| /* |
| * Copyright (c) 2017-2018 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/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 <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, |
| unsigned int depth_multiplier) |
| { |
| SimpleTensor<T> dst{ dst_shape, src.data_type(), 1 }; |
| |
| // 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 = conv_info.pad_left(); |
| const int pad_top = conv_info.pad_top(); |
| const int pad_right = conv_info.pad_right(); |
| const int pad_bottom = conv_info.pad_bottom(); |
| |
| 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; |
| |
| const T border_value(0); |
| |
| int out_pos = 0; |
| for(int r = 0; r < num_batches; ++r) |
| { |
| for(int z = 0; z < input_depth; ++z) |
| { |
| for(unsigned int m = 0; m < depth_multiplier; ++m) |
| { |
| const int out_z = z * depth_multiplier + m; |
| |
| 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 * out_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, border_value); |
| ++filter_offset; |
| } |
| } |
| |
| dst[out_pos++] = saturate_cast<T>(val + *static_cast<const TB *>(biases(Coordinates(out_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, unsigned int depth_multiplier) |
| { |
| SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.quantization_info() }; |
| |
| // Create reference |
| 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_width = filter_width / 2; |
| const int filter_half_height = filter_height / 2; |
| |
| const int pad_left = conv_info.pad_left(); |
| const int pad_top = conv_info.pad_top(); |
| const int pad_right = conv_info.pad_right(); |
| const int pad_bottom = conv_info.pad_bottom(); |
| |
| 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(unsigned int m = 0; m < depth_multiplier; ++m) |
| { |
| const int out_z = z * depth_multiplier + m; |
| const int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(out_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(x, y, z, r); |
| int filter_offset = filter_plane * out_z; |
| |
| int32_t val = 0; |
| for(int j = y - filter_half_height; j <= (y + filter_half_height); ++j) |
| { |
| for(int i = x - filter_half_width; i <= (x + filter_half_width); ++i) |
| { |
| coords.set(0, i); |
| coords.set(1, j); |
| const auto in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, -input_offset); |
| const 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, unsigned int depth_multiplier); |
| |
| template SimpleTensor<half> depthwise_convolution(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &biases, const TensorShape &dst_shape, |
| const PadStrideInfo &conv_info, unsigned int depth_multiplier); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |