| /* |
| * Copyright (c) 2017-2019 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 "DepthConcatenateLayer.h" |
| |
| #include "tests/validation/Helpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| template <typename T> |
| SimpleTensor<T> depthconcatenate_layer(const std::vector<SimpleTensor<T>> &srcs, SimpleTensor<T> &dst) |
| { |
| // Create reference |
| std::vector<TensorShape> shapes; |
| |
| for(const auto &src : srcs) |
| { |
| shapes.emplace_back(src.shape()); |
| } |
| |
| // Compute reference |
| int depth_offset = 0; |
| const int width_out = dst.shape().x(); |
| const int height_out = dst.shape().y(); |
| const int depth_out = dst.shape().z(); |
| const int out_stride_z = width_out * height_out; |
| const int batches = dst.shape().total_size_upper(3); |
| auto have_different_quantization_info = [&](const SimpleTensor<T> &tensor) |
| { |
| return tensor.quantization_info() != dst.quantization_info(); |
| }; |
| if(srcs[0].data_type() == DataType::QASYMM8 && std::any_of(srcs.cbegin(), srcs.cend(), have_different_quantization_info)) |
| { |
| for(int b = 0; b < batches; ++b) |
| { |
| // input tensors can have smaller width and height than the output, so for each output's slice we need to requantize 0 (as this is the value |
| // used in NEFillBorderKernel by NEDepthConcatenateLayer) using the corresponding quantization info for that particular slice/input tensor. |
| int slice = 0; |
| for(const auto &src : srcs) |
| { |
| auto ptr_slice = static_cast<T *>(dst(Coordinates(0, 0, slice, b))); |
| const auto num_elems_in_slice((dst.num_elements() / depth_out) * src.shape().z()); |
| std::transform(ptr_slice, ptr_slice + num_elems_in_slice, ptr_slice, [src, dst](T t) |
| { |
| return dst.quantization_info().quantize(src.quantization_info().dequantize(0), RoundingPolicy::TO_NEAREST_UP); |
| }); |
| slice += src.shape().z(); |
| } |
| } |
| } |
| else |
| { |
| std::fill_n(dst.data(), dst.num_elements(), 0); |
| } |
| |
| for(const auto &src : srcs) |
| { |
| ARM_COMPUTE_ERROR_ON(depth_offset >= depth_out); |
| ARM_COMPUTE_ERROR_ON(batches != static_cast<int>(src.shape().total_size_upper(3))); |
| |
| const int width = src.shape().x(); |
| const int height = src.shape().y(); |
| const int depth = src.shape().z(); |
| const int x_diff = (width_out - width) / 2; |
| const int y_diff = (height_out - height) / 2; |
| |
| const T *src_ptr = src.data(); |
| |
| for(int b = 0; b < batches; ++b) |
| { |
| const size_t offset_to_first_element = b * out_stride_z * depth_out + depth_offset * out_stride_z + y_diff * width_out + x_diff; |
| |
| for(int d = 0; d < depth; ++d) |
| { |
| for(int r = 0; r < height; ++r) |
| { |
| if(src.data_type() == DataType::QASYMM8 && src.quantization_info() != dst.quantization_info()) |
| { |
| std::transform(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out, [src, dst](T t) |
| { |
| const float dequantized_input = src.quantization_info().dequantize(t); |
| return dst.quantization_info().quantize(dequantized_input, RoundingPolicy::TO_NEAREST_UP); |
| }); |
| src_ptr += width; |
| } |
| else |
| { |
| std::copy(src_ptr, src_ptr + width, dst.data() + offset_to_first_element + d * out_stride_z + r * width_out); |
| src_ptr += width; |
| } |
| } |
| } |
| } |
| |
| depth_offset += depth; |
| } |
| |
| return dst; |
| } |
| |
| template SimpleTensor<uint8_t> depthconcatenate_layer(const std::vector<SimpleTensor<uint8_t>> &srcs, SimpleTensor<uint8_t> &dst); |
| template SimpleTensor<float> depthconcatenate_layer(const std::vector<SimpleTensor<float>> &srcs, SimpleTensor<float> &dst); |
| template SimpleTensor<half> depthconcatenate_layer(const std::vector<SimpleTensor<half>> &srcs, SimpleTensor<half> &dst); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |