From 7657224de2b697a8a92cccf26d98e53ccd7c1a03 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Wed, 4 Apr 2018 17:44:26 +0100 Subject: COMPMID-926 Add depth multiplier support to NEON/CL/GLES depthwise convolution Change-Id: I03f32c62350e5ea43e77bb15fc5a832d83719e3b Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126657 Tested-by: Jenkins Reviewed-by: Michele DiGiorgio Reviewed-by: Georgios Pinitas --- .../NEDepthwiseConvolutionLayer3x3Kernel.cpp | 41 ++++++++++++---------- 1 file changed, 22 insertions(+), 19 deletions(-) (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp index 49c67d19bb..8cdf175d8a 100644 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp @@ -52,13 +52,14 @@ class convolver_3x3 { public: static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { const int input_offset = -input->info()->quantization_info().offset; const int weights_offset = -weights->info()->quantization_info().offset; const int input_stride_x = input->info()->strides_in_bytes().x(); const int input_stride_y = input->info()->strides_in_bytes().y(); + const int input_stride_z = input->info()->strides_in_bytes().z(); const int output_stride_y = output->info()->strides_in_bytes().y(); const int kernel_stride_y = weights->info()->strides_in_bytes().y(); const int kernel_stride_z = weights->info()->strides_in_bytes().z(); @@ -93,7 +94,7 @@ public: int ih = 0; int oh = 0; - const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y; + const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y - (id.z() - id.z() / depth_multiplier) * input_stride_z; const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; const auto ptr_weights_r0 = reinterpret_cast(ptr_weights_base); @@ -125,19 +126,19 @@ public: template inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration, - const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info) + const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); switch(conv_stride_x) { case 1: - convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); + convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); break; case 2: - convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); + convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); break; case 3: - convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info); + convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); break; default: ARM_COMPUTE_ERROR("Not implemented"); @@ -146,7 +147,7 @@ inline void convolve_3x3(const Window &window, unsigned int num_elems_written_pe } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() - : _border_size(0), _input(), _output(), _weights(), _conv_info(), _convolver(nullptr), _num_elems_written_per_iteration(0), _run_optimized(false) + : _border_size(0), _input(), _output(), _weights(), _conv_info(), _convolver(nullptr), _num_elems_written_per_iteration(0), _run_optimized(false), _depth_multiplier(1) { } @@ -155,20 +156,22 @@ BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const return _border_size; } -void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, DataLayout data_layout) +void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, + DataLayout data_layout) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - _input = input; - _output = output; - _weights = weights; - _conv_info = conv_info; - _convolver = nullptr; + _input = input; + _output = output; + _weights = weights; + _conv_info = conv_info; + _depth_multiplier = depth_multiplier; + _convolver = nullptr; _run_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->info()->tensor_shape(), conv_info, - input->info()->data_type(), + input->info()->data_type(), depth_multiplier, data_layout); (_run_optimized) ? configure_optimized() : configure_generic(); @@ -182,7 +185,7 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const Threa (_run_optimized) ? run_optimized(window, info) : run_generic(window, info); } -bool NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(TensorShape input_shape, PadStrideInfo conv_info, DataType dt, DataLayout data_layout) +bool NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(TensorShape input_shape, PadStrideInfo conv_info, DataType dt, unsigned int depth_multiplier, DataLayout data_layout) { // Reshape input shape if in NHWC format TensorShape in_shape{ input_shape }; @@ -210,7 +213,7 @@ bool NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(Tenso bool is_valid_padding = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0); bool supported_padding = is_same_padding || is_valid_padding; - return supported_datatype && supported_strides && supported_padding; + return supported_datatype && supported_strides && supported_padding && (depth_multiplier == 1); } void NEDepthwiseConvolutionLayer3x3Kernel::generate_convolver() @@ -227,7 +230,7 @@ void NEDepthwiseConvolutionLayer3x3Kernel::configure_generic() ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(0) != 3 || _weights->info()->dimension(1) != 3); // Get convolved dimensions - const TensorShape output_shape = compute_depthwise_convolution_shape(*_input->info(), *_weights->info(), _conv_info); + const TensorShape output_shape = compute_depthwise_convolution_shape(*_input->info(), *_weights->info(), _conv_info, _depth_multiplier); const DataType output_dt = (_input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : _input->info()->data_type(); // Output auto inizialitation if not yet initialized @@ -317,10 +320,10 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run_generic(const Window &window, con switch(_input->info()->data_type()) { case DataType::F32: - convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); break; case DataType::QASYMM8: - convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info); + convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); break; default: ARM_COMPUTE_ERROR("Not implemented"); -- cgit v1.2.1