diff options
Diffstat (limited to 'src/cpu/kernels/directconv2d/nchw/all.cpp')
| -rw-r--r-- | src/cpu/kernels/directconv2d/nchw/all.cpp | 142 |
1 files changed, 77 insertions, 65 deletions
diff --git a/src/cpu/kernels/directconv2d/nchw/all.cpp b/src/cpu/kernels/directconv2d/nchw/all.cpp index a719fa50d6..218a4b7ee4 100644 --- a/src/cpu/kernels/directconv2d/nchw/all.cpp +++ b/src/cpu/kernels/directconv2d/nchw/all.cpp @@ -22,18 +22,17 @@ * SOFTWARE. */ -#include "src/cpu/kernels/directconv2d/nhwc/neon/impl.h" - -#include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" -#include "src/core/NEON/wrapper/wrapper.h" - #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/IAccessWindow.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" + #include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/kernels/directconv2d/nhwc/neon/impl.h" #include <algorithm> @@ -44,22 +43,26 @@ namespace cpu namespace kernels { template <typename T> -void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); +void convolve_nchw( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) -void neon_fp16_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +void neon_fp16_nchw_directconv2d( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) { convolve_nchw<float16_t>(window, src, weights, dst, conv_info); } #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -void neon_fp32_nchw_directconv2d(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +void neon_fp32_nchw_directconv2d( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) { convolve_nchw<float>(window, src, weights, dst, conv_info); } template <typename T> -void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) +void convolve_nchw( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info) { ARM_COMPUTE_UNUSED(conv_info); @@ -107,72 +110,81 @@ void convolve_nchw(const Window &window, const ITensor *src, const ITensor *weig constexpr int num_elems_read_per_iteration = 16 / sizeof(T); - execute_window_loop(window_out, [&](const Coordinates & id) - { - // We are computing the theoretical starting input starting points - const int in_w_start_t = static_cast<int>(id.x()) * conv_stride_w - conv_pad_left; - const int in_h_start_t = static_cast<int>(id.y()) * conv_stride_h - conv_pad_top; - const int in_w_end_t = in_w_start_t + kernel_dim_w; - const int in_h_end_t = in_h_start_t + kernel_dim_h; - - // We are computing the valid initial and ending input points by checking the borders - const int in_w_start = std::max(in_w_start_t, 0); - const int in_h_start = std::max(in_h_start_t, 0); - const int in_w_end = std::min(in_w_end_t, input_dim_w); - const int in_h_end = std::min(in_h_end_t, input_dim_h); - - // We use the input points to select the valid weight points to use - const int wei_w_start = in_w_start - in_w_start_t; - const int wei_h_start = in_h_start - in_h_start_t; - const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); - - const int index_c_end = weights->info()->dimension(2); - const T *const in_ptr_start = reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + id[3] * input_stride_n; - execute_window_loop(window_w, [&](const Coordinates & id_w) + execute_window_loop( + window_out, + [&](const Coordinates &id) { - const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); - uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; - T out_temp = static_cast<T>(0); - - for(int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c) - { - const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c; - const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c; - for(int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; ++index_wei_h, ++index_in_h) + // We are computing the theoretical starting input starting points + const int in_w_start_t = static_cast<int>(id.x()) * conv_stride_w - conv_pad_left; + const int in_h_start_t = static_cast<int>(id.y()) * conv_stride_h - conv_pad_top; + const int in_w_end_t = in_w_start_t + kernel_dim_w; + const int in_h_end_t = in_h_start_t + kernel_dim_h; + + // We are computing the valid initial and ending input points by checking the borders + const int in_w_start = std::max(in_w_start_t, 0); + const int in_h_start = std::max(in_h_start_t, 0); + const int in_w_end = std::min(in_w_end_t, input_dim_w); + const int in_h_end = std::min(in_h_end_t, input_dim_h); + + // We use the input points to select the valid weight points to use + const int wei_w_start = in_w_start - in_w_start_t; + const int wei_h_start = in_h_start - in_h_start_t; + const int wei_h_end = kernel_dim_h - (in_h_end_t - in_h_end); + + const int index_c_end = weights->info()->dimension(2); + const T *const in_ptr_start = + reinterpret_cast<const T *>(src->buffer() + src->info()->offset_first_element_in_bytes()) + + id[3] * input_stride_n; + execute_window_loop( + window_w, + [&](const Coordinates &id_w) { - const T *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h; - const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h; - int index_w = in_w_start; - int index_wei_w = wei_w_start; - vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); - for(; index_w <= ((in_w_end - num_elems_read_per_iteration)); index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration) - { - const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w); - const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w); - out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); - } - out_temp += vreduce(out_temp_vec); - for(; index_w < in_w_end; ++index_w, ++index_wei_w) + const T *const weights_ptr_start = reinterpret_cast<const T *>(wei.ptr()); + uint8_t *out_ptr = out.ptr() + id_w[3] * output_stride_c; + T out_temp = static_cast<T>(0); + + for (int index_wei_c = 0, index_in_c = 0; index_wei_c < index_c_end; ++index_wei_c, ++index_in_c) { - const auto src_val = *(in_ptr_row + index_w * input_stride_w); - const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w); - out_temp += src_val * w_val; + const T *const in_ptr_row_0 = in_ptr_start + index_in_c * input_stride_c; + const T *const weights_ptr_row_0 = weights_ptr_start + index_wei_c * kernel_stride_c; + for (int index_wei_h = wei_h_start, index_in_h = in_h_start; index_wei_h < wei_h_end; + ++index_wei_h, ++index_in_h) + { + const T *in_ptr_row = in_ptr_row_0 + index_in_h * input_stride_h; + const T *weights_ptr_row = weights_ptr_row_0 + index_wei_h * kernel_stride_h; + int index_w = in_w_start; + int index_wei_w = wei_w_start; + vector_type out_temp_vec = wrapper::vdup_n(static_cast<T>(0), tag_type()); + for (; index_w <= ((in_w_end - num_elems_read_per_iteration)); + index_w += num_elems_read_per_iteration, index_wei_w += num_elems_read_per_iteration) + { + const auto src_vec = wrapper::vloadq(in_ptr_row + index_w * input_stride_w); + const auto w_vec = wrapper::vloadq(weights_ptr_row + index_wei_w * kernel_stride_w); + out_temp_vec = wrapper::vmla(out_temp_vec, w_vec, src_vec); + } + out_temp += vreduce(out_temp_vec); + for (; index_w < in_w_end; ++index_w, ++index_wei_w) + { + const auto src_val = *(in_ptr_row + index_w * input_stride_w); + const auto w_val = *(weights_ptr_row + index_wei_w * kernel_stride_w); + out_temp += src_val * w_val; + } + } } - } - } - *(reinterpret_cast<T *>(out_ptr)) = out_temp; - + *(reinterpret_cast<T *>(out_ptr)) = out_temp; + }, + wei); }, - wei); - }, - out); + out); } #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -template void convolve_nchw<float16_t>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); +template void convolve_nchw<float16_t>( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ -template void convolve_nchw<float>(const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); +template void convolve_nchw<float>( + const Window &window, const ITensor *src, const ITensor *weights, ITensor *dst, const PadStrideInfo &conv_info); } // namespace kernels } // namespace cpu |