
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b.x -= a.x;
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b.y -= a.y;
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b.z -= a.z;
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}
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inline __device__ __host__ float3 operator /(float3 v, float f)
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{
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float inv = 1.0f / f;
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return v * inv;
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}
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inline __device__ __host__ void operator /=(float3 & b, float f)
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{
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float inv = 1.0f / f;
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b.x *= inv;
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b.y *= inv;
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b.z *= inv;
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}
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inline __device__ __host__ float dot(float3 a, float3 b)
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{
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return a.x * b.x + a.y * b.y + a.z * b.z;
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}
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inline __device__ __host__ float dot(float4 a, float4 b)
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{
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return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
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}
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inline __device__ __host__ float clamp(float f, float a, float b)
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{
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return max(a, min(f, b));
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}
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inline __device__ __host__ float3 clamp(float3 v, float a, float b)
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{
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return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
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}
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inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b)
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{
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return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
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}
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inline __device__ __host__ float3 normalize(float3 v)
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{
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float len = 1.0f / sqrtf(dot(v, v));
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return make_float3(v.x * len, v.y * len, v.z * len);
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}
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// Use power method to find the first eigenvector.
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// http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html
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inline __device__ __host__ float3 firstEigenVector( float matrix[6] )
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{
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// 8 iterations seems to be more than enough.
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float3 v = make_float3(1.0f, 1.0f, 1.0f);
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for(int i = 0; i < 8; i++) {
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float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
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float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
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float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
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float m = max(max(x, y), z);
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float iv = 1.0f / m;
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#if __DEVICE_EMULATION__
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if (m == 0.0f) iv = 0.0f;
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#endif
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v = make_float3(x*iv, y*iv, z*iv);
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}
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return v;
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}
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inline __device__ void colorSums(const float3 * colors, float3 * sums)
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{
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#if __DEVICE_EMULATION__
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float3 color_sum = make_float3(0.0f, 0.0f, 0.0f);
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for (int i = 0; i < 16; i++)
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{
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color_sum += colors[i];
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}
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for (int i = 0; i < 16; i++)
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{
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sums[i] = color_sum;
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}
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#else
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const int idx = threadIdx.x;
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// Copyright NVIDIA Corporation 2007 -- Ignacio Castano <icastano@nvidia.com>
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//
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// Permission is hereby granted, free of charge, to any person
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// obtaining a copy of this software and associated documentation
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// files (the "Software"), to deal in the Software without
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// restriction, including without limitation the rights to use,
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// copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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// copies of the Software, and to permit persons to whom the
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// Software is furnished to do so, subject to the following
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// conditions:
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//
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// The above copyright notice and this permission notice shall be
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// included in all copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
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// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
|
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// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
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// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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// OTHER DEALINGS IN THE SOFTWARE.
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// Math functions and operators to be used with vector types.
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#ifndef CUDAMATH_H
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#define CUDAMATH_H
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#include <float.h>
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inline __device__ __host__ float3 operator *(float3 a, float3 b)
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{
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return make_float3(a.x*b.x, a.y*b.y, a.z*b.z);
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}
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inline __device__ __host__ float3 operator *(float f, float3 v)
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{
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return make_float3(v.x*f, v.y*f, v.z*f);
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}
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inline __device__ __host__ float3 operator *(float3 v, float f)
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{
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return make_float3(v.x*f, v.y*f, v.z*f);
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}
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inline __device__ __host__ float3 operator +(float3 a, float3 b)
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{
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return make_float3(a.x+b.x, a.y+b.y, a.z+b.z);
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}
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inline __device__ __host__ void operator +=(float3 & b, float3 a)
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{
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b.x += a.x;
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b.y += a.y;
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b.z += a.z;
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}
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inline __device__ __host__ float3 operator -(float3 a, float3 b)
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{
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return make_float3(a.x-b.x, a.y-b.y, a.z-b.z);
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}
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inline __device__ __host__ void operator -=(float3 & b, float3 a)
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{
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b.x -= a.x;
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b.y -= a.y;
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b.z -= a.z;
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}
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inline __device__ __host__ float3 operator /(float3 v, float f)
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{
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float inv = 1.0f / f;
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return v * inv;
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}
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inline __device__ __host__ void operator /=(float3 & b, float f)
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{
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float inv = 1.0f / f;
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b.x *= inv;
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b.y *= inv;
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b.z *= inv;
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}
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// float2 operators
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inline __device__ __host__ float2 operator *(float2 a, float2 b)
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{
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return make_float2(a.x*b.x, a.y*b.y);
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}
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inline __device__ __host__ float2 operator *(float f, float2 v)
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{
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return make_float2(v.x*f, v.y*f);
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}
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inline __device__ __host__ float2 operator *(float2 v, float f)
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{
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return make_float2(v.x*f, v.y*f);
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}
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inline __device__ __host__ float2 operator +(float2 a, float2 b)
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{
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return make_float2(a.x+b.x, a.y+b.y);
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}
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inline __device__ __host__ void operator +=(float2 & b, float2 a)
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{
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b.x += a.x;
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b.y += a.y;
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}
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inline __device__ __host__ float2 operator -(float2 a, float2 b)
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{
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return make_float2(a.x-b.x, a.y-b.y);
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}
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inline __device__ __host__ void operator -=(float2 & b, float2 a)
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{
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b.x -= a.x;
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b.y -= a.y;
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}
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inline __device__ __host__ float2 operator /(float2 v, float f)
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{
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float inv = 1.0f / f;
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return v * inv;
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}
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inline __device__ __host__ void operator /=(float2 & b, float f)
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{
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float inv = 1.0f / f;
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b.x *= inv;
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b.y *= inv;
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}
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inline __device__ __host__ float dot(float2 a, float2 b)
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{
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return a.x * b.x + a.y * b.y;
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}
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inline __device__ __host__ float dot(float3 a, float3 b)
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{
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return a.x * b.x + a.y * b.y + a.z * b.z;
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}
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inline __device__ __host__ float dot(float4 a, float4 b)
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{
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return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
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}
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inline __device__ __host__ float clamp(float f, float a, float b)
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{
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return max(a, min(f, b));
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}
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inline __device__ __host__ float3 clamp(float3 v, float a, float b)
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{
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return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
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}
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inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b)
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{
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return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
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}
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inline __device__ __host__ float3 normalize(float3 v)
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{
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float len = 1.0f / sqrtf(dot(v, v));
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return make_float3(v.x * len, v.y * len, v.z * len);
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}
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// Use power method to find the first eigenvector.
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// http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html
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inline __device__ __host__ float3 firstEigenVector( float matrix[6] )
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{
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// 8 iterations seems to be more than enough.
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float3 v = make_float3(1.0f, 1.0f, 1.0f);
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for(int i = 0; i < 8; i++) {
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float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
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float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
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float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
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float m = max(max(x, y), z);
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float iv = 1.0f / m;
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#if __DEVICE_EMULATION__
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if (m == 0.0f) iv = 0.0f;
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#endif
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v = make_float3(x*iv, y*iv, z*iv);
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}
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return v;
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}
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inline __device__ void colorSums(const float3 * colors, float3 * sums)
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{
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#if __DEVICE_EMULATION__
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float3 color_sum = make_float3(0.0f, 0.0f, 0.0f);
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for (int i = 0; i < 16; i++)
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{
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color_sum += colors[i];
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}
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for (int i = 0; i < 16; i++)
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{
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sums[i] = color_sum;
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}
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#else
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const int idx = threadIdx.x;
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sums[idx] = colors[idx];
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sums[idx] += sums[idx^8];
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sums[idx] += sums[idx^4];
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sums[idx] += sums[idx^2];
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sums[idx] += sums[idx^1];
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#endif
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}
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inline __device__ float3 bestFitLine(const float3 * colors, float3 color_sum, float3 colorMetric)
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{
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// Compute covariance matrix of the given colors.
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#if __DEVICE_EMULATION__
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float covariance[6] = {0, 0, 0, 0, 0, 0};
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for (int i = 0; i < 16; i++)
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{
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float3 a = (colors[i] - color_sum * (1.0f / 16.0f)) * colorMetric;
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covariance[0] += a.x * a.x;
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covariance[1] += a.x * a.y;
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covariance[2] += a.x * a.z;
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covariance[3] += a.y * a.y;
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covariance[4] += a.y * a.z;
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covariance[5] += a.z * a.z;
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}
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#else
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const int idx = threadIdx.x;
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float3 diff = (colors[idx] - color_sum * (1.0f / 16.0f)) * colorMetric;
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// @@ Eliminate two-way bank conflicts here.
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// @@ It seems that doing that and unrolling the reduction doesn't help...
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__shared__ float covariance[16*6];
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covariance[6 * idx + 0] = diff.x * diff.x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
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covariance[6 * idx + 1] = diff.x * diff.y;
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covariance[6 * idx + 2] = diff.x * diff.z;
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covariance[6 * idx + 3] = diff.y * diff.y;
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covariance[6 * idx + 4] = diff.y * diff.z;
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covariance[6 * idx + 5] = diff.z * diff.z;
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for(int d = 8; d > 0; d >>= 1)
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{
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if (idx < d)
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{
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covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
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covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
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covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
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covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
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covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
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covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
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}
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}
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#endif
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// Compute first eigen vector.
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return firstEigenVector(covariance);
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}
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#endif // CUDAMATH_H
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#endif
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}
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inline __device__ float3 bestFitLine(const float3 * colors, float3 color_sum, float3 colorMetric)
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{
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// Compute covariance matrix of the given colors.
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#if __DEVICE_EMULATION__
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float covariance[6] = {0, 0, 0, 0, 0, 0};
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for (int i = 0; i < 16; i++)
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{
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float3 a = (colors[i] - color_sum * (1.0f / 16.0f)) * colorMetric;
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covariance[0] += a.x * a.x;
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covariance[1] += a.x * a.y;
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covariance[2] += a.x * a.z;
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covariance[3] += a.y * a.y;
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covariance[4] += a.y * a.z;
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covariance[5] += a.z * a.z;
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}
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#else
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const int idx = threadIdx.x;
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float3 diff = (colors[idx] - color_sum * (1.0f / 16.0f)) * colorMetric;
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// @@ Eliminate two-way bank conflicts here.
|
||||
// @@ It seems that doing that and unrolling the reduction doesn't help...
|
||||
__shared__ float covariance[16*6];
|
||||
|
||||
covariance[6 * idx + 0] = diff.x * diff.x; // 0, 6, 12, 2, 8, 14, 4, 10, 0
|
||||
covariance[6 * idx + 1] = diff.x * diff.y;
|
||||
covariance[6 * idx + 2] = diff.x * diff.z;
|
||||
covariance[6 * idx + 3] = diff.y * diff.y;
|
||||
covariance[6 * idx + 4] = diff.y * diff.z;
|
||||
covariance[6 * idx + 5] = diff.z * diff.z;
|
||||
|
||||
for(int d = 8; d > 0; d >>= 1)
|
||||
{
|
||||
if (idx < d)
|
||||
{
|
||||
covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
|
||||
covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
|
||||
covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
|
||||
covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
|
||||
covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
|
||||
covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
// Compute first eigen vector.
|
||||
return firstEigenVector(covariance);
|
||||
}
|
||||
|
||||
// @@ For 2D this may not be the most efficient method. It's a quadratic equation, right?
|
||||
inline __device__ __host__ float2 firstEigenVector2D( float matrix[3] )
|
||||
{
|
||||
// @@ 8 iterations is probably more than enough.
|
||||
|
||||
float2 v = make_float2(1.0f, 1.0f);
|
||||
for(int i = 0; i < 8; i++) {
|
||||
float x = v.x * matrix[0] + v.y * matrix[1];
|
||||
float y = v.x * matrix[1] + v.y * matrix[2];
|
||||
float m = max(x, y);
|
||||
float iv = 1.0f / m;
|
||||
#if __DEVICE_EMULATION__
|
||||
if (m == 0.0f) iv = 0.0f;
|
||||
#endif
|
||||
v = make_float2(x*iv, y*iv);
|
||||
}
|
||||
|
||||
return v;
|
||||
}
|
||||
|
||||
inline __device__ void colorSums(const float2 * colors, float2 * sums)
|
||||
{
|
||||
#if __DEVICE_EMULATION__
|
||||
float2 color_sum = make_float2(0.0f, 0.0f, 0.0f);
|
||||
for (int i = 0; i < 16; i++)
|
||||
{
|
||||
color_sum += colors[i];
|
||||
}
|
||||
|
||||
for (int i = 0; i < 16; i++)
|
||||
{
|
||||
sums[i] = color_sum;
|
||||
}
|
||||
#else
|
||||
|
||||
const int idx = threadIdx.x;
|
||||
|
||||
sums[idx] = colors[idx];
|
||||
sums[idx] += sums[idx^8];
|
||||
sums[idx] += sums[idx^4];
|
||||
sums[idx] += sums[idx^2];
|
||||
sums[idx] += sums[idx^1];
|
||||
|
||||
#endif
|
||||
}
|
||||
|
||||
inline __device__ float2 bestFitLine(const float2 * colors, float2 color_sum)
|
||||
{
|
||||
// Compute covariance matrix of the given colors.
|
||||
#if __DEVICE_EMULATION__
|
||||
float covariance[3] = {0, 0, 0};
|
||||
for (int i = 0; i < 16; i++)
|
||||
{
|
||||
float2 a = (colors[i] - color_sum * (1.0f / 16.0f));
|
||||
covariance[0] += a.x * a.x;
|
||||
covariance[1] += a.x * a.y;
|
||||
covariance[3] += a.y * a.y;
|
||||
}
|
||||
#else
|
||||
|
||||
const int idx = threadIdx.x;
|
||||
|
||||
float2 diff = (colors[idx] - color_sum * (1.0f / 16.0f));
|
||||
|
||||
__shared__ float covariance[16*3];
|
||||
|
||||
covariance[3 * idx + 0] = diff.x * diff.x;
|
||||
covariance[3 * idx + 1] = diff.x * diff.y;
|
||||
covariance[3 * idx + 2] = diff.y * diff.y;
|
||||
|
||||
for(int d = 8; d > 0; d >>= 1)
|
||||
{
|
||||
if (idx < d)
|
||||
{
|
||||
covariance[3 * idx + 0] += covariance[3 * (idx+d) + 0];
|
||||
covariance[3 * idx + 1] += covariance[3 * (idx+d) + 1];
|
||||
covariance[3 * idx + 2] += covariance[3 * (idx+d) + 2];
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
// Compute first eigen vector.
|
||||
return firstEigenVector2D(covariance);
|
||||
}
|
||||
|
||||
|
||||
#endif // CUDAMATH_H
|
||||
|
||||
Loading…
Reference in New Issue