diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp | 126 |
1 files changed, 126 insertions, 0 deletions
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp new file mode 100644 index 0000000000..b890c6f5d5 --- /dev/null +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -0,0 +1,126 @@ +/* + * Copyright (c) 2017 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/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/PixelValue.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "support/ToolchainSupport.h" + +using namespace arm_compute; + +NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3() + : _kernel(), _bias_kernel(), _border_handler(), _has_bias(false) +{ +} + +void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights); + + // Call convolution kernel + _kernel.configure(input, weights, output, conv_info); + _border_handler.configure(input, _kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast<float>(0.f))); + if(biases != nullptr) + { + _bias_kernel.configure(output, biases); + _has_bias = true; + } +} + +void NEDepthwiseConvolutionLayer3x3::run() +{ + NEScheduler::get().schedule(&_border_handler, Window::DimX); + NEScheduler::get().schedule(&_kernel, Window::DimX); + if(_has_bias) + { + NEScheduler::get().schedule(&_bias_kernel, Window::DimX); + } +} + +NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer() + : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _input_reshaped(), _weights_reshaped(), _v2mm_output() +{ +} + +void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != weights->info()->dimension(2)); + + const size_t weights_w = weights->info()->dimension(0); + const size_t weights_h = weights->info()->dimension(1); + const size_t weights_z = weights->info()->dimension(2); + + bool has_bias = (biases != nullptr); + + unsigned int conv_w = 0; + unsigned int conv_h = 0; + std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights_w, weights_h, conv_info); + + // Set up intermediate tensors + const size_t patch_size = weights_w * weights_h + ((has_bias) ? 1 : 0); + const size_t conv_size = conv_w * conv_h; + + // Im2Col configuration + TensorShape shape_im2col = input->info()->tensor_shape(); + shape_im2col.set(0, patch_size); + shape_im2col.set(1, conv_size); + shape_im2col.set(2, weights_z); + const TensorInfo info_im2col(shape_im2col, 1, input->info()->data_type(), input->info()->fixed_point_position()); + _input_reshaped.allocator()->init(info_im2col); + _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, has_bias); + + // Weights reshape configuration + const TensorShape shape_weights_reshape(patch_size, weights_z); + const TensorInfo info_weights_reshape(shape_weights_reshape, 1, weights->info()->data_type(), weights->info()->fixed_point_position()); + _weights_reshaped.allocator()->init(info_weights_reshape); + _weights_reshape_kernel.configure(weights, &_weights_reshaped, biases); + + // GEMV configuration + TensorShape shape_v2mm_out = input->info()->tensor_shape(); + shape_v2mm_out.set(0, conv_size * weights_z); + shape_v2mm_out.set(1, 1); + shape_v2mm_out.set(2, 1); + const TensorInfo info_v2mm_out(shape_v2mm_out, 1, input->info()->data_type(), input->info()->fixed_point_position()); + _v2mm_output.allocator()->init(info_v2mm_out); + _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output); + _vector_to_tensor_kernel.configure(&_v2mm_output, output, conv_w, conv_h); + + // Allocate intermediate tensors + _input_reshaped.allocator()->allocate(); + _weights_reshaped.allocator()->allocate(); + _v2mm_output.allocator()->allocate(); +} + +void NEDepthwiseConvolutionLayer::run() +{ + NEScheduler::get().schedule(&_im2col_kernel, Window::DimX); + NEScheduler::get().schedule(&_weights_reshape_kernel, Window::DimX); + NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX); + NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX); +}
\ No newline at end of file |