diff options
Diffstat (limited to 'tests/validation/CPP/DepthwiseConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/CPP/DepthwiseConvolutionLayer.cpp | 195 |
1 files changed, 0 insertions, 195 deletions
diff --git a/tests/validation/CPP/DepthwiseConvolutionLayer.cpp b/tests/validation/CPP/DepthwiseConvolutionLayer.cpp deleted file mode 100644 index 99baa4b3c7..0000000000 --- a/tests/validation/CPP/DepthwiseConvolutionLayer.cpp +++ /dev/null @@ -1,195 +0,0 @@ -/* - * 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/CPP/Utils.h" -#include "tests/validation/CPP/UtilsQuantizedAsymm.h" -#include "tests/validation/FixedPoint.h" -#include "tests/validation/Helpers.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 <typename T, typename TB> -SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info) -{ - // Create reference - SimpleTensor<T> 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<int>(conv_info.pad_left()), filter_half_width); - const int pad_top = std::min(static_cast<int>(conv_info.pad_top()), filter_half_height); - const int pad_right = std::min(static_cast<int>(conv_info.pad_right()), filter_half_width); - const int pad_bottom = std::min(static_cast<int>(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<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r)); - size_t filter_offset = filter_plane * z; - - T val = 0; - for(int j = y - filter_half_height; j <= static_cast<int>(y + filter_half_height); ++j) - { - for(int i = x - filter_half_width; i <= static_cast<int>(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<T>(val + *static_cast<const TB *>(biases(Coordinates(z)))); - } - } - } - } - - return dst; -} - -template <> -SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape, - const PadStrideInfo &conv_info) -{ - // Create reference - SimpleTensor<uint8_t> 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<int>(conv_info.pad().first)); - const int pad_y = std::min(filter_half_size, static_cast<int>(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<const int32_t *>(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<uint8_t>(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<int32_t>(val, 0); - val = std::min<int32_t>(val, 255); - - // Store the result - dst[out_pos++] = val; - } - } - } - } - - return dst; -} - -template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape, - const PadStrideInfo &conv_info); -} // namespace reference -} // namespace validation -} // namespace test -} // namespace arm_compute |