diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-09-26 11:25:40 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:55:45 +0000 |
commit | c93691717a6e7ca67e32b4dedd233b8c63b6daf2 (patch) | |
tree | d3929606b525e89f60299b16f95eb4223d11d5a8 /src/core | |
parent | e6dbde0128bf33b5d72a00c480bd92c290fd17b7 (diff) | |
download | ComputeLibrary-c93691717a6e7ca67e32b4dedd233b8c63b6daf2.tar.gz |
COMPMID-1523: Fuse BN node with convolution.
Change-Id: I146936c9e98b343496a4b61cdbadf0eaa38e885a
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/154008
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
Diffstat (limited to 'src/core')
-rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 1 | ||||
-rw-r--r-- | src/core/CL/cl_kernels/batchnormalization_layer.cl | 162 | ||||
-rw-r--r-- | src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp | 221 |
3 files changed, 382 insertions, 2 deletions
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 957543c877..a2428ca99d 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -237,6 +237,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "fill_image_borders_constant", "fill_border.cl" }, { "fill_image_borders_replicate", "fill_border.cl" }, { "finalize", "optical_flow_pyramid_lk.cl" }, + { "fuse_batchnormalization_layer", "batchnormalization_layer.cl" }, { "floor_layer", "floor.cl" }, { "gaussian1x5_sub_x", "gaussian_pyramid.cl" }, { "gaussian5x1_sub_y", "gaussian_pyramid.cl" }, diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl index 5352af3c5a..df141269bc 100644 --- a/src/core/CL/cl_kernels/batchnormalization_layer.cl +++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl @@ -23,14 +23,14 @@ */ #include "helpers.h" -#if defined(VEC_SIZE) && defined(DATA_TYPE) - #define ADD_OP(a, b) ((a) + (b)) #define SUB_OP(a, b) ((a) - (b)) #define MUL_OP(a, b) ((a) * (b)) #define INVSQRT_OP(a) rsqrt((a)) #define SQCVT_SAT(a) (a) +#if defined(VEC_SIZE) && defined(DATA_TYPE) + #if defined(FUSED_ACTIVATION) #include "activation_layer.cl" #define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x) @@ -258,3 +258,161 @@ __kernel void batchnormalization_layer_nhwc(TENSOR3D_DECLARATION(input), (res, 0, (__global DATA_TYPE *)out.ptr); } #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) */ + +#if defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) +/** Fuse batchnorm parameters to convolution layer parameters + * + * @attention Data type should be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float + * @attention Input tensor depth should be given as a preprocessor argument using -DNUM_CHANNELS=size. e.g. -DNUM_CHANNELS=16 + * @attention Batch normalization epsilon parameter should be given as a preprocessor argument with -DEPSILON=value. e.g. -DEPSILON=0.001f + * + * @param[in] conv_w_ptr Pointer to the source tensor. Supported data types: F16/F32 + * @param[in] conv_w_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] conv_w_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] conv_w_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] conv_w_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] conv_w_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] conv_w_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] conv_w__stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] conv_w__step_w input_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] conv_w_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] bn_mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_mean_stride_x Stride of the mean source tensor in X dimension (in bytes) + * @param[in] bn_mean_step_x bn_mean_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor + * @param[in] bn_var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr + * @param[in] bn_var_stride_x Stride of the var tensor in X dimension (in bytes) + * @param[in] bn_var_step_x bn_var_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_var_offset_first_element_in_bytes The offset of the first element in the var source tensor + * @param[out] fused_w_ptr Pointer to the destination weights tensors. Supported data types: same as @p input_ptr + * @param[in] fused_w_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] fused_w_step_x fused_w_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] fused_w_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] fused_w_step_y fused_w_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] fused_w_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] fused_w_step_z fused_w_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] fused_w_stride_w Stride of the destination tensor in W dimension (in bytes) + * @param[in] fused_w_step_w fused_w_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] fused_w_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] fused_b_ptr Pointer to the destination bias tensor. Supported data types: same as @p input_ptr + * @param[in] fused_b_stride_x Stride of the bias source tensor in X dimension (in bytes) + * @param[in] fused_b_step_x fused_b_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] fused_b_offset_first_element_in_bytes The offset of the first element in the destination tensor + * @param[in] conv_b_ptr Pointer to the source bias tensor. Supported data types: same as @p input_ptr + * @param[in] conv_b_stride_x Stride of the beta source tensor in X dimension (in bytes) + * @param[in] conv_b_step_x conv_b_beta_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] conv_b_offset_first_element_in_bytes The offset of the first element in the source bias tensor + * @param[in] bn_beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_beta_stride_x Stride of the beta source tensor in X dimension (in bytes) + * @param[in] bn_beta_step_x bn_beta_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor + * @param[in] bn_gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr + * @param[in] bn_gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes) + * @param[in] bn_gamma_step_x bn_gamma_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bn_gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor + * @param[in] epsilon Epsilon parameter in the batch normalization equation + */ +__kernel void fuse_batchnormalization_layer(TENSOR4D_DECLARATION(conv_w), + VECTOR_DECLARATION(bn_mean), + VECTOR_DECLARATION(bn_var) +#ifndef IN_PLACE_W + , + TENSOR4D_DECLARATION(fused_w) +#endif /* not IN_PLACE_W */ +#ifndef IN_PLACE_B + , + VECTOR_DECLARATION(fused_b) +#endif /* not IN_PLACE_B */ +#ifdef HAS_BIAS + , + VECTOR_DECLARATION(conv_b) +#endif /* HAS_BIAS */ +#ifndef USE_DEFAULT_BETA + , + VECTOR_DECLARATION(bn_beta) +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + , + VECTOR_DECLARATION(bn_gamma) +#endif /* USE_DEFAULT_GAMMA */ + ) +{ + Tensor4D conv_w = CONVERT_TO_TENSOR4D_STRUCT(conv_w, NUM_CHANNELS); + Vector bn_mean = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_mean); + Vector bn_var = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_var); + + // In-place ops +#ifdef IN_PLACE_W + Tensor4D fused_w = conv_w; +#else /* IN_PLACE_W */ + Tensor4D fused_w = CONVERT_TO_TENSOR4D_STRUCT(fused_w, NUM_CHANNELS); +#endif /* IN_PLACE */ +#ifdef IN_PLACE_B + Vector fused_b = conv_b; +#else /* IN_PLACE_W */ + Vector fused_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(fused_b); +#endif /* IN_PLACE */ + + // Conditional ops +#ifdef HAS_BIAS + Vector conv_b = CONVERT_TO_VECTOR_STRUCT_NO_STEP(conv_b); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_BETA + Vector bn_beta = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_beta); +#endif /* USE_DEFAULT_BETA */ +#ifndef USE_DEFAULT_GAMMA + Vector bn_gamma = CONVERT_TO_VECTOR_STRUCT_NO_STEP(bn_gamma); +#endif /* USE_DEFAULT_GAMMA */ + + const int current_slice = get_global_id(2) / NUM_CHANNELS; + +#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + // Check if access on width gets out of bounds + // If it does shift access vector to access elements within bounds + const int xi = (int)(get_global_id(0) * VEC_SIZE); + conv_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * conv_w_stride_x; + fused_w.ptr -= max(xi - (int)LAST_ACCESSED_X, 0) * fused_w_stride_x; + + // Load W + VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) + wn = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)conv_w.ptr); +#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X) + DATA_TYPE wn = *((__global DATA_TYPE *)(conv_w.ptr)); +#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + + // rvar = 1 / sqrt(var + epsilon) + const DATA_TYPE var = *((__global DATA_TYPE *)(bn_var.ptr + current_slice * bn_var.stride_x)); + const DATA_TYPE rvar = INVSQRT_OP(ADD_OP(var, SQCVT_SAT((float)EPSILON))); + wn *= rvar; + + // Load b + const DATA_TYPE mean = *((__global DATA_TYPE *)(bn_mean.ptr + current_slice * bn_mean.stride_x)); + DATA_TYPE bn = 0; +#ifdef HAS_BIAS + bn = *((__global DATA_TYPE *)(conv_b.ptr + current_slice * conv_b.stride_x)); +#endif /* HAS_BIAS */ + bn = (bn - mean) * rvar; + +#ifndef USE_DEFAULT_GAMMA + const DATA_TYPE gamma_scalar = *((__global DATA_TYPE *)(bn_gamma.ptr + current_slice * bn_gamma.stride_x)); + wn *= gamma_scalar; + bn *= gamma_scalar; +#endif /* USE_DEFAULT_GAMMA */ + +#ifndef USE_DEFAULT_BETA + const DATA_TYPE beta_scalar = *((__global DATA_TYPE *)(bn_beta.ptr + current_slice * bn_beta.stride_x)); + bn += beta_scalar; +#endif /* USE_DEFAULT_BETA */ + +#if defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + // Store updated weights + VSTORE(VEC_SIZE) + (wn, 0, (__global DATA_TYPE *)fused_w.ptr); +#else // !defined(VEC_SIZE) || !defined(LAST_ACCESSED_X) + *((__global DATA_TYPE *)(fused_w.ptr)) = wn; +#endif // defined(VEC_SIZE) && defined(LAST_ACCESSED_X) + + // Store updated bias + *((__global DATA_TYPE *)(fused_b.ptr + current_slice * fused_b.stride_x)) = bn; +} +#endif /* defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON) */ diff --git a/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp new file mode 100644 index 0000000000..e14b8a3777 --- /dev/null +++ b/src/core/CL/kernels/CLFuseBatchNormalizationKernel.cpp @@ -0,0 +1,221 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLFuseBatchNormalizationKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Window.h" + +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, + const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, + const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, + float epsilon) +{ + ARM_COMPUTE_UNUSED(epsilon); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(conv_weights); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(conv_weights, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_var); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_mean, bn_var); + + unsigned int kernels_idx = get_data_layout_dimension_index(conv_weights->data_layout(), DataLayoutDimension::BATCHES); + ARM_COMPUTE_RETURN_ERROR_ON(conv_weights->dimension(kernels_idx) != bn_mean->dimension(0)); + + // Validate bias + if(conv_bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, conv_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, conv_bias); + } + // Validate beta + if(bn_beta != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_beta); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_beta); + } + // Validate gamma + if(bn_gamma != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, bn_gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, bn_gamma); + } + + // Validate output weights + if(fused_weights != nullptr && fused_weights->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(conv_weights, fused_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(conv_weights, fused_weights); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_weights); + } + // Validate output bias + if(fused_bias != nullptr && fused_bias->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(bn_mean, fused_bias); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(conv_weights, fused_bias); + } + + return Status{}; +} +} // namespace + +CLFuseBatchNormalizationKernel::CLFuseBatchNormalizationKernel() + : _conv_weights(nullptr), _conv_bias(nullptr), _bn_mean(nullptr), _bn_var(nullptr), _bn_gamma(nullptr), _bn_beta(nullptr), _fused_weights(nullptr), _fused_bias(nullptr), _epsilon(), + _run_in_place_weights(false), _run_in_place_bias(false) +{ +} + +void CLFuseBatchNormalizationKernel::configure(const ICLTensor *conv_weights, const ICLTensor *bn_mean, const ICLTensor *bn_var, + ICLTensor *fused_weights, ICLTensor *fused_bias, + const ICLTensor *conv_bias, const ICLTensor *bn_beta, const ICLTensor *bn_gamma, + float epsilon) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(conv_weights, bn_mean, bn_var); + + _conv_weights = conv_weights; + _conv_bias = conv_bias; + _bn_mean = bn_mean; + _bn_var = bn_var; + _bn_beta = bn_beta; + _bn_gamma = bn_gamma; + _fused_weights = fused_weights; + _fused_bias = fused_bias; + _epsilon = epsilon; + + _run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights); + _run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias); + + // Auto initialize outputs + if(_fused_weights != nullptr) + { + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*_fused_weights->info(), *_conv_weights->info()->clone()); + fused_weights->info()->set_valid_region(conv_weights->info()->valid_region()); + } + if(_fused_bias != nullptr) + { + // Output tensor auto initialization if not yet initialized + auto_init_if_empty(*_fused_bias->info(), *_bn_mean->info()->clone()); + _fused_bias->info()->set_valid_region(bn_mean->info()->valid_region()); + } + + // Validate arguments + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(conv_weights->info(), bn_mean->info(), bn_var->info(), + (fused_weights != nullptr) ? fused_weights->info() : nullptr, + (fused_bias != nullptr) ? fused_bias->info() : nullptr, + (conv_bias != nullptr) ? conv_bias->info() : nullptr, + (bn_beta != nullptr) ? bn_beta->info() : nullptr, + (bn_gamma != nullptr) ? bn_gamma->info() : nullptr, + epsilon)); + + // Configure kernel window + const unsigned int num_elems_processed_per_iteration_x = 16 / conv_weights->info()->element_size(); + const int output_width_x = conv_weights->info()->tensor_shape().x(); + const bool multi_access_x = (output_width_x / num_elems_processed_per_iteration_x > 0); + + Window win = calculate_max_window(*conv_weights->info()); + if(multi_access_x) + { + win.set(Window::DimX, Window::Dimension(win.x().start(), + ceil_to_multiple(win.x().end(), num_elems_processed_per_iteration_x), + num_elems_processed_per_iteration_x)); + } + ICLKernel::configure_internal(win); + + // Set build options + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(conv_weights->info()->data_type())); + build_opts.add_option("-DSELECT_DATA_TYPE=" + get_cl_select_type_from_data_type(conv_weights->info()->data_type())); + build_opts.add_option("-DNUM_CHANNELS=" + support::cpp11::to_string(conv_weights->info()->dimension(2))); + build_opts.add_option("-DEPSILON=" + float_to_string_with_full_precision(epsilon)); + build_opts.add_option_if(multi_access_x, "-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration_x)); + build_opts.add_option_if(multi_access_x, "-DLAST_ACCESSED_X=" + support::cpp11::to_string(std::max<int>(output_width_x - num_elems_processed_per_iteration_x, 0))); + build_opts.add_option_if(_run_in_place_weights, "-DIN_PLACE_W"); + build_opts.add_option_if(_run_in_place_bias, "-DIN_PLACE_B"); + build_opts.add_option_if(conv_bias != nullptr, "-DHAS_BIAS"); + build_opts.add_option_if(bn_beta == nullptr, "-DUSE_DEFAULT_BETA"); + build_opts.add_option_if(bn_gamma == nullptr, "-DUSE_DEFAULT_GAMMA"); + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("fuse_batchnormalization_layer", build_opts.options())); +} + +Status CLFuseBatchNormalizationKernel::validate(const ITensorInfo *conv_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, + const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, + const ITensorInfo *conv_bias, const ITensorInfo *bn_beta, const ITensorInfo *bn_gamma, + float epsilon) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(conv_weights, bn_mean, bn_var, fused_weights, fused_bias, conv_bias, bn_beta, bn_gamma, epsilon)); + return Status{}; +} + +void CLFuseBatchNormalizationKernel::run(const arm_compute::Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + // Create window slice + Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); + Window slice = collapsed_window.first_slice_window_4D(); + + Window vector_slice = window.first_slice_window_1D(); + vector_slice.set(Window::DimX, Window::Dimension(0, 0, 0)); + + // Add kernel arguments + unsigned int idx = 0; + add_4D_tensor_argument(idx, _conv_weights, slice); + add_1D_tensor_argument(idx, _bn_mean, vector_slice); + add_1D_tensor_argument(idx, _bn_var, vector_slice); + if(!_run_in_place_weights) + { + add_4D_tensor_argument(idx, _fused_weights, slice); + } + if(!_run_in_place_bias) + { + add_1D_tensor_argument(idx, _fused_bias, vector_slice); + } + if(_conv_bias != nullptr) + { + add_1D_tensor_argument(idx, _conv_bias, vector_slice); + } + if(_bn_beta != nullptr) + { + add_1D_tensor_argument(idx, _bn_beta, vector_slice); + } + if(_bn_gamma != nullptr) + { + add_1D_tensor_argument(idx, _bn_gamma, vector_slice); + } + enqueue(queue, *this, slice, lws_hint()); +} +} // namespace arm_compute |