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Diffstat (limited to 'tests/validation/reference/ConvolutionLayer.cpp')
| -rw-r--r-- | tests/validation/reference/ConvolutionLayer.cpp | 293 |
1 files changed, 293 insertions, 0 deletions
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp new file mode 100644 index 0000000000..10664119fb --- /dev/null +++ b/tests/validation/reference/ConvolutionLayer.cpp @@ -0,0 +1,293 @@ +/* + * 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 = 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 |