diff options
| author | Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com> | 2019-04-02 09:40:08 +0100 |
|---|---|---|
| committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-04-03 11:46:24 +0000 |
| commit | 642680abde9d9021398695b495f9da63f4688d76 (patch) | |
| tree | aac5aeb654b118ed6992e574a1f12ea997904578 | |
| parent | 3bfacb24a9b6eced921027fd1c1e3cb3757db9c7 (diff) | |
| download | ComputeLibrary-642680abde9d9021398695b495f9da63f4688d76.tar.gz | |
COMPMID-1983 : Optimize NEIm2Col with NHWC data layout
Improves NEIm2Col performance by ~45% for single threaded run.
Change-Id: I6cfb9e3af51ab0756e880f0e0f82cad4c644702c
Signed-off-by: Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com>
Reviewed-on: https://review.mlplatform.org/c/920
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
| -rw-r--r-- | src/core/NEON/kernels/NEIm2ColKernel.cpp | 48 |
1 files changed, 32 insertions, 16 deletions
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp index 2e3d9de5f1..34af0cf3fd 100644 --- a/src/core/NEON/kernels/NEIm2ColKernel.cpp +++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp @@ -208,32 +208,48 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, const int end_x = start_x + kernel_width * dilation_x; const int end_y = start_y + kernel_height * dilation_y; const int pad_quant = kernel_width * input_c; - - for(int y = start_y; y < end_y; y += dilation_y) + if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1)) { - if(y < 0 || y >= input_h) + for(int y = start_y; y < end_y; y += dilation_y) { - memset(out_ptr, pad_value, pad_quant * sizeof(T)); - out_ptr += pad_quant; + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * sizeof(T)); + out_ptr += input_c * kernel_width; } - else + } + else + { + for(int y = start_y; y < end_y; y += dilation_y) { - for(int x = start_x; x < end_x; x += dilation_x) + if(y < 0 || y >= input_h) { - if(x < 0 || x >= input_w) - { - memset(out_ptr, pad_value, input_c * sizeof(T)); - out_ptr += input_c; - } - else + memset(out_ptr, pad_value, pad_quant * sizeof(T)); + out_ptr += pad_quant; + } + else if(dilation_x > 1 || start_x < 0 || end_x >= input_w) + { + for(int x = start_x; x < end_x; x += dilation_x) { - memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * sizeof(T)); - out_ptr += input_c; + if(x < 0 || x >= input_w) + { + memset(out_ptr, pad_value, input_c * sizeof(T)); + out_ptr += input_c; + } + else + { + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * sizeof(T)); + out_ptr += input_c; + } } } + else + { + //optimized for no dilation and no boundary pixels + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), input_c * kernel_width * sizeof(T)); + out_ptr += input_c * kernel_width; + } } } - // Append 1 if the convolution layer has biases if(has_bias) { |