From e99b353d8bc2670e1f0a7aee1e9c6242192a99ae Mon Sep 17 00:00:00 2001 From: Davide Sapienza Date: Thu, 19 Dec 2019 14:47:57 +0100 Subject: [PATCH] Fix the inference operation of the deformable convolutional layer. This commit removes the malloc operation in the inference method and adds the sigmoid kernel. Signed-off-by: Davide Sapienza --- include/tkDNN/Layer.h | 5 + include/tkDNN/kernels.h | 1 + src/DeformConv2d.cpp | 156 +++++++++--------------------- src/kernels/activation_sigmoid.cu | 31 ++++++ src/kernels/deformable_conv.cu | 67 +++---------- 5 files changed, 97 insertions(+), 163 deletions(-) create mode 100644 src/kernels/activation_sigmoid.cu diff --git a/include/tkDNN/Layer.h b/include/tkDNN/Layer.h index 8c501f4..188cffb 100644 --- a/include/tkDNN/Layer.h +++ b/include/tkDNN/Layer.h @@ -219,7 +219,12 @@ public: int kernelH, kernelW, strideH, strideW, paddingH, paddingW; protected: + dnnType *ones_d1; + dnnType *ones_d2; cudnnTensorDescriptor_t biasTensorDesc; + int chunk_dim; + dnnType *offset, *mask; + dnnType *output_conv; void initCUDNN(); diff --git a/include/tkDNN/kernels.h b/include/tkDNN/kernels.h index dfff6e3..fa46efb 100644 --- a/include/tkDNN/kernels.h +++ b/include/tkDNN/kernels.h @@ -6,6 +6,7 @@ void activationELUForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream = cudaStream_t(0)); void activationLEAKYForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream = cudaStream_t(0)); void activationLOGISTICForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream = cudaStream_t(0)); +void activationSIGMOIDForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream = cudaStream_t(0)); void fill(dnnType* data, int size, dnnType val, cudaStream_t stream = cudaStream_t(0)); diff --git a/src/DeformConv2d.cpp b/src/DeformConv2d.cpp index f135b40..712419d 100644 --- a/src/DeformConv2d.cpp +++ b/src/DeformConv2d.cpp @@ -14,9 +14,36 @@ void DeformConv2d::initCUDNN() { net->tensorFormat, net->dataType, 1, output_dim.c, 1, 1) ); - checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc, + checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc, net->tensorFormat, net->dataType, output_dim.n, output_dim.c, output_dim.h, output_dim.w)); + const int height_ones = (preconv->input_dim.h + 2 * this->paddingH - (1 * (this->kernelH - 1) + 1)) / this->strideH + 1; + const int width_ones = (preconv->input_dim.w + 2 * this->paddingW - (1 * (this->kernelW - 1) + 1)) / this->strideW + 1; + const int dim_ones = preconv->input_dim.c * this->kernelH * this->kernelW * 1 * height_ones * width_ones; + + int dst_dim = preconv->output_dim.tot(); + if (dst_dim % 3 != 0 ) + std::cout<<"take attention\n\n"; + chunk_dim = dst_dim/3; + checkCuda(cudaMalloc(&offset, 2*chunk_dim*sizeof(dnnType))); + checkCuda(cudaMalloc(&mask, chunk_dim*sizeof(dnnType))); + + // kernel ones + + cudaMallocHost(&ones_d1, (height_ones*width_ones)*sizeof(dnnType)); + float aus1[height_ones*width_ones]; + for(int i=0; iinfer(dim, srcData); - dim = preconv->output_dim; - - //split to chank - dnnType *offset, *mask; - int dst_dim = dim.tot(); - if (dst_dim % 3 != 0 ) - std::cout<<"take attention\n\n"; - int chunk_dim = dst_dim/3; - checkCuda(cudaMalloc(&offset, 2*chunk_dim*sizeof(dnnType))); - checkCuda(cudaMalloc(&mask, chunk_dim*sizeof(dnnType))); - cudaDeviceSynchronize(); - - Conv2dToChunk(chunk_dim, srcData, offset, mask); + // conv2d + output_conv = preconv->infer(dim, srcData); + // split conv2d outputs into offset to mask + checkCuda(cudaMemcpy(offset, output_conv, 2*chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); + checkCuda(cudaMemcpy(mask, output_conv + 2*chunk_dim, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToDevice)); // kernel sigmoide - dnnType *vec; - vec = new dnnType[chunk_dim]; - cudaDeviceSynchronize(); - cudaMemcpy(vec, mask, chunk_dim*sizeof(dnnType), cudaMemcpyDeviceToHost); - cudaDeviceSynchronize(); - for(int i=0; iinput_dim.h + 2 * this->paddingH - (1 * (this->kernelH - 1) + 1)) / this->strideH + 1; - const int width_ones = (preconv->input_dim.w + 2 * this->paddingW - (1 * (this->kernelW - 1) + 1)) / this->strideW + 1; - const int dim_ones = preconv->input_dim.c * this->kernelH * this->kernelW * 1 * height_ones * width_ones; - - // kernel ones - dnnType *ones_d1; - cudaMallocHost(&ones_d1, (height_ones*width_ones)*sizeof(dnnType)); - float aus1[height_ones*width_ones]; - for(int i=0; idata_d, + // deformable convolution + dcn_v2_cuda_forward(srcData, this->data_d, this->bias2_d, ones_d1, offset, mask, dstData, ones_d2, @@ -148,44 +107,18 @@ dnnType* DeformConv2d::infer(dataDim_t &dim, dnnType* srcData) { this->deformableGroup, preconv->input_dim.n, preconv->input_dim.c, preconv->input_dim.h, preconv->input_dim.w, this->output_dim.n, this->output_dim.c, this->output_dim.h, this->output_dim.w, - dst_dim); - - cudaFree(offset); - cudaFree(mask); - cudaFree(input); - cudaFreeHost(ones_d1); - cudaFreeHost(ones_d2); - - - // dnnType *aus3; - // cudaMallocHost(&aus3, 256*7*7*sizeof(dnnType)); - // cudaMemcpy(aus3, dstData, (256*7*7)*sizeof(dnnType), cudaMemcpyDeviceToHost); - // checkCuda(cudaDeviceSynchronize()); - // std::cout<<"OutDim:\n"; - // this->output_dim.print(); - // std::cout<<"\n\n\nprint dstData: \n"; - // for (int i = 0 ; i < 256*7*7; i++){ - // if(i==294) - // std::cout<<"\n\n\n"; - // std::cout<cudnnHandle, &alpha, biasTensorDesc, bias_d, &beta, dstTensorDesc, dstData) ); } else { - std::cout<<"LOL\n"; alpha = dnnType(1); beta = dnnType(0); checkCUDNN( cudnnBatchNormalizationForwardInference(net->cudnnHandle, @@ -195,9 +128,8 @@ dnnType* DeformConv2d::infer(dataDim_t &dim, dnnType* srcData) { scales_d, bias_d, mean_d, variance_d, CUDNN_BN_MIN_EPSILON) ); } + //update data dimensions - std::cout<<"dstData BN:\n"; - printDeviceVector(64, dstData); dim = output_dim; return dstData; } diff --git a/src/kernels/activation_sigmoid.cu b/src/kernels/activation_sigmoid.cu new file mode 100644 index 0000000..ba6c997 --- /dev/null +++ b/src/kernels/activation_sigmoid.cu @@ -0,0 +1,31 @@ +#include "kernels.h" + +__device__ +__forceinline__ +double sigmoid (double a) +{ + return 1.0 / (1.0 + exp (-a)); +} + + +__global__ +void activation_sigmoid(dnnType *input, dnnType *output, int size) { + + int stride = gridDim.x * blockDim.x; + int tid = blockDim.x * blockIdx.x + threadIdx.x; + for (int i = tid; i < size; i += stride) { + output[i] = sigmoid (input[i]); + } + } + + +/** + ELU activation function +*/ +void activationSIGMOIDForward(dnnType* srcData, dnnType* dstData, int size, cudaStream_t stream) +{ + int blocks = (size+255)/256; + int threads = 256; + + activation_sigmoid<<>>(srcData, dstData, size); +} \ No newline at end of file diff --git a/src/kernels/deformable_conv.cu b/src/kernels/deformable_conv.cu index 71efb0d..cb9ded0 100644 --- a/src/kernels/deformable_conv.cu +++ b/src/kernels/deformable_conv.cu @@ -149,10 +149,8 @@ void dcn_v2_cuda_forward(float *input, float *weight, const int deformable_group, const int in_n, const int in_c, const int in_h, const int in_w, const int out_n, const int out_c, const int out_h, const int out_w, - const int dst_dim, cudaStream_t stream) -{ - checkCuda(cudaDeviceSynchronize()); - cudaError_t cudaStat; + const int chunk_dim, cudaStream_t stream) +{ cublasStatus_t stat; cublasHandle_t handle; stat = cublasCreate(&handle); @@ -160,83 +158,50 @@ void dcn_v2_cuda_forward(float *input, float *weight, printf ("CUBLAS initialization failed\n"); return; } - checkCuda(cudaDeviceSynchronize()); - - const int batch = in_n; + const int channels = in_c; const int height = in_h; const int width = in_w; + const int channels_out = out_c; - const int channels_kernel = in_c; - const int kernel_h_ = kernel_h; - const int kernel_w_ = kernel_w; const int height_out = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; const int width_out = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - float *input_n; - cudaMalloc(&input_n, (in_n*in_c*in_h*in_w)*sizeof(float)); - cudaMemcpy(input_n, input, (in_n*in_c*in_h*in_w)*sizeof(float), cudaMemcpyDeviceToDevice); - checkCuda(cudaDeviceSynchronize()); - - float *offset_n; - cudaMalloc(&offset_n, ((dst_dim/3)*2)*sizeof(float)); - cudaMemcpy(offset_n, offset, ((dst_dim/3)*2)*sizeof(float), cudaMemcpyDeviceToDevice); - checkCuda(cudaDeviceSynchronize()); - - float *mask_n; - cudaMalloc(&mask_n, (dst_dim/3)*sizeof(float)); - cudaMemcpy(mask_n, mask, (dst_dim/3)*sizeof(float), cudaMemcpyDeviceToDevice); - checkCuda(cudaDeviceSynchronize()); - - float *output_n; - checkCuda(cudaMalloc(&output_n, (channels_out*height_out*width_out)*sizeof(float))); - checkCuda(cudaDeviceSynchronize()); - long m_ = channels_out; - long n_ = height_out * width_out; - long k_ = 1; + long m = channels_out; + long n = height_out * width_out; + long k = 1; float alpha = 1.0; float beta = 0.0; - checkCuda(cudaDeviceSynchronize()); + stat = cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, - n_, m_, k_, &alpha, - ones, k_, bias, k_, - &beta, output_n, n_); + n, m, k, &alpha, + ones, k, bias, k, + &beta, output, n); if (stat != CUBLAS_STATUS_SUCCESS) { printf ("CUBLAS initialization failed\n"); return ; } - checkCuda(cudaDeviceSynchronize()); - modulated_deformable_im2col_cuda(stream, - input_n, offset_n, - mask_n, + 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); - checkCuda(cudaDeviceSynchronize()); - + //(k * m) x (m * n) // Y = WC - long m = channels_out; - long n = height_out * width_out; - long k = channels * kernel_h * kernel_w; - - alpha = 1.0; + k = channels * kernel_h * kernel_w; beta = 1.0; stat = cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, n, m, k, &alpha, columns, n, weight, k, - &beta, output_n, n); + &beta, output, n); - cudaMemcpy(output, output_n, (n*m)*sizeof(float), cudaMemcpyDeviceToDevice); - checkCuda(cudaDeviceSynchronize()); - if (stat != CUBLAS_STATUS_SUCCESS) { printf ("CUBLAS initialization failed\n"); return ;