-#include "complexmat.cuh"
+#include "complexmat.hpp"
-__global__ void sqr_norm_kernel(int n, float* out, float* data, float rows, float cols)
+
+__global__ void sqr_norm_kernel(const float *in, float *block_res, int total)
{
extern __shared__ float sdata[];
- int i = blockDim.x * threadIdx.y + threadIdx.x;
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- sdata[i] = 0;
- sdata[i] = data[threadId]*data[threadId] + data[threadId+1]*data[threadId+1];
- __syncthreads();
+ int in_idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+ int i = threadIdx.x;
- for (unsigned int s=(blockDim.x*blockDim.y+1)/2, old_s = blockDim.x*blockDim.y;s>0; s>>=1) {
-
- if(old_s&1) s+=1;
+ if (in_idx >= total * 2)
+ sdata[i] = 0;
+ else
+ sdata[i] = in[in_idx] * in[in_idx] + in[in_idx + 1] * in[in_idx + 1];
- if (i < s && i+s < old_s) {
+ for (unsigned s = (blockDim.x + 1) / 2; s > 0; s >>= 1) {
+ __syncthreads();
+ if (i < s)
sdata[i] += sdata[i + s];
- }
- old_s = s;
- __syncthreads();
- }
-
- if(i == 0){
- atomicAdd(&out[blockId/n], sdata[0]/(rows*cols));
}
+
+ if (i == 0)
+ block_res[blockIdx.x] = sdata[0];
}
-void ComplexMat::sqr_norm(float *result) const
+void ComplexMat_::sqr_norm(DynMem &result) const
{
- cudaMemset(result, 0, n_scales*sizeof(float));
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
-
- sqr_norm_kernel<<<numBlocks, threadsPerBlock, rows*cols*sizeof(float)>>>(n_channels/n_scales, result, p_data, rows, cols);
-
- return;
+ assert(n_scales == 1);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(1024);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ DynMem block_res(blocks.x);
+
+ sqr_norm_kernel<<<blocks, threads, threads.x * sizeof(float)>>>((const float*)p_data.deviceMem(),
+ block_res.deviceMem(), total);
+ CudaCheckError();
+ CudaSafeCall(cudaStreamSynchronize(cudaStreamPerThread));
+
+ T res = 0;
+ for (int i = 0; i < blocks.x; i++)
+ res += block_res[i];
+ result.hostMem()[0] = res / static_cast<T>(cols * rows);
}
-__global__ void sqr_mag_kernel(float* data, float* result)
+__global__ void sqr_mag_kernel(const float *data, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
- result[threadId] = data[threadId]*data[threadId] + data[threadId+1]*data[threadId+1];
- result[threadId+1] = 0;
+ result[idx] = data[idx] * data[idx] + data[idx + 1] * data[idx + 1];
+ result[idx + 1] = 0;
}
-ComplexMat ComplexMat::sqr_mag() const
+ComplexMat_ ComplexMat_::sqr_mag() const
{
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- sqr_mag_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, result.p_data);
-
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ sqr_mag_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(), (float*)result.p_data.deviceMem());
+ CudaCheckError();
+
return result;
}
-__global__ void conj_kernel(float* data, float* result)
+__global__ void conj_kernel(const float *data, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data[threadId];
- result[threadId+1] = -data[threadId+1];
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = data[idx];
+ result[idx + 1] = -data[idx + 1];
}
-ComplexMat ComplexMat::conj() const
+ComplexMat_ ComplexMat_::conj() const
{
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- conj_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, result.p_data);
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ conj_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(), (float*)result.p_data.deviceMem());
+ CudaCheckError();
+
return result;
}
-ComplexMat ComplexMat::sum_over_channels() const
+__global__ static void sum_channels(float *dest, const float *src, uint channels, uint num_channel_elem)
{
-// assert(p_data.size() > 1);
- ComplexMat result(this->rows, this->cols, 1);
- return result;
+ int idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+ if (idx >= num_channel_elem)
+ return;
+
+ float acc = 0;
+ for (uint i = 0; i < channels; ++i)
+ acc += src[idx + i * num_channel_elem];
+ dest[idx] = acc;
}
-cufftComplex* ComplexMat::get_p_data() const
+ComplexMat_ ComplexMat_::sum_over_channels() const
{
- return (cufftComplex*) p_data;
+ assert(p_data.num_elem == n_channels * rows * cols);
+
+ uint n_channels_per_scale = n_channels / n_scales;
+ uint scale_offset = n_channels_per_scale * rows * cols;
+
+ ComplexMat_ result(this->rows, this->cols, 1, n_scales);
+
+ const uint total = rows * cols * 2;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ for (uint scale = 0; scale < n_scales; ++scale) {
+ sum_channels<<<blocks, threads>>>(reinterpret_cast<float*>(result.p_data.deviceMem() + scale * scale_offset),
+ reinterpret_cast<const float*>(p_data.deviceMem() + scale * scale_offset),
+ n_channels_per_scale, total);
+ }
+ CudaSafeCall(cudaStreamSynchronize(cudaStreamPerThread));
+ return result;
}
-__global__ void same_num_channels_mul_kernel(float* data_l, float* data_r, float* result)
+__global__ void same_num_channels_mul_kernel(const float *data_l, const float *data_r, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data_l[threadId]*data_r[threadId] - data_l[threadId+1]*data_r[threadId+1];
- result[threadId+1] = data_l[threadId]*data_r[threadId+1] + data_l[threadId+1]*data_r[threadId];
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = data_l[idx] * data_r[idx] - data_l[idx + 1] * data_r[idx + 1];
+ result[idx + 1] = data_l[idx] * data_r[idx + 1] + data_l[idx + 1] * data_r[idx];
}
-//element-wise per channel multiplication, division and addition
-ComplexMat ComplexMat::operator*(const ComplexMat & rhs) const
+// element-wise per channel multiplication, division and addition
+ComplexMat_ ComplexMat_::operator*(const ComplexMat_ &rhs) const
{
assert(rhs.n_channels == n_channels && rhs.cols == cols && rhs.rows == rows);
-
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- same_num_channels_mul_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs.p_data, result.p_data);
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ same_num_channels_mul_kernel<<<blocks, threads, 0>>>((float*)this->p_data.deviceMem(),
+ (float*)rhs.p_data.deviceMem(),
+ (float*)result.p_data.deviceMem());
+ CudaCheckError();
return result;
}
-__global__ void same_num_channels_div_kernel(float* data_l, float* data_r, float* result)
+__global__ void same_num_channels_div_kernel(const float *data_l, const float *data_r, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = (data_l[threadId]*data_r[threadId] + data_l[threadId+1]*data_r[threadId+1])/
- (data_r[threadId]*data_r[threadId] + data_r[threadId+1]*data_r[threadId+1]);
- result[threadId+1] = (data_l[threadId+1]*data_r[threadId] - data_l[threadId]*data_r[threadId+1])/
- (data_r[threadId]*data_r[threadId] + data_r[threadId+1]*data_r[threadId+1]);
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = (data_l[idx] * data_r[idx] + data_l[idx + 1] * data_r[idx + 1]) /
+ (data_r[idx] * data_r[idx] + data_r[idx + 1] * data_r[idx + 1]);
+ result[idx + 1] = (data_l[idx + 1] * data_r[idx] - data_l[idx] * data_r[idx + 1]) /
+ (data_r[idx] * data_r[idx] + data_r[idx + 1] * data_r[idx + 1]);
}
-ComplexMat ComplexMat::operator/(const ComplexMat & rhs) const
+ComplexMat_ ComplexMat_::operator/(const ComplexMat_ &rhs) const
{
assert(rhs.n_channels == n_channels && rhs.cols == cols && rhs.rows == rows);
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- same_num_channels_div_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs.p_data, result.p_data);
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ same_num_channels_div_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(),
+ (float*)rhs.p_data.deviceMem(),
+ (float*)result.p_data.deviceMem());
+ CudaCheckError();
return result;
}
-__global__ void same_num_channels_add_kernel(float* data_l, float* data_r, float* result)
+__global__ void same_num_channels_add_kernel(const float *data_l, const float *data_r, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data_l[threadId]+data_r[threadId];
- result[threadId+1] = data_l[threadId+1]+data_r[threadId+1];
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = data_l[idx] + data_r[idx];
+ result[idx + 1] = data_l[idx + 1] + data_r[idx + 1];
}
-ComplexMat ComplexMat::operator+(const ComplexMat & rhs) const
+ComplexMat_ ComplexMat_::operator+(const ComplexMat_ &rhs) const
{
assert(rhs.n_channels == n_channels && rhs.cols == cols && rhs.rows == rows);
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- same_num_channels_add_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs.p_data, result.p_data);
-
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ same_num_channels_add_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(),
+ (float*)rhs.p_data.deviceMem(),
+ (float*)result.p_data.deviceMem());
+ CudaCheckError();
+
return result;
}
-__global__ void constant_mul_kernel(float* data_l, float constant, float* result)
+__global__ void constant_mul_kernel(const float *data_l, float constant, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data_l[threadId]*constant;
- result[threadId+1] = data_l[threadId+1]*constant;
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = data_l[idx] * constant;
+ result[idx + 1] = data_l[idx + 1] * constant;
}
-ComplexMat ComplexMat::operator*(const float & rhs) const
+ComplexMat_ ComplexMat_::operator*(const float &rhs) const
{
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- constant_mul_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs, result.p_data);
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ constant_mul_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(),
+ rhs,
+ (float*)result.p_data.deviceMem());
+ CudaCheckError();
return result;
}
-__global__ void constant_add_kernel(float* data_l, float constant, float* result)
+__global__ void constant_add_kernel(const float *data_l, float constant, float *result)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data_l[threadId]+constant;
- result[threadId+1] = data_l[threadId+1];
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+
+ result[idx] = data_l[idx] + constant;
+ result[idx + 1] = data_l[idx + 1];
}
-ComplexMat ComplexMat::operator+(const float & rhs) const
+ComplexMat_ ComplexMat_::operator+(const float &rhs) const
{
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- constant_add_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs, result.p_data);
+ ComplexMat_ result = ComplexMat_::same_size(*this);
+
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ constant_add_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(),
+ rhs,
+ (float*)result.p_data.deviceMem());
+ CudaCheckError();
return result;
}
-__global__ void one_channel_mul_kernel(float* data_l, float* data_r, float* result)
+__global__ void one_channel_mul_kernel(const float *data_l, const float *data_r, float *result, int channel_total)
{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
- int one_ch_index = 2*((threadIdx.y * blockDim.x) + threadIdx.x);
-
- result[threadId] = data_l[threadId]*data_r[one_ch_index] - data_l[threadId+1]*data_r[one_ch_index+1];
- result[threadId+1] = data_l[threadId]*data_r[one_ch_index+1] + data_l[threadId+1]*data_r[one_ch_index];
+ int idx = 2 * (blockIdx.x * blockDim.x + threadIdx.x);
+ int one_ch_idx = idx % (2 * channel_total);
+
+ result[idx] = data_l[idx] * data_r[one_ch_idx] - data_l[idx + 1] * data_r[one_ch_idx + 1];
+ result[idx + 1] = data_l[idx] * data_r[one_ch_idx + 1] + data_l[idx + 1] * data_r[one_ch_idx];
}
-//multiplying element-wise multichannel by one channel mats (rhs mat is with one channel)
-ComplexMat ComplexMat::mul(const ComplexMat & rhs) const
+// multiplying element-wise multichannel by one channel mats (rhs mat is with one channel)
+ComplexMat_ ComplexMat_::mul(const ComplexMat_ &rhs) const
{
assert(rhs.n_channels == 1 && rhs.cols == cols && rhs.rows == rows);
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- one_channel_mul_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs.p_data, result.p_data);
-
- return result;
-}
+ ComplexMat_ result = ComplexMat_::same_size(*this);
-__global__ void scales_channel_mul_kernel(float* data_l, float* data_r, float* result)
-{
- int blockId = blockIdx.x + blockIdx.y * gridDim.x;
- int threadId = 2*(blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
- int one_ch_index = 2*((threadIdx.y * blockDim.x) + threadIdx.x+blockIdx.x*blockDim.x*blockDim.y);
-
- result[threadId] = data_l[threadId]*data_r[one_ch_index] - data_l[threadId+1]*data_r[one_ch_index+1];
- result[threadId+1] = data_l[threadId]*data_r[one_ch_index+1] + data_l[threadId+1]*data_r[one_ch_index];
-}
+ const uint total = n_channels * rows * cols;
+ const dim3 threads(256);
+ const dim3 blocks((total + threads.x - 1) / threads.x);
+
+ one_channel_mul_kernel<<<threads, blocks, 0>>>((float*)this->p_data.deviceMem(),
+ (float*)rhs.p_data.deviceMem(),
+ (float*)result.p_data.deviceMem(),
+ rows * cols);
+ CudaCheckError();
-//multiplying element-wise multichannel by one channel mats (rhs mat is with multiple channel)
-ComplexMat ComplexMat::mul2(const ComplexMat & rhs) const
-{
- assert(rhs.n_channels == n_channels/n_scales && rhs.cols == cols && rhs.rows == rows);
-
- ComplexMat result(this->rows, this->cols, this->channels(), this->n_scales);
-
- dim3 threadsPerBlock(rows, cols);
- dim3 numBlocks(n_channels/n_scales, n_scales);
- scales_channel_mul_kernel<<<numBlocks, threadsPerBlock>>>(this->p_data, rhs.p_data, result.p_data);
-
return result;
}
-void ComplexMat::operator=(ComplexMat & rhs)
-{
- cols = rhs.cols;
- rows = rhs.rows;
- n_channels = rhs.n_channels;
- n_scales = rhs.n_scales;
-
- p_data = rhs.p_data;
-
-}
+// __global__ void scales_channel_mul_kernel(float *data_l, float *data_r, float *result)
+// {
+// int blockId = blockIdx.x + blockIdx.y * gridDim.x;
+// int idx = 2 * (blockId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x);
+// int one_ch_index = 2 * ((threadIdx.y * blockDim.x) + threadIdx.x + blockIdx.x * blockDim.x * blockDim.y);
-void ComplexMat::operator=(ComplexMat && rhs)
-{
- cols = rhs.cols;
- rows = rhs.rows;
- n_channels = rhs.n_channels;
- n_scales = rhs.n_scales;
-
- p_data = rhs.p_data;
-
- rhs.p_data = nullptr;
-}
+// result[idx] = data_l[idx] * data_r[one_ch_index] - data_l[idx + 1] * data_r[one_ch_index + 1];
+// result[idx + 1] = data_l[idx] * data_r[one_ch_index + 1] + data_l[idx + 1] * data_r[one_ch_index];
+// }
+
+// multiplying element-wise multichannel by one channel mats (rhs mat is with multiple channel)
+// ComplexMat_ ComplexMat_::mul2(const ComplexMat_ &rhs) const
+// {
+// assert(rhs.n_channels == n_channels / n_scales && rhs.cols == cols && rhs.rows == rows);
+
+// ComplexMat_ result(this->rows, this->cols, this->channels(), this->n_scales);
+
+// dim3 threadsPerBlock(rows, cols);
+// dim3 numBlocks(n_channels / n_scales, n_scales);
+// scales_channel_mul_kernel<<<threads, blocks, 0>>>(this->p_data, rhs.p_data, result.p_data);
+// CudaCheckError();
+
+// return result;
+// }
+
+// void ComplexMat_::operator=(ComplexMat_ &&rhs)
+// {
+// cols = rhs.cols;
+// rows = rhs.rows;
+// n_channels = rhs.n_channels;
+// n_scales = rhs.n_scales;
+
+// p_data = rhs.p_data;
+
+// rhs.p_data = nullptr;
+// }
projects / hercules2020 / kcf.git / blobdiff