| /* |
| * 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 "ConvolutionLayer.h" |
| |
| #include "tests/validation/FixedPoint.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/Utils.h" |
| #include "tests/validation/reference/UtilsQuantizedAsymm.h" |
| |
| #include "tests/framework/Asserts.h" |
| |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace reference |
| { |
| namespace |
| { |
| inline bool is_valid_pixel(int i, int min, int max) |
| { |
| return (i >= min && i < max); |
| } |
| |
| // 3D convolution for floating point type |
| template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 > |
| void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out, |
| int i_offset, int w_offset, int b_offset, int o_offset, |
| int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights) |
| { |
| const T *in_ptr = in.data() + i_offset; |
| const T *w_ptr = weights.data() + w_offset; |
| const TB *b_ptr = bias.data() + b_offset; |
| T *out_ptr = out.data() + o_offset; |
| |
| const int half_width_weights = width_weights / 2; |
| const int half_height_weights = height_weights / 2; |
| |
| // Reset accumulator |
| T acc(0); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) |
| { |
| for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights; |
| const int idy = yk + half_height_weights; |
| |
| const T i_value = in_ptr[offset_slice_in + xk + yk * width_in]; |
| const T w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights]; |
| |
| acc += i_value * w_value; |
| } |
| } |
| } |
| } |
| |
| // Accumulate the bias and store the result |
| *out_ptr = acc + (*b_ptr); |
| } |
| |
| // 3D convolution for fixed point type |
| template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 > |
| void convolution3d(const SimpleTensor<T> &in, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &out, |
| int i_offset, int w_offset, int b_offset, int o_offset, |
| int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights) |
| { |
| const T *in_ptr = in.data() + i_offset; |
| const T *w_ptr = weights.data() + w_offset; |
| const T *b_ptr = bias.data() + b_offset; |
| T *out_ptr = out.data() + o_offset; |
| int fixed_point_position = in.fixed_point_position(); |
| |
| const int half_width_weights = width_weights / 2; |
| const int half_height_weights = height_weights / 2; |
| |
| using namespace fixed_point_arithmetic; |
| using promoted_type = fixed_point_arithmetic::traits::promote_t<T>; |
| |
| // Reset accumulator |
| fixed_point<promoted_type> acc(0, fixed_point_position); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) |
| { |
| for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights; |
| const int idy = yk + half_height_weights; |
| |
| const fixed_point<promoted_type> i_value(in_ptr[offset_slice_in + xk + yk * width_in], fixed_point_position, true); |
| const fixed_point<promoted_type> w_value(w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights], fixed_point_position, true); |
| const fixed_point<promoted_type> iw = i_value * w_value; |
| acc = iw + acc; |
| } |
| } |
| } |
| } |
| |
| // Get the bias |
| const fixed_point<promoted_type> b(*b_ptr, fixed_point_position, true); |
| |
| // Accumulate the bias and covert back |
| acc = acc + b; |
| fixed_point<T> res(acc); |
| *out_ptr = res.raw(); |
| } |
| |
| // 3D convolution for QASYMM8 type |
| template <> |
| void convolution3d(const SimpleTensor<uint8_t> &in, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &out, |
| int i_offset, int w_offset, int b_offset, int o_offset, |
| int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights) |
| { |
| const uint8_t *in_ptr = in.data() + i_offset; |
| const uint8_t *w_ptr = weights.data() + w_offset; |
| const int32_t *b_ptr = bias.data() + b_offset; |
| uint8_t *out_ptr = out.data() + o_offset; |
| |
| const int input_offset = -in.quantization_info().offset; |
| const float input_scale = in.quantization_info().scale; |
| const int weights_offset = -weights.quantization_info().offset; |
| const float weights_scale = weights.quantization_info().scale; |
| const int output_offset = out.quantization_info().offset; |
| const float output_scale = out.quantization_info().scale; |
| |
| int output_multiplier = 0; |
| int output_shift = 0; |
| const float multiplier = input_scale * weights_scale / output_scale; |
| arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| |
| const int half_width_weights = width_weights / 2; |
| const int half_height_weights = height_weights / 2; |
| |
| // Reset accumulator |
| int32_t acc(0); |
| |
| // Compute a 2D convolution for each IFM and accumulate the result |
| for(int ifm = 0; ifm < depth_in; ++ifm) |
| { |
| // Compute the offset for the input slice |
| const int offset_slice_in = xi + yi * width_in + ifm * width_in * height_in; |
| |
| // Compute 2D convolution |
| for(int yk = -half_height_weights; yk <= half_height_weights; ++yk) |
| { |
| for(int xk = -half_width_weights; xk <= half_width_weights; ++xk) |
| { |
| // Check if the pixel is out-of-bound |
| if(is_valid_pixel(xi + xk, 0, width_in) && is_valid_pixel(yi + yk, 0, height_in)) |
| { |
| const int idx = xk + half_width_weights; |
| const int idy = yk + half_height_weights; |
| |
| const uint8_t i_value = in_ptr[offset_slice_in + xk + yk * width_in]; |
| const uint8_t w_value = w_ptr[idx + idy * width_weights + ifm * width_weights * height_weights]; |
| |
| acc += (i_value + input_offset) * (w_value + weights_offset); |
| } |
| } |
| } |
| } |
| |
| // Accumulate the bias |
| acc += (*b_ptr); |
| |
| acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift); |
| acc += output_offset; |
| acc = utility::clamp<int32_t>(acc, 0, 255); |
| |
| // Store the result |
| *out_ptr = acc; |
| } |
| } // namespace |
| |
| template <typename T, typename TB> |
| SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info) |
| { |
| // Create reference |
| SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() }; |
| |
| // Compute reference |
| const int width_in = src.shape().x(); |
| const int height_in = src.shape().y(); |
| const int depth_in = src.shape().z(); |
| const int width_out = dst.shape().x(); |
| const int height_out = dst.shape().y(); |
| const int depth_out = dst.shape().z(); |
| const int width_weights = weights.shape().x(); |
| const int height_weights = weights.shape().y(); |
| const int depth_weights = weights.shape().z(); |
| const int pad_left = std::min(static_cast<int>(info.pad_left()), width_weights / 2); |
| const int pad_top = std::min(static_cast<int>(info.pad_top()), height_weights / 2); |
| const int pad_right = std::min(static_cast<int>(info.pad_right()), width_weights / 2); |
| const int pad_bottom = std::min(static_cast<int>(info.pad_bottom()), height_weights / 2); |
| |
| const int start_xi = width_weights / 2 - pad_left; |
| const int start_yi = height_weights / 2 - pad_top; |
| const int end_xi = width_in + pad_left - width_weights / 2 + pad_right - width_weights / 2; |
| const int end_yi = height_in + pad_top - height_weights / 2 + pad_bottom - height_weights / 2; |
| const int stride_xi = info.stride().first; |
| const int stride_yi = info.stride().second; |
| const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in); |
| |
| for(int r = 0; r < num_batches; ++r) |
| { |
| for(int yi = start_yi; yi < start_yi + end_yi; yi += stride_yi) |
| { |
| for(int xi = start_xi; xi < start_xi + end_xi; xi += stride_xi) |
| { |
| for(int ofm = 0; ofm < depth_out; ++ofm) |
| { |
| // Compute input and output offsets |
| const int offset_in = r * width_in * height_in * depth_in; |
| const int xo = (xi - start_xi) / stride_xi; |
| const int yo = (yi - start_yi) / stride_yi; |
| const int offset_out = xo + yo * width_out + ofm * width_out * height_out + r * width_out * height_out * depth_out; |
| |
| ARM_COMPUTE_ASSERT(xo < width_out); |
| ARM_COMPUTE_ASSERT(yo < height_out); |
| |
| // Compute 3D convolution |
| convolution3d(src, weights, bias, dst, |
| offset_in, ofm * width_weights * height_weights * depth_weights, ofm, offset_out, |
| xi, yi, |
| width_in, height_in, depth_in, |
| width_weights, height_weights); |
| } |
| } |
| } |
| } |
| |
| return dst; |
| } |
| |
| template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info); |
| template SimpleTensor<half> convolution_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info); |
| template SimpleTensor<qint8_t> convolution_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info); |
| template SimpleTensor<qint16_t> convolution_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info); |
| template SimpleTensor<uint8_t> convolution_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &output_shape, |
| const PadStrideInfo &info); |
| } // namespace reference |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |