| /*! |
| * Copyright (c) 2016 by Contributors |
| * \file spatial_transformer.cu |
| * \brief |
| * \author Wei Wu |
| */ |
| |
| #include "./spatial_transformer-inl.h" |
| #include <algorithm> |
| #if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5 |
| #include "./cudnn_spatial_transformer-inl.h" |
| #endif // MXNET_USE_CUDNN && CUDNN_MAJOR |
| |
| namespace mshadow { |
| template<typename DType> |
| __global__ void BilinearSamplingForwardKernel(const int i_c, const int i_h, |
| const int i_w, const DType* data, |
| const DType* grid, const int o_n, |
| const int o_c, const int o_h, |
| const int o_w, DType* out) { |
| for (int index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x; |
| index < o_n * o_c * o_h * o_w; |
| index += blockDim.x * gridDim.x * gridDim.y) { |
| // (n, c, h, w) is the element in out |
| int w = index % o_w; |
| int h = (index / o_w) % o_h; |
| int c = (index / o_w / o_h) % o_c; |
| int n = index / o_w / o_h / o_c; |
| index_t out_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w; |
| index_t grid_index = n * o_h * o_w * 2 + h * o_w + w; |
| DType y_real = (*(grid + grid_index + o_h * o_w) + 1) * (i_h - 1) / 2; |
| DType x_real = (*(grid + grid_index) + 1) * (i_w - 1) / 2; |
| index_t top_left_y = min(i_h, max(0, static_cast<int>(floor(y_real)))); |
| index_t top_left_x = min(i_w, max(0, static_cast<int>(floor(x_real)))); |
| DType top_left_y_w = 1.0 - (y_real - top_left_y); |
| DType top_left_x_w = 1.0 - (x_real - top_left_x); |
| index_t data_index = n * i_c * i_h * i_w + c * i_h * i_w + top_left_y * i_w + top_left_x; |
| DType top_left_v = *(data + data_index); |
| DType top_right_v = *(data + data_index + 1); |
| DType bottom_left_v = *(data + data_index + i_w); |
| DType bottom_right_v = *(data + data_index + i_w + 1); |
| *(out+out_index) = top_left_v * top_left_y_w * top_left_x_w + |
| top_right_v * top_left_y_w * (1.0 - top_left_x_w) + |
| bottom_left_v * (1.0 - top_left_y_w) * top_left_x_w + |
| bottom_right_v * (1.0 - top_left_y_w) * (1.0 - top_left_x_w); |
| } |
| } |
| |
| template<typename DType> |
| __global__ void BilinearSamplingBackwardKernel(const int i_c, const int i_h, |
| const int i_w, const DType* grad, |
| const DType* data, const int o_n, |
| const int o_c, const int o_h, |
| const int o_w, DType* g_input, |
| DType* grid_src) { |
| for (int index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x; |
| index < o_n * o_h * o_w; |
| index += blockDim.x * gridDim.x * gridDim.y) { |
| // (n, c, h, w) is the element in grad |
| int w = index % o_w; |
| int h = (index / o_w) % o_h; |
| int n = index / o_w / o_h; |
| DType top_left_y_gw = 0.0; |
| DType top_left_x_gw = 0.0; |
| index_t grid_src_index = n * o_h * o_w * 2 + h * o_w + w; |
| DType y_real = (*(grid_src + grid_src_index + o_h * o_w) + 1) * (i_h - 1) / 2; |
| DType x_real = (*(grid_src + grid_src_index) + 1) * (i_w - 1) / 2; |
| index_t top_left_y = min(i_h, max(0, static_cast<int>(floor(y_real)))); |
| index_t top_left_x = min(i_w, max(0, static_cast<int>(floor(x_real)))); |
| DType top_left_y_w = 1.0 - (y_real - top_left_y); |
| DType top_left_x_w = 1.0 - (x_real - top_left_x); |
| for (index_t c = 0; c < o_c; ++c) { |
| index_t grad_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w; |
| index_t data_index = n * i_c * i_h * i_w + c * i_h * i_w + top_left_y * i_w + top_left_x; |
| // calc 4 vertex value in input data |
| DType top_left_v = *(data + data_index); |
| DType top_right_v = *(data + data_index + 1); |
| DType bottom_left_v = *(data + data_index + i_w); |
| DType bottom_right_v = *(data + data_index + i_w + 1); |
| // calc input grad |
| *(g_input + data_index) += *(grad + grad_index) * top_left_y_w * top_left_x_w; |
| *(g_input + data_index + 1) += *(grad + grad_index) * top_left_y_w * (1.0 - top_left_x_w); |
| *(g_input + data_index+ i_w) += *(grad + grad_index) * (1.0 - top_left_y_w) * top_left_x_w; |
| *(g_input + data_index+ i_w + 1) += *(grad + grad_index) * (1.0 - top_left_y_w) * |
| (1.0 - top_left_x_w); |
| // calc weight grad of top_left_w, then multiple -1 is the grad of grid_src |
| top_left_y_gw -= *(grad + grad_index) * (top_right_v - bottom_right_v + |
| (top_left_v - top_right_v - bottom_left_v + bottom_right_v) * top_left_x_w); |
| top_left_x_gw -= *(grad + grad_index) * (bottom_left_v - bottom_right_v + (top_left_v - |
| top_right_v - bottom_left_v + bottom_right_v) * top_left_y_w); |
| } |
| // calc grid_src grad |
| *(grid_src + grid_src_index + o_h * o_w) = top_left_y_gw * (i_h - 1) / 2; |
| *(grid_src + grid_src_index) = top_left_x_gw * (i_w - 1) / 2; |
| } |
| } |
| |
| template<typename DType> |
| inline void BilinearSamplingForward(const Tensor<gpu, 4, DType> &output, |
| const Tensor<gpu, 4, DType> &input, |
| const Tensor<gpu, 3, DType> grid_src) { |
| DType *out = output.dptr_; |
| const DType *data = input.dptr_; |
| const DType *grid = grid_src.dptr_; |
| int o_n = output.size(0), o_c = output.size(1), o_h = output.size(2), o_w = output.size(3); |
| int i_c = input.size(1), i_h = input.size(2), i_w = input.size(3); |
| using namespace cuda; |
| const int max_block = (output.shape_.Size() + kMaxThreadsPerBlock - 1) / kMaxThreadsPerBlock; |
| dim3 num_blocks(kMaxGridDim, (max_block + kMaxGridDim - 1) / kMaxGridDim); |
| dim3 threads_per_block(kMaxThreadsPerBlock); |
| CheckLaunchParam(num_blocks, threads_per_block, "spatial transformer forward"); |
| cudaStream_t stream = Stream<gpu>::GetStream(output.stream_); |
| BilinearSamplingForwardKernel<DType> << <num_blocks, threads_per_block, 0, stream >> >( |
| i_c, i_h, i_w, data, grid, o_n, o_c, o_h, o_w, out); |
| } |
| |
| template<typename DType> |
| inline void BilinearSamplingBackward(const Tensor<gpu, 4, DType> &input_grad, |
| const Tensor<gpu, 3, DType> &grid_src_data, |
| const Tensor<gpu, 4, DType> &output_grad, |
| const Tensor<gpu, 4, DType> &input_data) { |
| DType *g_input = input_grad.dptr_; |
| DType *grid_src = grid_src_data.dptr_; |
| const DType *grad = output_grad.dptr_; |
| const DType *data = input_data.dptr_; |
| int o_n = output_grad.size(0), o_c = output_grad.size(1), |
| o_h = output_grad.size(2), o_w = output_grad.size(3); |
| int i_c = input_data.size(1), i_h = input_data.size(2), i_w = input_data.size(3); |
| using namespace cuda; |
| const int max_block = (output_grad.shape_.Size() / o_c + kMaxThreadsPerBlock - 1) |
| / kMaxThreadsPerBlock; |
| dim3 num_blocks(kMaxGridDim, (max_block + kMaxGridDim - 1) / kMaxGridDim); |
| dim3 threads_per_block(kMaxThreadsPerBlock); |
| CheckLaunchParam(num_blocks, threads_per_block, "spatial transformer backward"); |
| cudaStream_t stream = Stream<gpu>::GetStream(input_grad.stream_); |
| BilinearSamplingBackwardKernel<DType> << <num_blocks, threads_per_block, 0, stream >> >( |
| i_c, i_h, i_w, grad, data, o_n, o_c, o_h, o_w, g_input, grid_src); |
| } |
| |
| } // namespace mshadow |
| |
| namespace mxnet { |
| namespace op { |
| template<> |
| Operator* CreateOp<gpu>(SpatialTransformerParam param, int dtype) { |
| Operator *op = NULL; |
| #if MXNET_USE_CUDNN == 1 && CUDNN_MAJOR >= 5 |
| MSHADOW_REAL_TYPE_SWITCH(dtype, DType, { |
| op = new CuDNNSpatialTransformerOp<DType>(param); |
| }) |
| #else |
| MSHADOW_REAL_TYPE_SWITCH(dtype, DType, { |
| op = new SpatialTransformerOp<gpu, DType>(param); |
| }) |
| #endif // MXNET_USE_CUDNN && CUDNN_MAJOR |
| return op; |
| } |
| |
| } // namespace op |
| } // namespace mxnet |
