From ce093143ec7b554edefc533c90e45c80946cde51 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 19 Jun 2017 16:11:53 +0100 Subject: COMPMID-403:Add support for 7x7 pooling on CL. Change-Id: I3c2c8d7e8e61d7737170cb1568900ce4ac337068 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78181 Reviewed-by: Michele DiGiorgio Tested-by: Kaizen Reviewed-by: Moritz Pflanzer --- src/core/CL/CLKernelLibrary.cpp | 1 + src/core/CL/cl_kernels/pooling_layer.cl | 91 ++++++++++++++++++++++++++-- src/core/CL/kernels/CLPoolingLayerKernel.cpp | 23 ++++--- 3 files changed, 99 insertions(+), 16 deletions(-) (limited to 'src/core/CL') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 15a5d90835..3070d4817e 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -219,6 +219,7 @@ const std::map CLKernelLibrary::_kernel_program_map = { "pixelwise_mul_int", "pixelwise_mul_int.cl" }, { "pooling_layer_2", "pooling_layer.cl" }, { "pooling_layer_3", "pooling_layer.cl" }, + { "pooling_layer_7", "pooling_layer.cl" }, { "remap_nearest_neighbour", "remap.cl" }, { "remap_bilinear", "remap.cl" }, { "reshape_to_columns", "convolution_layer.cl" }, diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl index 1902df9b7d..6bdb174235 100644 --- a/src/core/CL/cl_kernels/pooling_layer.cl +++ b/src/core/CL/cl_kernels/pooling_layer.cl @@ -41,7 +41,6 @@ float calculate_avg_scale(const int pool_size, const int upper_bound_w, const in /** Performs a pooling function of pool size equal to 2. * - * @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3 * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed. * @@ -88,7 +87,7 @@ __kernel void pooling_layer_2( data0 = POOL_OP(data0, data1); DATA_TYPE res = POOL_OP(data0.s0, data0.s1); - // Divide by 4 in case of average pooling + // Divide by pool region in case of average pooling #ifdef POOL_AVG res *= calculate_avg_scale(2, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y); #endif @@ -99,7 +98,6 @@ __kernel void pooling_layer_2( /** Performs a pooling function of pool size equal to 3. * - * @note Pooling stride must be passed using -DPOOL_STRIDE e.g -DPOOL_STRIDE=2. Supported strides are 1,2,3 * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed. * @@ -149,7 +147,7 @@ __kernel void pooling_layer_3( data0 = POOL_OP(data0, data2); DATA_TYPE res = POOL_OP(POOL_OP(data0.s0, data0.s1), data0.s2); - // Divide by 4 in case of average pooling + // Divide by pool region in case of average pooling #ifdef POOL_AVG res *= calculate_avg_scale(3, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y); #endif @@ -157,3 +155,88 @@ __kernel void pooling_layer_3( // Store result *(__global DATA_TYPE *)output.ptr = res; } + +/** Performs a pooling function of pool size equal to 7. + * + * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16, F32; + * @note In case of average pooling -DPOOL_AVG must be provided otherwise max pooling will be performed. + * + * @param[in] input_ptr Pointer to the source image. Supported data types: F16, F32 + * @param[in] input_stride_x Stride of the source image in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image + * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32 + * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image + * @param[in] max_dims The maximum index that can be accessed in x and y dimension (width + pad) + * @param[in] strides The pooling operation strides in each dimension + * @param[in] paddings The pooling operation paddings in each dimension + */ +__kernel void pooling_layer_7( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(output) +#ifdef POOL_AVG + , + int2 max_dims, int2 strides, int2 paddings +#endif +) +{ + // Get pixels pointer + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + // Load data + VEC_DATA_TYPE(DATA_TYPE, 8) + data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data1 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data2 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data3 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 3, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data4 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 4, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data5 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 5, 0)); + VEC_DATA_TYPE(DATA_TYPE, 8) + data6 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 6, 0)); + + // Pool operation of all rows + data0 = POOL_OP(data0, data1); + data2 = POOL_OP(data2, data3); + data4 = POOL_OP(data4, data5); + data0 = POOL_OP(data0, data2); + data4 = POOL_OP(data4, data6); + data0 = POOL_OP(data0, data4); + + // Set last element +#ifdef POOL_AVG + data0.s7 = 0; +#else + data0.s7 = data0.s6; +#endif + + // Reduce result + VEC_DATA_TYPE(DATA_TYPE, 4) + reduce4 = POOL_OP(data0.s0123, data0.s4567); + VEC_DATA_TYPE(DATA_TYPE, 2) + reduce2 = POOL_OP(reduce4.s01, reduce4.s23); + DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1); + + // Divide by pool region in case of average pooling +#ifdef POOL_AVG + res *= calculate_avg_scale(7, max_dims.x, max_dims.y, paddings.x, paddings.y, strides.x, strides.y); +#endif + + // Store result + *(__global DATA_TYPE *)output.ptr = res; +} diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp index dc5ae4ec7a..7648025caa 100644 --- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp +++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp @@ -65,10 +65,13 @@ void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); + static const std::set supported_pool_sizes = { 2, 3, 7 }; + ARM_COMPUTE_UNUSED(supported_pool_sizes); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_ERROR_ON(2 != pool_size && 3 != pool_size); + ARM_COMPUTE_ERROR_ON(supported_pool_sizes.find(pool_size) == supported_pool_sizes.end()); ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size); // Check output dimensions @@ -82,10 +85,11 @@ void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, ARM_COMPUTE_UNUSED(pooled_h); ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); - const int input_width = input->info()->dimension(0); - const int input_height = input->info()->dimension(1); - const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width; - const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; + const int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size; + const int input_width = input->info()->dimension(0); + const int input_height = input->info()->dimension(1); + const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width; + const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; // Set instance variables _input = input; @@ -138,17 +142,12 @@ void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, } // Configure kernel window - const unsigned int num_elems_processed_per_iteration = 1; - - Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); - + const unsigned int num_elems_processed_per_iteration = 1; + Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); - update_window_and_padding(win, input_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); - ICLKernel::configure(win); } -- cgit v1.2.1