diff options
Diffstat (limited to 'src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp | 20 |
1 files changed, 14 insertions, 6 deletions
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index a5db977371..132c3ee926 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -98,6 +98,13 @@ CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryMa void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math) { + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, act_info, enable_fast_math); +} + +void CLWinogradConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, bool enable_fast_math) +{ // Get indices for the width and height const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); @@ -129,17 +136,18 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we // Do not manage _input1 as it contains the weights // Configure input transform - _input_transform.configure(input, &_input0, winograd_info); + _input_transform.configure(compile_context, input, &_input0, winograd_info); // Configure filter transform - _filter_transform.configure(weights, &_input1, winograd_info); + _filter_transform.configure(compile_context, weights, &_input1, winograd_info); // Configure batched matrix multiply - _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, GEMMLowpOutputStageInfo(), - (input->info()->data_type() == DataType::F16))); + _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, + GEMMLowpOutputStageInfo(), + (input->info()->data_type() == DataType::F16))); // Configure output transform - _output_transform.configure(&_batched_mm_output, biases, output, winograd_info, act_info); + _output_transform.configure(compile_context, &_batched_mm_output, biases, output, winograd_info, act_info); // Allocate temporary tensors _input0.allocator()->allocate(); |