From 5a7e776eee2e9147eab12631f5717847fb6cac5c Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 1 Dec 2017 16:27:29 +0000 Subject: COMPMID-556: Rename CPP folder to reference Change-Id: I147644349547c4e3804a80b564a9ad95131ad2d0 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111560 Reviewed-by: Michalis Spyrou Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com --- .../reference/DepthwiseConvolutionLayer.cpp | 195 +++++++++++++++++++++ 1 file changed, 195 insertions(+) create mode 100644 tests/validation/reference/DepthwiseConvolutionLayer.cpp (limited to 'tests/validation/reference/DepthwiseConvolutionLayer.cpp') diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp new file mode 100644 index 0000000000..0e88d3dbd3 --- /dev/null +++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp @@ -0,0 +1,195 @@ +/* + * 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 "DepthwiseConvolutionLayer.h" + +#include "ConvolutionLayer.h" +#include "Utils.h" + +#include "tests/validation/FixedPoint.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/reference/Utils.h" +#include "tests/validation/reference/UtilsQuantizedAsymm.h" + +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +/** Perform a depthwise convolution + * + * - Three dimensions tensors + * - Third dimention is number of channels + * - Depths of input tensor and filter are equals + * - Padding, stride and output shape "match" + * + */ +template +SimpleTensor depthwise_convolution(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info) +{ + // Create reference + SimpleTensor dst{ dst_shape, src.data_type(), 1, src.fixed_point_position() }; + + // Compute reference + const int filter_width = weights.shape().x(); + const int filter_height = weights.shape().y(); + const int filter_plane = filter_width * filter_height; + const int input_width = src.shape().x(); + const int input_height = src.shape().y(); + const int input_depth = src.shape().z(); + const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth); + + const int filter_half_width = filter_width / 2; + const int filter_half_height = filter_height / 2; + + const int pad_left = std::min(static_cast(conv_info.pad_left()), filter_half_width); + const int pad_top = std::min(static_cast(conv_info.pad_top()), filter_half_height); + const int pad_right = std::min(static_cast(conv_info.pad_right()), filter_half_width); + const int pad_bottom = std::min(static_cast(conv_info.pad_bottom()), filter_half_height); + + const int minimum_x = -pad_left + filter_half_width; + const int minimum_y = -pad_top + filter_half_height; + const int maximum_x = input_width + pad_left - filter_half_width + pad_right - filter_half_width; + const int maximum_y = input_height + pad_top - filter_half_height + pad_bottom - filter_half_height; + + int out_pos = 0; + for(int r = 0; r < num_batches; ++r) + { + for(int z = 0; z < input_depth; ++z) + { + for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second) + { + for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first) + { + Coordinates coords(static_cast(x), static_cast(y), static_cast(z), static_cast(r)); + size_t filter_offset = filter_plane * z; + + T val = 0; + for(int j = y - filter_half_height; j <= static_cast(y + filter_half_height); ++j) + { + for(int i = x - filter_half_width; i <= static_cast(x + filter_half_width); ++i) + { + coords.set(0, i); + coords.set(1, j); + val += *(weights.data() + filter_offset) * tensor_elem_at(src, coords, BorderMode::CONSTANT, 0.f); + ++filter_offset; + } + } + coords.set(0, x); + coords.set(1, y); + dst[out_pos++] = saturate_cast(val + *static_cast(biases(Coordinates(z)))); + } + } + } + } + + return dst; +} + +template <> +SimpleTensor depthwise_convolution(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &biases, const TensorShape &dst_shape, + const PadStrideInfo &conv_info) +{ + // Create reference + SimpleTensor dst{ dst_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() }; + + const int input_offset = -src.quantization_info().offset; + const float input_scale = src.quantization_info().scale; + const int weights_offset = -weights.quantization_info().offset; + const float weights_scale = weights.quantization_info().scale; + const int output_offset = dst.quantization_info().offset; + const float output_scale = dst.quantization_info().scale; + + int output_multiplier; + int output_shift; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + // Compute reference + const int filter_width = weights.shape().x(); + const int filter_height = weights.shape().y(); + const int filter_plane = filter_width * filter_height; + const int input_width = src.shape().x(); + const int input_height = src.shape().y(); + const int input_depth = src.shape().z(); + const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth); + + const int filter_half_size = filter_width / 2; + const int pad_x = std::min(filter_half_size, static_cast(conv_info.pad().first)); + const int pad_y = std::min(filter_half_size, static_cast(conv_info.pad().second)); + const int minimum_x = -pad_x + filter_half_size; + const int minimum_y = -pad_y + filter_half_size; + + int out_pos = 0; + for(int r = 0; r < num_batches; ++r) + { + for(int z = 0; z < input_depth; ++z) + { + int32_t bias_val = *static_cast(biases(Coordinates(z))); + for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second) + { + for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first) + { + Coordinates coords(x, y, z); + int filter_offset = filter_plane * z; + + uint32_t val = 0; + for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j) + { + for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i) + { + coords.set(0, i); + coords.set(1, j); + auto in_val = tensor_elem_at(src, coords, BorderMode::CONSTANT, 0); + uint8_t w_val = *(weights.data() + filter_offset); + val += (in_val + input_offset) * (w_val + weights_offset); + ++filter_offset; + } + } + val += bias_val; + val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift); + val += output_offset; + val = std::max(val, 0); + val = std::min(val, 255); + + // Store the result + dst[out_pos++] = val; + } + } + } + } + + return dst; +} + +template SimpleTensor depthwise_convolution(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &biases, const TensorShape &dst_shape, + const PadStrideInfo &conv_info); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute -- cgit v1.2.1