diff options
Diffstat (limited to 'src/cpu/kernels/CpuIm2ColKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuIm2ColKernel.cpp | 288 |
1 files changed, 149 insertions, 139 deletions
diff --git a/src/cpu/kernels/CpuIm2ColKernel.cpp b/src/cpu/kernels/CpuIm2ColKernel.cpp index 9ac291549b..55ac7c5192 100644 --- a/src/cpu/kernels/CpuIm2ColKernel.cpp +++ b/src/cpu/kernels/CpuIm2ColKernel.cpp @@ -29,13 +29,13 @@ #include "arm_compute/core/Size2D.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/Validate.h" + #include "src/core/CPP/Validate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" - #include <arm_neon.h> #include <cstddef> #include <cstdint> @@ -51,26 +51,34 @@ namespace kernels { namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups, unsigned int input_pad_right) +Status validate_arguments(const ITensorInfo *input, + const ITensorInfo *output, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups, + unsigned int input_pad_right) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, + DataType::BFLOAT16, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(input->data_type()) && has_bias); ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups > 1, "Number of groups greater than one are not supported on Neon"); // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions - const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); - const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); + const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + const unsigned total_width = input->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); const unsigned total_height = input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom(); ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height)); - if(output->total_size() > 0) + if (output->total_size() > 0) { - TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false, num_groups, input_pad_right)); + TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape( + input, kernel_dims, conv_info, has_bias, dilation, false, num_groups, input_pad_right)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); @@ -106,14 +114,14 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, // This for loop linearize a volume with 3 slices. This allows: // 1) to reduce the iterations of the outer for loop "d" // 2) to have an optimized im2col for the first convolution layer where usually we have 3 IFMs - for(; d <= (kernel_depth - 3); d += 3) + for (; d <= (kernel_depth - 3); d += 3) { - for(int y = top_left_y; y < y_e; y += dilation_y) + for (int y = top_left_y; y < y_e; y += dilation_y) { - if((y < 0 || y >= input_h) && has_pads) + if ((y < 0 || y >= input_h) && has_pads) { // All the values will be the offset (will be zeros when not quantized) - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) { *(out_ptr + 0 * kernel_size2) = pad_value; *(out_ptr + 1 * kernel_size2) = pad_value; @@ -122,9 +130,9 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, } else { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) { - if((x < 0 || x >= input_w) && has_pads) + if ((x < 0 || x >= input_w) && has_pads) { *(out_ptr + 0 * kernel_size2) = pad_value; *(out_ptr + 1 * kernel_size2) = pad_value; @@ -132,9 +140,12 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, } else { - *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); - *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>(in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 0 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 0) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 1 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 1) * input_stride_z + y * input_stride_y + x * input_stride_x))); + *(out_ptr + 2 * kernel_size2) = *(reinterpret_cast<const T *>( + in_ptr + ((d + 2) * input_stride_z + y * input_stride_y + x * input_stride_x))); } } } @@ -143,11 +154,11 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, } // Left over - for(; d < kernel_depth; d++) + for (; d < kernel_depth; d++) { - for(int y = top_left_y; y < y_e; y += dilation_y) + for (int y = top_left_y; y < y_e; y += dilation_y) { - if((y < 0 || y >= input_h) && has_pads) + if ((y < 0 || y >= input_h) && has_pads) { // All the values will be the offset (will be zeros when not quantized) memset(static_cast<void *>(out_ptr), pad_value, kernel_width * sizeof(T)); @@ -155,15 +166,16 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, } else { - for(int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) + for (int x = top_left_x; x < x_e; x += dilation_x, ++out_ptr) { - if((x < 0 || x >= input_w) && has_pads) + if ((x < 0 || x >= input_w) && has_pads) { *out_ptr = pad_value; } else { - *out_ptr = *(reinterpret_cast<const T *>(in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); + *out_ptr = *(reinterpret_cast<const T *>( + in_ptr + (d * input_stride_z + y * input_stride_y + x * input_stride_x))); } } } @@ -171,7 +183,7 @@ inline void linearize_volume_nchw(const uint8_t *const in_ptr, } // Append 1 if the convolution layer has biases - if(has_bias) + if (has_bias) { *out_ptr = static_cast<T>(1); } @@ -198,36 +210,39 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, const int end_y = start_y + kernel_height * dilation_y; const int pad_quant = kernel_width * input_c; const int element_size = static_cast<int>(sizeof(T)); - if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == input_c * element_size)) + if ((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && + (input_stride_y == input_c * element_size)) { - for(int y = start_y; y < end_y; y += dilation_y) + for (int y = start_y; y < end_y; y += dilation_y) { //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 * element_size); + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), + input_c * kernel_width * element_size); out_ptr += input_c * kernel_width; } } else { - for(int y = start_y; y < end_y; y += dilation_y) + for (int y = start_y; y < end_y; y += dilation_y) { - if(y < 0 || y >= input_h) + if (y < 0 || y >= input_h) { memset(static_cast<void *>(out_ptr), pad_value, pad_quant * element_size); out_ptr += pad_quant; } - else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) + else if (dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != input_c * element_size) { - for(int x = start_x; x < end_x; x += dilation_x) + for (int x = start_x; x < end_x; x += dilation_x) { - if(x < 0 || x >= input_w) + if (x < 0 || x >= input_w) { memset(static_cast<void *>(out_ptr), pad_value, input_c * element_size); out_ptr += input_c; } else { - memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), input_c * element_size); + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), + input_c * element_size); out_ptr += input_c; } } @@ -235,13 +250,14 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, 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 * element_size); + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + start_x * input_stride_y)), + input_c * kernel_width * element_size); out_ptr += input_c * kernel_width; } } } // Append 1 if the convolution layer has biases - if(has_bias) + if (has_bias) { *out_ptr = static_cast<T>(1); } @@ -271,12 +287,13 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, const int element_size = static_cast<int>(sizeof(T)); const int channel_chunk_size = input_c * element_size; - if((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && (input_stride_y == channel_chunk_size)) + if ((start_y >= 0) && (end_y < input_h) && (start_x >= 0) && (end_x < input_w) && (dilation_x == 1) && + (input_stride_y == channel_chunk_size)) { - for(int y = start_y; y < end_y; y += dilation_y) + for (int y = start_y; y < end_y; y += dilation_y) { const uint8_t *offset_ptr = in_ptr + (y * input_stride_z + start_x * input_stride_y); - for(int e = 0; e < kernel_width; e++) + for (int e = 0; e < kernel_width; e++) { memcpy(out_ptr, reinterpret_cast<const T *>(offset_ptr + e * channel_chunk_size), channel_chunk_size); out_ptr += input_c + pad_right; @@ -285,25 +302,26 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, } else { - for(int y = start_y; y < end_y; y += dilation_y) + for (int y = start_y; y < end_y; y += dilation_y) { - if(y < 0 || y >= input_h) + if (y < 0 || y >= input_h) { memset(static_cast<void *>(out_ptr), pad_value, pad_quant * element_size); out_ptr += pad_quant; } - else if(dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != channel_chunk_size) + else if (dilation_x > 1 || start_x < 0 || end_x >= input_w || input_stride_y != channel_chunk_size) { - for(int x = start_x; x < end_x; x += dilation_x) + for (int x = start_x; x < end_x; x += dilation_x) { - if(x < 0 || x >= input_w) + if (x < 0 || x >= input_w) { memset(static_cast<void *>(out_ptr), pad_value, (input_c + pad_right) * element_size); out_ptr += input_c + pad_right; } else { - memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), channel_chunk_size); + memcpy(out_ptr, reinterpret_cast<const T *>(in_ptr + (y * input_stride_z + x * input_stride_y)), + channel_chunk_size); out_ptr += input_c + pad_right; } } @@ -311,16 +329,17 @@ inline void linearize_volume_nhwc(const uint8_t *const in_ptr, else { const uint8_t *offset_ptr = in_ptr + (y * input_stride_z + start_x * input_stride_y); - for(int e = 0; e < kernel_width; e++) + for (int e = 0; e < kernel_width; e++) { - memcpy(out_ptr, reinterpret_cast<const T *>(offset_ptr + e * channel_chunk_size), channel_chunk_size); + memcpy(out_ptr, reinterpret_cast<const T *>(offset_ptr + e * channel_chunk_size), + channel_chunk_size); out_ptr += input_c + pad_right; } } } } // Append 1 if the convolution layer has biases - if(has_bias) + if (has_bias) { *out_ptr = static_cast<T>(1); } @@ -348,7 +367,8 @@ void CpuIm2ColKernel::run_im2col(const ITensor *src, ITensor *dst, const Window const int pad_top = _conv_info.pad_top(); const int stride_x = _conv_info.stride().first; const int stride_y = _conv_info.stride().second; - const int pad_value = is_data_type_quantized(src->info()->data_type()) ? src->info()->quantization_info().uniform().offset : 0; + const int pad_value = + is_data_type_quantized(src->info()->data_type()) ? src->info()->quantization_info().uniform().offset : 0; Window window_in_out(window); // The first three dimensions of the input and output are increased by the inner loops @@ -361,84 +381,57 @@ void CpuIm2ColKernel::run_im2col(const ITensor *src, ITensor *dst, const Window Iterator out(dst, window_in_out); execute_window_loop( - window, [&](const Coordinates & id) - { - const int start_w = id[width_idx] * stride_x - pad_left; - const int start_h = id[height_idx] * stride_y - pad_top; + window, + [&](const Coordinates &id) + { + const int start_w = id[width_idx] * stride_x - pad_left; + const int start_h = id[height_idx] * stride_y - pad_top; - // Get pointers - const uint8_t *const input_ptr = in.ptr(); - auto output_ptr = reinterpret_cast<T *>(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * dst->info()->strides_in_bytes().y()); + // Get pointers + const uint8_t *const input_ptr = in.ptr(); + auto output_ptr = + reinterpret_cast<T *>(out.ptr() + (id[width_idx] + id[height_idx] * _convolved_dims.first) * + dst->info()->strides_in_bytes().y()); - // Linearize volume - if(is_nchw) - { - linearize_volume_nchw<T, has_pads>(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_c, - input_w, - input_h, - input_stride_x, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); - } - else - { - if(_input_pad_right > 0) + // Linearize volume + if (is_nchw) { - linearize_volume_nhwc<T, has_pads>(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_w, - input_h, - input_c, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y(), - _input_pad_right); + linearize_volume_nchw<T, has_pads>( + input_ptr, output_ptr, _has_bias, start_w, start_h, _kernel_width, _kernel_height, input_c, input_w, + input_h, input_stride_x, input_stride_y, input_stride_z, pad_value, _dilation.x(), _dilation.y()); } else { - linearize_volume_nhwc<T, has_pads>(input_ptr, - output_ptr, - _has_bias, - start_w, - start_h, - _kernel_width, - _kernel_height, - input_w, - input_h, - input_c, - input_stride_y, - input_stride_z, - pad_value, - _dilation.x(), - _dilation.y()); + if (_input_pad_right > 0) + { + linearize_volume_nhwc<T, has_pads>(input_ptr, output_ptr, _has_bias, start_w, start_h, + _kernel_width, _kernel_height, input_w, input_h, input_c, + input_stride_y, input_stride_z, pad_value, _dilation.x(), + _dilation.y(), _input_pad_right); + } + else + { + linearize_volume_nhwc<T, has_pads>( + input_ptr, output_ptr, _has_bias, start_w, start_h, _kernel_width, _kernel_height, input_w, + input_h, input_c, input_stride_y, input_stride_z, pad_value, _dilation.x(), _dilation.y()); + } } - } - }, - in, out); + }, + in, out); } -void CpuIm2ColKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups, unsigned int input_pad_right) +void CpuIm2ColKernel::configure(const ITensorInfo *src, + ITensorInfo *dst, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups, + unsigned int input_pad_right) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups, input_pad_right)); + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups, input_pad_right)); ARM_COMPUTE_UNUSED(num_groups); _data_layout = src->data_layout(); @@ -451,31 +444,34 @@ void CpuIm2ColKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const _kernel_height = kernel_dims.height; _input_pad_right = input_pad_right; _dilation = dilation; - _convolved_dims = scaled_dimensions(src->dimension(width_idx), dst->dimension(height_idx), - _kernel_width, _kernel_height, - _conv_info, _dilation); + _convolved_dims = scaled_dimensions(src->dimension(width_idx), dst->dimension(height_idx), _kernel_width, + _kernel_height, _conv_info, _dilation); _has_bias = has_bias; - if(_data_layout == DataLayout::NCHW) + if (_data_layout == DataLayout::NCHW) { - switch(src->data_type()) + switch (src->data_type()) { case DataType::F32: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float, false, true> : &CpuIm2ColKernel::run_im2col<float, true, true>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float, false, true> + : &CpuIm2ColKernel::run_im2col<float, true, true>; break; #if defined(ARM_COMPUTE_ENABLE_BF16) case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<bfloat16, false, true> : &CpuIm2ColKernel::run_im2col<bfloat16, true, true>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<bfloat16, false, true> + : &CpuIm2ColKernel::run_im2col<bfloat16, true, true>; break; #endif /* defined(ARM_COMPUTE_ENABLE_BF16) */ #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float16_t, false, true> : &CpuIm2ColKernel::run_im2col<float16_t, true, true>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float16_t, false, true> + : &CpuIm2ColKernel::run_im2col<float16_t, true, true>; break; #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ case DataType::QASYMM8_SIGNED: case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<qasymm8_t, false, true> : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, true>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<qasymm8_t, false, true> + : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, true>; break; default: ARM_COMPUTE_ERROR("Data type not supported"); @@ -484,26 +480,31 @@ void CpuIm2ColKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const } else { - switch(src->data_type()) + switch (src->data_type()) { case DataType::F32: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float, false, false> : &CpuIm2ColKernel::run_im2col<float, true, false>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float, false, false> + : &CpuIm2ColKernel::run_im2col<float, true, false>; break; #if defined(ARM_COMPUTE_ENABLE_BF16) case DataType::BFLOAT16: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<bfloat16, false, false> : &CpuIm2ColKernel::run_im2col<bfloat16, true, false>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<bfloat16, false, false> + : &CpuIm2ColKernel::run_im2col<bfloat16, true, false>; break; #endif /* defined(ARM_COMPUTE_ENABLE_BF16) */ #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float16_t, false, false> : &CpuIm2ColKernel::run_im2col<float16_t, true, false>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<float16_t, false, false> + : &CpuIm2ColKernel::run_im2col<float16_t, true, false>; break; #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ case DataType::QASYMM8: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<uint8_t, false, false> : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, false>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<uint8_t, false, false> + : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, false>; break; case DataType::QASYMM8_SIGNED: - _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<int8_t, false, false> : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, false>; + _func = (!conv_info.has_padding()) ? &CpuIm2ColKernel::run_im2col<int8_t, false, false> + : &CpuIm2ColKernel::run_im2col<qasymm8_t, true, false>; break; default: ARM_COMPUTE_ERROR("Data type not supported"); @@ -512,11 +513,13 @@ void CpuIm2ColKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const } // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, false, num_groups, _input_pad_right))); + auto_init_if_empty( + *dst, src->clone()->set_tensor_shape(compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, + false, num_groups, _input_pad_right))); - std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(src->dimension(width_idx), src->dimension(height_idx), - kernel_dims.width, kernel_dims.height, - conv_info, dilation); + std::pair<unsigned int, unsigned int> convolved_dims = + scaled_dimensions(src->dimension(width_idx), src->dimension(height_idx), kernel_dims.width, kernel_dims.height, + conv_info, dilation); Window win = calculate_max_window(*src, Steps()); win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1)); @@ -526,10 +529,17 @@ void CpuIm2ColKernel::configure(const ITensorInfo *src, ITensorInfo *dst, const ICpuKernel::configure(win); } -Status CpuIm2ColKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, - bool has_bias, const Size2D &dilation, unsigned int num_groups, unsigned int input_pad_right) +Status CpuIm2ColKernel::validate(const ITensorInfo *src, + const ITensorInfo *dst, + const Size2D &kernel_dims, + const PadStrideInfo &conv_info, + bool has_bias, + const Size2D &dilation, + unsigned int num_groups, + unsigned int input_pad_right) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups, input_pad_right)); + ARM_COMPUTE_RETURN_ON_ERROR( + validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups, input_pad_right)); return Status{}; } |