DXT3 and DXT5 cuda compressors.
This commit is contained in:
castano
parent
83490b0b66
commit
31c9ef0413
@ -141,6 +141,7 @@ __device__ void loadColorBlock(const uint * image, float3 colors[16], float3 sum
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const int bid = blockIdx.x;
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const int idx = threadIdx.x;
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__shared__ float3 rawColors[16];
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__shared__ float dps[16];
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if (idx < 16)
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@ -148,10 +149,13 @@ __device__ void loadColorBlock(const uint * image, float3 colors[16], float3 sum
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// Read color and copy to shared mem.
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uint c = image[(bid) * 16 + idx];
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colors[idx].z = ((c >> 0) & 0xFF) * (1.0f / 255.0f);
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colors[idx].y = ((c >> 8) & 0xFF) * (1.0f / 255.0f);
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colors[idx].x = ((c >> 16) & 0xFF) * (1.0f / 255.0f);
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weights[idx] = ((c >> 24) & 0xFF) * (1.0f / 255.0f);
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rawColors[idx].z = ((c >> 0) & 0xFF) * (1.0f / 255.0f);
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rawColors[idx].y = ((c >> 8) & 0xFF) * (1.0f / 255.0f);
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rawColors[idx].x = ((c >> 16) & 0xFF) * (1.0f / 255.0f);
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weights[idx] = (((c >> 24) & 0xFF) + 1) * (1.0f / 256.0f);
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colors[idx] = rawColors[idx] * weights[idx];
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// No need to synchronize, 16 < warp size.
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#if __DEVICE_EMULATION__
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@ -162,7 +166,7 @@ __device__ void loadColorBlock(const uint * image, float3 colors[16], float3 sum
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colorSums(colors, sums);
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float3 axis = bestFitLine(colors, sums[0]);
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dps[idx] = dot(colors[idx], axis);
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dps[idx] = dot(rawColors[idx], axis);
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#if __DEVICE_EMULATION__
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} __debugsync(); if (idx < 16) {
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@ -283,7 +287,7 @@ __constant__ float alphaTable3[4] = { 4.0f, 0.0f, 2.0f, 2.0f };
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__constant__ const uint prods4[4] = { 0x090000,0x000900,0x040102,0x010402 };
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__constant__ const uint prods3[4] = { 0x040000,0x000400,0x040101,0x010401 };
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__device__ float evalPermutation4(const float3 * colors, uint permutation, ushort * start, ushort * end, float3 color_sum)
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__device__ float evalPermutation4(const float3 * colors, float3 color_sum, uint permutation, ushort * start, ushort * end)
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{
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// Compute endpoints using least squares.
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float3 alphax_sum = make_float3(0.0f, 0.0f, 0.0f);
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@ -318,7 +322,7 @@ __device__ float evalPermutation4(const float3 * colors, uint permutation, ushor
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return (1.0f / 9.0f) * dot(e, kColorMetric);
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}
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__device__ float evalPermutation3(const float3 * colors, uint permutation, ushort * start, ushort * end, float3 color_sum)
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__device__ float evalPermutation3(const float3 * colors, float3 color_sum, uint permutation, ushort * start, ushort * end)
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{
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// Compute endpoints using least squares.
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float3 alphax_sum = make_float3(0.0f, 0.0f, 0.0f);
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@ -353,14 +357,13 @@ __device__ float evalPermutation3(const float3 * colors, uint permutation, ushor
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return (1.0f / 4.0f) * dot(e, kColorMetric);
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}
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__device__ float evalPermutation4(const float3 * colors, const float * weights, uint permutation, ushort * start, ushort * end)
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__device__ float evalPermutation4(const float3 * colors, const float * weights, float3 color_sum, uint permutation, ushort * start, ushort * end)
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{
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// Compute endpoints using least squares.
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float alpha2_sum = 0.0f;
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float beta2_sum = 0.0f;
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float alphabeta_sum = 0.0f;
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float3 alphax_sum = make_float3(0.0f, 0.0f, 0.0f);
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float3 betax_sum = make_float3(0.0f, 0.0f, 0.0f);
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// Compute alpha & beta for this permutation.
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for (int i = 0; i < 16; i++)
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@ -374,10 +377,11 @@ __device__ float evalPermutation4(const float3 * colors, const float * weights,
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alpha2_sum += alpha * alpha * weights[i];
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beta2_sum += beta * beta * weights[i];
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alphabeta_sum += alpha * beta * weights[i];
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alphax_sum += alpha * colors[i] * weights[i];
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betax_sum += beta * colors[i] * weights[i];
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alphax_sum += alpha * colors[i];
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}
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float3 betax_sum = color_sum - alphax_sum;
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const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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float3 a = (alphax_sum * beta2_sum - betax_sum * alphabeta_sum) * factor;
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@ -393,45 +397,47 @@ __device__ float evalPermutation4(const float3 * colors, const float * weights,
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return dot(e, kColorMetric);
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}
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__device__ float evalPermutation3(const float3 * colors, const float * weights, uint permutation, ushort * start, ushort * end)
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{
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// Compute endpoints using least squares.
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float alpha2_sum = 0.0f;
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float beta2_sum = 0.0f;
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float alphabeta_sum = 0.0f;
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float3 alphax_sum = make_float3(0.0f, 0.0f, 0.0f);
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float3 betax_sum = make_float3(0.0f, 0.0f, 0.0f);
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// Compute alpha & beta for this permutation.
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for (int i = 0; i < 16; i++)
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{
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const uint bits = permutation >> (2*i);
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float beta = (bits & 1);
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if (bits & 2) beta = 0.5f;
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float alpha = 1.0f - beta;
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alpha2_sum += alpha * alpha * weights[i];
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beta2_sum += beta * beta * weights[i];
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alphabeta_sum += alpha * beta * weights[i];
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alphax_sum += alpha * colors[i] * weights[i];
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betax_sum += beta * colors[i] * weights[i];
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}
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const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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float3 a = (alphax_sum * beta2_sum - betax_sum * alphabeta_sum) * factor;
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float3 b = (betax_sum * alpha2_sum - alphax_sum * alphabeta_sum) * factor;
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// Round a, b to the closest 5-6-5 color and expand...
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a = roundAndExpand(a, start);
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b = roundAndExpand(b, end);
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// compute the error
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float3 e = a * a * alpha2_sum + b * b * beta2_sum + 2.0f * (a * b * alphabeta_sum - a * alphax_sum - b * betax_sum);
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return dot(e, kColorMetric);
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}
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/*
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__device__ float evalPermutation3(const float3 * colors, const float * weights, uint permutation, ushort * start, ushort * end)
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{
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// Compute endpoints using least squares.
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float alpha2_sum = 0.0f;
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float beta2_sum = 0.0f;
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float alphabeta_sum = 0.0f;
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float3 alphax_sum = make_float3(0.0f, 0.0f, 0.0f);
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// Compute alpha & beta for this permutation.
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for (int i = 0; i < 16; i++)
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{
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const uint bits = permutation >> (2*i);
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float beta = (bits & 1);
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if (bits & 2) beta = 0.5f;
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float alpha = 1.0f - beta;
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alpha2_sum += alpha * alpha * weights[i];
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beta2_sum += beta * beta * weights[i];
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alphabeta_sum += alpha * beta * weights[i];
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alphax_sum += alpha * colors[i];
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}
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float3 betax_sum = color_sum - alphax_sum;
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const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
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float3 a = (alphax_sum * beta2_sum - betax_sum * alphabeta_sum) * factor;
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float3 b = (betax_sum * alpha2_sum - alphax_sum * alphabeta_sum) * factor;
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// Round a, b to the closest 5-6-5 color and expand...
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a = roundAndExpand(a, start);
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b = roundAndExpand(b, end);
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// compute the error
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float3 e = a * a * alpha2_sum + b * b * beta2_sum + 2.0f * (a * b * alphabeta_sum - a * alphax_sum - b * betax_sum);
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return dot(e, kColorMetric);
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}
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*/
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////////////////////////////////////////////////////////////////////////////////
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@ -454,7 +460,7 @@ __device__ void evalAllPermutations(const float3 * colors, float3 colorSum, cons
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uint permutation = permutations[pidx];
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if (pidx < 160) s_permutations[pidx] = permutation;
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float error = evalPermutation4(colors, permutation, &start, &end, colorSum);
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float error = evalPermutation4(colors, colorSum, permutation, &start, &end);
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if (error < bestError)
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{
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@ -478,7 +484,7 @@ __device__ void evalAllPermutations(const float3 * colors, float3 colorSum, cons
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ushort start, end;
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uint permutation = s_permutations[pidx];
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float error = evalPermutation3(colors, permutation, &start, &end, colorSum);
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float error = evalPermutation3(colors, colorSum, permutation, &start, &end);
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if (error < bestError)
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{
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@ -498,6 +504,7 @@ __device__ void evalAllPermutations(const float3 * colors, float3 colorSum, cons
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errors[idx] = bestError;
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}
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/*
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__device__ void evalAllPermutations(const float3 * colors, const float * weights, const uint * permutations, ushort & bestStart, ushort & bestEnd, uint & bestPermutation, float * errors)
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{
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const int idx = threadIdx.x;
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@ -558,9 +565,9 @@ __device__ void evalAllPermutations(const float3 * colors, const float * weights
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errors[idx] = bestError;
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}
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*/
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__device__ void evalLevel4Permutations(const float3 * colors, const float * weights, const uint * permutations, ushort & bestStart, ushort & bestEnd, uint & bestPermutation, float * errors)
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__device__ void evalLevel4Permutations(const float3 * colors, const float * weights, float3 colorSum, const uint * permutations, ushort & bestStart, ushort & bestEnd, uint & bestPermutation, float * errors)
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{
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const int idx = threadIdx.x;
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@ -574,7 +581,7 @@ __device__ void evalLevel4Permutations(const float3 * colors, const float * weig
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ushort start, end;
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uint permutation = permutations[pidx];
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float error = evalPermutation4(colors, weights, permutation, &start, &end);
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float error = evalPermutation4(colors, weights, colorSum, permutation, &start, &end);
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if (error < bestError)
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{
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@ -755,7 +762,7 @@ __global__ void compressWeighted(const uint * permutations, const uint * image,
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__shared__ float errors[NUM_THREADS];
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evalLevel4Permutations(colors, weights, permutations, bestStart, bestEnd, bestPermutation, errors);
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evalLevel4Permutations(colors, weights, sums[0], permutations, bestStart, bestEnd, bestPermutation, errors);
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// Use a parallel reduction to find minimum error.
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int minIdx = findMinError(errors);
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@ -768,6 +775,7 @@ __global__ void compressWeighted(const uint * permutations, const uint * image,
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}
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/*
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__device__ float computeError(const float weights[16], uchar a0, uchar a1)
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{
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float palette[6];
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@ -803,7 +811,7 @@ inline __device__ uchar roundAndExpand(float a)
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{
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return rintf(__saturatef(a) * 255.0f);
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}
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*/
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/*
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__device__ void optimizeAlpha8(const float alphas[16], uchar & a0, uchar & a1)
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{
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@ -838,7 +846,7 @@ __device__ void optimizeAlpha8(const float alphas[16], uchar & a0, uchar & a1)
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a1 = roundAndExpand(b);
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}
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*/
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/*
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__device__ void compressAlpha(const float alphas[16], uint4 * result)
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{
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const int tid = threadIdx.x;
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@ -932,7 +940,7 @@ __global__ void compressDXT5(const uint * permutations, const uint * image, uint
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__shared__ float errors[NUM_THREADS];
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evalLevel4Permutations(colors, weights, permutations, bestStart, bestEnd, bestPermutation, errors);
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evalLevel4Permutations(colors, weights, sums[0], permutations, bestStart, bestEnd, bestPermutation, errors);
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// Use a parallel reduction to find minimum error.
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int minIdx = findMinError(errors);
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@ -943,7 +951,7 @@ __global__ void compressDXT5(const uint * permutations, const uint * image, uint
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saveBlockDXT1(bestStart, bestEnd, bestPermutation, xrefs, (uint2 *)result);
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}
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}
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*/
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////////////////////////////////////////////////////////////////////////////////
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// Setup kernel
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@ -29,10 +29,12 @@
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#include <nvimage/ColorBlock.h>
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#include <nvimage/BlockDXT.h>
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#include <nvimage/nvtt/CompressionOptions.h>
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#include <nvimage/nvtt/FastCompressDXT.h>
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#include "CudaCompressDXT.h"
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#include "CudaUtils.h"
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#if defined HAVE_CUDA
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#include <cuda_runtime.h>
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#endif
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@ -186,6 +188,28 @@ static void doPrecomputation()
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#endif
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// Convert linear image to block linear.
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static void convertToBlockLinear(const Image * image, uint * blockLinearImage)
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{
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const uint w = (image->width() + 3) / 4;
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const uint h = (image->height() + 3) / 4;
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for(uint by = 0; by < h; by++) {
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for(uint bx = 0; bx < w; bx++) {
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const uint bw = min(image->width() - bx * 4, 4U);
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const uint bh = min(image->height() - by * 4, 4U);
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for (uint i = 0; i < 16; i++) {
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const int x = (i % 4) % bw;
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const int y = (i / 4) % bh;
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blockLinearImage[(by * w + bx) * 16 + i] = image->pixel(bx * 4 + x, by * 4 + y).u;
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}
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}
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}
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}
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// @@ This code is very repetitive and needs to be cleaned up.
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/// Compress image using CUDA.
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void nv::cudaCompressDXT1(const Image * image, const OutputOptions & outputOptions, const CompressionOptions::Private & compressionOptions)
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@ -201,24 +225,11 @@ void nv::cudaCompressDXT1(const Image * image, const OutputOptions & outputOptio
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uint imageSize = w * h * 16 * sizeof(Color32);
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uint * blockLinearImage = (uint *) malloc(imageSize);
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// Convert linear image to block linear.
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for(uint by = 0; by < h; by++) {
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for(uint bx = 0; bx < w; bx++) {
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const uint bw = min(image->width() - bx * 4, 4U);
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const uint bh = min(image->height() - by * 4, 4U);
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for (uint i = 0; i < 16; i++) {
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const int x = (i % 4) % bw;
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const int y = (i / 4) % bh;
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blockLinearImage[(by * w + bx) * 16 + i] = image->pixel(bx * 4 + x, by * 4 + y).u;
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}
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}
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}
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convertToBlockLinear(image, blockLinearImage);
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const uint blockNum = w * h;
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const uint compressedSize = blockNum * 8;
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const uint blockMax = 32768; // 65535
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const uint blockMax = 32768; // 49152, 65535
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// Allocate image in device memory.
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uint * d_data = NULL;
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@ -283,6 +294,201 @@ void nv::cudaCompressDXT1(const Image * image, const OutputOptions & outputOptio
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}
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/// Compress image using CUDA.
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void nv::cudaCompressDXT3(const Image * image, const OutputOptions & outputOptions, const CompressionOptions::Private & compressionOptions)
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{
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nvDebugCheck(cuda::isHardwarePresent());
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#if defined HAVE_CUDA
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doPrecomputation();
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// Image size in blocks.
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const uint w = (image->width() + 3) / 4;
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const uint h = (image->height() + 3) / 4;
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uint imageSize = w * h * 16 * sizeof(Color32);
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uint * blockLinearImage = (uint *) malloc(imageSize);
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convertToBlockLinear(image, blockLinearImage);
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const uint blockNum = w * h;
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const uint compressedSize = blockNum * 8;
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const uint blockMax = 32768; // 49152, 65535
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// Allocate image in device memory.
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uint * d_data = NULL;
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cudaMalloc((void**) &d_data, min(imageSize, blockMax * 64U));
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// Allocate result.
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uint * d_result = NULL;
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cudaMalloc((void**) &d_result, min(compressedSize, blockMax * 8U));
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AlphaBlockDXT3 * alphaBlocks = NULL;
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alphaBlocks = (AlphaBlockDXT3 *)malloc(min(compressedSize, blockMax * 8U));
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setupCompressKernel(compressionOptions.colorWeight.ptr());
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clock_t start = clock();
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uint bn = 0;
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while(bn != blockNum)
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{
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uint count = min(blockNum - bn, blockMax);
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cudaMemcpy(d_data, blockLinearImage + bn * 16, count * 64, cudaMemcpyHostToDevice);
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// Launch kernel.
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compressWeightedKernel(count, d_data, d_result, d_bitmaps);
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// Compress alpha in parallel with the GPU.
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for (uint i = 0; i < count; i++)
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{
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ColorBlock rgba(blockLinearImage + (bn + i) * 16);
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compressBlock(rgba, alphaBlocks + i);
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}
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// Check for errors.
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cudaError_t err = cudaGetLastError();
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if (err != cudaSuccess)
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{
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nvDebug("CUDA Error: %s\n", cudaGetErrorString(err));
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if (outputOptions.errorHandler != NULL)
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{
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outputOptions.errorHandler->error(Error_CudaError);
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}
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}
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// Copy result to host, overwrite swizzled image.
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cudaMemcpy(blockLinearImage, d_result, count * 8, cudaMemcpyDeviceToHost);
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// Output result.
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if (outputOptions.outputHandler != NULL)
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{
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for (uint i = 0; i < count; i++)
|
||||
{
|
||||
outputOptions.outputHandler->writeData(alphaBlocks + i, 8);
|
||||
outputOptions.outputHandler->writeData(blockLinearImage + i * 2, 8);
|
||||
}
|
||||
}
|
||||
|
||||
bn += count;
|
||||
}
|
||||
|
||||
clock_t end = clock();
|
||||
printf("\rCUDA time taken: %.3f seconds\n", float(end-start) / CLOCKS_PER_SEC);
|
||||
|
||||
free(alphaBlocks);
|
||||
free(blockLinearImage);
|
||||
cudaFree(d_data);
|
||||
cudaFree(d_result);
|
||||
|
||||
#else
|
||||
if (outputOptions.errorHandler != NULL)
|
||||
{
|
||||
outputOptions.errorHandler->error(Error_CudaError);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
/// Compress image using CUDA.
|
||||
void nv::cudaCompressDXT5(const Image * image, const OutputOptions & outputOptions, const CompressionOptions::Private & compressionOptions)
|
||||
{
|
||||
nvDebugCheck(cuda::isHardwarePresent());
|
||||
#if defined HAVE_CUDA
|
||||
|
||||
doPrecomputation();
|
||||
|
||||
// Image size in blocks.
|
||||
const uint w = (image->width() + 3) / 4;
|
||||
const uint h = (image->height() + 3) / 4;
|
||||
|
||||
uint imageSize = w * h * 16 * sizeof(Color32);
|
||||
uint * blockLinearImage = (uint *) malloc(imageSize);
|
||||
convertToBlockLinear(image, blockLinearImage);
|
||||
|
||||
const uint blockNum = w * h;
|
||||
const uint compressedSize = blockNum * 8;
|
||||
const uint blockMax = 32768; // 49152, 65535
|
||||
|
||||
// Allocate image in device memory.
|
||||
uint * d_data = NULL;
|
||||
cudaMalloc((void**) &d_data, min(imageSize, blockMax * 64U));
|
||||
|
||||
// Allocate result.
|
||||
uint * d_result = NULL;
|
||||
cudaMalloc((void**) &d_result, min(compressedSize, blockMax * 8U));
|
||||
|
||||
AlphaBlockDXT5 * alphaBlocks = NULL;
|
||||
alphaBlocks = (AlphaBlockDXT5 *)malloc(min(compressedSize, blockMax * 8U));
|
||||
|
||||
setupCompressKernel(compressionOptions.colorWeight.ptr());
|
||||
|
||||
clock_t start = clock();
|
||||
|
||||
uint bn = 0;
|
||||
while(bn != blockNum)
|
||||
{
|
||||
uint count = min(blockNum - bn, blockMax);
|
||||
|
||||
cudaMemcpy(d_data, blockLinearImage + bn * 16, count * 64, cudaMemcpyHostToDevice);
|
||||
|
||||
// Launch kernel.
|
||||
compressWeightedKernel(count, d_data, d_result, d_bitmaps);
|
||||
|
||||
// Compress alpha in parallel with the GPU.
|
||||
for (uint i = 0; i < count; i++)
|
||||
{
|
||||
ColorBlock rgba(blockLinearImage + (bn + i) * 16);
|
||||
compressBlock_Iterative(rgba, alphaBlocks + i);
|
||||
}
|
||||
|
||||
// Check for errors.
|
||||
cudaError_t err = cudaGetLastError();
|
||||
if (err != cudaSuccess)
|
||||
{
|
||||
nvDebug("CUDA Error: %s\n", cudaGetErrorString(err));
|
||||
|
||||
if (outputOptions.errorHandler != NULL)
|
||||
{
|
||||
outputOptions.errorHandler->error(Error_CudaError);
|
||||
}
|
||||
}
|
||||
|
||||
// Copy result to host, overwrite swizzled image.
|
||||
cudaMemcpy(blockLinearImage, d_result, count * 8, cudaMemcpyDeviceToHost);
|
||||
|
||||
// Output result.
|
||||
if (outputOptions.outputHandler != NULL)
|
||||
{
|
||||
for (uint i = 0; i < count; i++)
|
||||
{
|
||||
outputOptions.outputHandler->writeData(alphaBlocks + i, 8);
|
||||
outputOptions.outputHandler->writeData(blockLinearImage + i * 2, 8);
|
||||
}
|
||||
}
|
||||
|
||||
bn += count;
|
||||
}
|
||||
|
||||
clock_t end = clock();
|
||||
printf("\rCUDA time taken: %.3f seconds\n", float(end-start) / CLOCKS_PER_SEC);
|
||||
|
||||
free(alphaBlocks);
|
||||
free(blockLinearImage);
|
||||
cudaFree(d_data);
|
||||
cudaFree(d_result);
|
||||
|
||||
#else
|
||||
if (outputOptions.errorHandler != NULL)
|
||||
{
|
||||
outputOptions.errorHandler->error(Error_CudaError);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
|
||||
#if defined HAVE_CUDA
|
||||
|
||||
class Task
|
||||
@ -429,7 +635,7 @@ void nv::cudaCompressDXT1_2(const Image * image, const OutputOptions & outputOpt
|
||||
const uint h = image->height();
|
||||
|
||||
const uint blockNum = ((w + 3) / 4) * ((h + 3) / 4);
|
||||
const uint blockMax = 32768; // 65535
|
||||
const uint blockMax = 32768; // 49152, 65535
|
||||
|
||||
doPrecomputation();
|
||||
|
||||
|
||||
@ -32,6 +32,9 @@ namespace nv
|
||||
class Image;
|
||||
|
||||
void cudaCompressDXT1(const Image * image, const nvtt::OutputOptions & outputOptions, const nvtt::CompressionOptions::Private & compressionOptions);
|
||||
void cudaCompressDXT3(const Image * image, const nvtt::OutputOptions & outputOptions, const nvtt::CompressionOptions::Private & compressionOptions);
|
||||
void cudaCompressDXT5(const Image * image, const nvtt::OutputOptions & outputOptions, const nvtt::CompressionOptions::Private & compressionOptions);
|
||||
|
||||
void cudaCompressDXT1_2(const Image * image, const nvtt::OutputOptions & outputOptions, const nvtt::CompressionOptions::Private & compressionOptions);
|
||||
|
||||
} // nv namespace
|
||||
|
||||
Loading…
Reference in New Issue
Block a user