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Merge branch 'cnet' of https://github.com/ceccocats/tkDNN into cnet

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xavier
2020-02-11 10:32:52 +01:00
parent 801b8b5641 51ffcb1f50
commit b5c0baa99d
3 changed files with 180 additions and 60 deletions
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CMakeLists.txt
+1
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@@ -17,6 +17,7 @@ endif()
find_package(CUDA 9.0 REQUIRED)
SET(CUDA_SEPARABLE_COMPILATION ON)
#set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -arch=sm_30 --compiler-options '-fPIC'")
#set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} --maxrregcount=32)
find_package(CUDNN REQUIRED)
include/tkDNN/kernels.h
+9 -3
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@@ -30,14 +30,20 @@ void upsampleForward(dnnType* srcData, dnnType* dstData,
void float2half(float* srcData, __half* dstData, int size, const cudaStream_t stream = cudaStream_t(0));
// void modulated_deformable_im2col_cuda(cudaStream_t stream,
// const float *data_im, const float *data_offset, const float *data_mask,
// const int batch_size, const int channels, const int height_im, const int width_im,
// const int height_col, const int width_col, const int kernel_h, const int kenerl_w,
// const int pad_h, const int pad_w, const int stride_h, const int stride_w,
// const int dilation_h, const int dilation_w,
// const int deformable_group, float *data_col);
void modulated_deformable_im2col_cuda(cudaStream_t stream,
const float *data_im, const float *data_offset, const float *data_mask,
const int batch_size, const int channels, const int height_im, const int width_im,
const int height_col, const int width_col, const int kernel_h, const int kenerl_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int height_col, const int width_col,
const int deformable_group, float *data_col);
void dcn_v2_cuda_forward(cublasStatus_t stat, cublasHandle_t handle,
float *input, float *weight,
float *bias, float *ones,
src/kernels/deformable_conv.cu
+170 -57
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@@ -9,7 +9,7 @@
i < (n); \
i += blockDim.x * gridDim.x)
const int CUDA_NUM_THREADS = 1024;
const int CUDA_NUM_THREADS = 512;
inline int GET_BLOCKS(const int N)
{
return (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS;
@@ -17,84 +17,89 @@ inline int GET_BLOCKS(const int N)
__device__ float dmcn_im2col_bilinear(const float *bottom_data, const int data_width,
const int height, const int width, float h, float w)
const int height, const int width, float h, float w)
{
int h_low = floor(h);
int w_low = floor(w);
int h_high = h_low + 1;
int w_high = w_low + 1;
int h_low = floor(h);
int w_low = floor(w);
int h_high = h_low + 1;
int w_high = w_low + 1;
float lh = h - h_low;
float lw = w - w_low;
float hh = 1 - lh, hw = 1 - lw;
float lh = h - h_low;
float lw = w - w_low;
float hh = 1 - lh, hw = 1 - lw;
float v1 = 0;
if (h_low >= 0 && w_low >= 0)
v1 = bottom_data[h_low * data_width + w_low];
float v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
v2 = bottom_data[h_low * data_width + w_high];
float v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
v3 = bottom_data[h_high * data_width + w_low];
float v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
v4 = bottom_data[h_high * data_width + w_high];
float v1 = ( (h_low >= 0 && w_low >= 0) ? bottom_data[h_low * data_width + w_low]:0);
float v2 = ( (h_low >= 0 && w_high <= width - 1) ? bottom_data[h_low * data_width + w_high]:0);
float v3 = ( (h_high <= height - 1 && w_low >= 0) ? bottom_data[h_high * data_width + w_low]:0);
float v4 = ( (h_high <= height - 1 && w_high <= width - 1) ? bottom_data[h_high * data_width + w_high]:0);
float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
float w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
float val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
}
__global__ void modulated_deformable_im2col_gpu_kernel(const int n,
const float *data_im, const float *data_offset, const float *data_mask,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int channel_per_deformable_group,
const int batch_size, const int num_channels, const int deformable_group,
const int height_col, const int width_col,
float *data_col)
const float *data_im, const float *data_offset, const float *data_mask,
const int height, const int width,
const int batch_size, const int num_channels, const int deformable_group,
const int height_col, const int width_col,
float *data_col)
{
CUDA_KERNEL_LOOP(index, n)
{
//If n is a power of 2, ( i / n ) is equivalent to ( i ≫ log2 n ) and ( i % n ) is equivalent to ( i & n - 1 ).
const int ind_on_w = index / width_col;
const int ind_on_w_on_h = ind_on_w / height_col;
const int kk = 3 * 3;
// index index of output matrix
const int w_col = index % width_col;
const int h_col = (index / width_col) % height_col;
const int b_col = (index / width_col / height_col) % batch_size;
const int c_im = (index / width_col / height_col) / batch_size;
const int c_col = c_im * kernel_h * kernel_w;
const int h_col = (ind_on_w) % height_col;
const int b_col = (ind_on_w_on_h) % batch_size;
const int c_im = (ind_on_w_on_h) / batch_size;
const int c_col = c_im * kk;
// compute deformable group index
const int deformable_group_index = c_im / channel_per_deformable_group;
const int deformable_group_index = c_im / (int)(num_channels / deformable_group);
const int h_in = h_col * stride_h - pad_h;
const int w_in = w_col * stride_w - pad_w;
const int h_in = h_col - 1;
const int w_in = w_col - 1;
const int s_col = height_col * width_col;
const int s_col2 = 2 * s_col;
float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
//const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
const float *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col;
const int add_ptr = (b_col * deformable_group + deformable_group_index) * kk * s_col;
const float *data_offset_ptr = data_offset + add_ptr + add_ptr;
const float *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col;
const float *data_mask_ptr = data_mask + add_ptr;
for (int i = 0; i < kernel_h; ++i)
const int first_member = w_col + width_col * h_col;
float val = static_cast<float>(0);
#pragma unroll
for (int i = 0; i < 3; ++i)
{
for (int j = 0; j < kernel_w; ++j)
#pragma unroll
for (int j = 0; j < 3; ++j)
{
const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const int iter_member = (i * 3 + j);
// const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
const int data_offset_h_ptr = first_member + s_col2 * iter_member;
// const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
const int data_offset_w_ptr = s_col + first_member + s_col2 * iter_member;
// const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const int data_mask_hw_ptr = first_member + s_col * iter_member;
const float offset_h = data_offset_ptr[data_offset_h_ptr];
const float offset_w = data_offset_ptr[data_offset_w_ptr];
const float mask = data_mask_ptr[data_mask_hw_ptr];
float val = static_cast<float>(0);
const float h_im = h_in + i * dilation_h + offset_h;
const float w_im = w_in + j * dilation_w + offset_w;
const float h_im = offset_h + h_in + i;
const float w_im = offset_w + w_in + j;
//if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
if (h_im < height && w_im < width && h_im > -1 && w_im > -1)
{
//const float map_h = i * dilation_h + offset_h;
//const float map_w = j * dilation_w + offset_w;
@@ -104,7 +109,89 @@ __global__ void modulated_deformable_im2col_gpu_kernel(const int n,
val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
}
*data_col_ptr = val * mask;
data_col_ptr += batch_size * height_col * width_col;
data_col_ptr += batch_size * s_col;
//data_col_ptr += height_col * width_col;
}
}
}
}
__global__ void modulated_deformable_im2col_gpu_kernel2(const int n,
const float *data_im, const float *data_offset, const float *data_mask,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int channel_per_deformable_group,
const int batch_size, const int num_channels, const int deformable_group,
const int height_col, const int width_col,
float *data_col)
{
CUDA_KERNEL_LOOP(index, n)
{
//If n is a power of 2, ( i / n ) is equivalent to ( i ≫ log2 n ) and ( i % n ) is equivalent to ( i & n - 1 ).
// printf("--- %d %d %d %d %d %d %d %d\n",kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w);
const int ind_on_w = index / width_col;
const int ind_on_w_on_h = ind_on_w / height_col;
const int kk = kernel_h * kernel_w;
// index index of output matrix
const int w_col = index % width_col;
const int h_col = (ind_on_w) % height_col;
const int b_col = (ind_on_w_on_h) % batch_size;
const int c_im = (ind_on_w_on_h) / batch_size;
const int c_col = c_im * kk;
// compute deformable group index
const int deformable_group_index = c_im / channel_per_deformable_group;
const int h_in = h_col * stride_h - pad_h;
const int w_in = w_col * stride_w - pad_w;
const int s_col = height_col * width_col;
const int s_col2 = 2 * s_col;
float *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
//const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in;
const float *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width;
const int add_ptr = (b_col * deformable_group + deformable_group_index) * kk * s_col;
const float *data_offset_ptr = data_offset + add_ptr + add_ptr;
const float *data_mask_ptr = data_mask + add_ptr;
const int first_member = w_col + width_col * h_col;
float val = static_cast<float>(0);
#pragma unroll
for (int i = 0; i < kernel_h; ++i)
{
#pragma unroll
for (int j = 0; j < kernel_w; ++j)
{
const int iter_member = (i * kernel_w + j);
// const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
const int data_offset_h_ptr = first_member + s_col2 * iter_member;
// const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col;
const int data_offset_w_ptr = s_col + first_member + s_col2 * iter_member;
// const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const int data_mask_hw_ptr = first_member + s_col * iter_member;
const float offset_h = data_offset_ptr[data_offset_h_ptr];
const float offset_w = data_offset_ptr[data_offset_w_ptr];
const float mask = data_mask_ptr[data_mask_hw_ptr];
const float h_im = offset_h + h_in + i * dilation_h;
const float w_im = offset_w + w_in + j * dilation_w;
//if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) {
if (h_im < height && w_im < width && h_im > -1 && w_im > -1)
{
//const float map_h = i * dilation_h + offset_h;
//const float map_w = j * dilation_w + offset_w;
//const int cur_height = height - h_in;
//const int cur_width = width - w_in;
//val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w);
val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im);
}
*data_col_ptr = val * mask;
data_col_ptr += batch_size * s_col;
//data_col_ptr += height_col * width_col;
}
}
@@ -113,6 +200,28 @@ __global__ void modulated_deformable_im2col_gpu_kernel(const int n,
void modulated_deformable_im2col_cuda(cudaStream_t stream,
const float* data_im, const float* data_offset, const float* data_mask,
const int batch_size, const int channels, const int height_im, const int width_im,
const int height_col, const int width_col,
const int deformable_group, float* data_col) {
// num_axes should be smaller than block size
// const int channel_per_deformable_group = channels / deformable_group;
const int num_kernels = channels * batch_size * height_col * width_col;
modulated_deformable_im2col_gpu_kernel
<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS,
0, stream>>>(
num_kernels, data_im, data_offset, data_mask, height_im, width_im,
batch_size, channels, deformable_group, height_col, width_col, data_col);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
{
printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
}
}
void modulated_deformable_im2col_cuda2(cudaStream_t stream,
const float* data_im, const float* data_offset, const float* data_mask,
const int batch_size, const int channels, const int height_im, const int width_im,
const int height_col, const int width_col, const int kernel_h, const int kenerl_w,
@@ -122,7 +231,7 @@ void modulated_deformable_im2col_cuda(cudaStream_t stream,
// num_axes should be smaller than block size
const int channel_per_deformable_group = channels / deformable_group;
const int num_kernels = channels * batch_size * height_col * width_col;
modulated_deformable_im2col_gpu_kernel
modulated_deformable_im2col_gpu_kernel2
<<<GET_BLOCKS(num_kernels), CUDA_NUM_THREADS,
0, stream>>>(
num_kernels, data_im, data_offset, data_mask, height_im, width_im, kernel_h, kenerl_w,
@@ -137,7 +246,6 @@ void modulated_deformable_im2col_cuda(cudaStream_t stream,
}
void dcn_v2_cuda_forward(cublasStatus_t stat, cublasHandle_t handle,
float *input, float *weight,
float *bias, float *ones,
@@ -182,9 +290,14 @@ void dcn_v2_cuda_forward(cublasStatus_t stat, cublasHandle_t handle,
input, offset,
mask,
1, channels, height, width,
height_out, width_out, kernel_h, kernel_w,
pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
deformable_group, columns);
height_out, width_out, deformable_group, columns);
// modulated_deformable_im2col_cuda2(stream,
// input, offset,
// mask,
// 1, channels, height, width,
// height_out, width_out, kernel_h, kernel_w,
// pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
// deformable_group, columns);
//(k * m) x (m * n)
// Y = WC