arm_compute v17.12
diff --git a/tests/validation/reference/FullyConnectedLayer.cpp b/tests/validation/reference/FullyConnectedLayer.cpp
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+++ b/tests/validation/reference/FullyConnectedLayer.cpp
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+/*
+ * 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 "FullyConnectedLayer.h"
+
+#include "arm_compute/core/Types.h"
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/reference/UtilsQuantizedAsymm.h"
+
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include <numeric>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+// Vector matrix multiply for floating point
+template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 >
+void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
+ int rows_weights, uint8_t fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ const T *src_ptr = src.data() + offset_src;
+ const T *weights_ptr = weights.data();
+ const TB *bias_ptr = bias.data();
+ T *dst_ptr = dst.data() + offset_dst;
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast<T>(0)) + bias_ptr[y];
+ weights_ptr += cols_weights;
+ }
+}
+
+// Vector matrix multiply 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 vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
+ int rows_weights, uint8_t fixed_point_position)
+{
+ const T *src_ptr = src.data() + offset_src;
+ const T *weights_ptr = weights.data();
+ const TB *bias_ptr = bias.data();
+ T *dst_ptr = dst.data() + offset_dst;
+
+ using namespace fixed_point_arithmetic;
+ using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ // Reset accumulator
+ fixed_point<promoted_type> acc(0, fixed_point_position);
+
+ for(int x = 0; x < cols_weights; ++x)
+ {
+ const fixed_point<promoted_type> i_value(src_ptr[x], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(weights_ptr[x], fixed_point_position, true);
+ acc = acc + i_value * w_value;
+ }
+
+ // Get the bias
+ const fixed_point<T> b(bias_ptr[y], fixed_point_position, true);
+
+ // Convert back and accumulate the bias
+ fixed_point<T> res(acc);
+ res = res + b;
+
+ // Store the result
+ dst_ptr[y] = res.raw();
+
+ weights_ptr += cols_weights;
+ }
+}
+
+// Vector matrix multiply for quantized type
+template <>
+void vector_matrix_multiply(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, int offset_src, int offset_dst,
+ int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ const uint8_t *src_ptr = src.data() + offset_src;
+ const uint8_t *weights_ptr = weights.data();
+ const int32_t *bias_ptr = bias.data();
+ uint8_t *dst_ptr = dst.data() + offset_dst;
+
+ 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 = 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);
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ // Reset accumulator
+ int32_t acc = 0;
+
+ for(int x = 0; x < cols_weights; ++x)
+ {
+ acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset);
+ }
+
+ // Accumulate the bias
+ acc += bias_ptr[y];
+
+ 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
+ dst_ptr[y] = static_cast<uint8_t>(acc);
+
+ weights_ptr += cols_weights;
+ }
+}
+} // namespace
+
+template <typename T, typename TB>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape)
+{
+ // Create reference
+ SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
+
+ // Sanity checks
+ const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
+ const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions;
+ const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions);
+
+ ARM_COMPUTE_UNUSED(num_batch_dimensions);
+ ARM_COMPUTE_UNUSED(num_input_dimensions);
+ ARM_COMPUTE_UNUSED(linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size);
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x());
+ ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x());
+
+ // Compute reference
+ const int cols_weights = weights.shape().x();
+ const int rows_weights = weights.shape().y();
+ const int num_batches = dst_shape.total_size_upper(1);
+
+ for(int k = 0; k < num_batches; ++k)
+ {
+ const int offset_in = k * cols_weights;
+ const int offset_out = k * rows_weights;
+
+ vector_matrix_multiply<T>(src,
+ weights,
+ bias,
+ dst,
+ offset_in,
+ offset_out,
+ cols_weights,
+ rows_weights,
+ src.fixed_point_position());
+ }
+
+ return dst;
+}
+
+template SimpleTensor<float> fully_connected_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &dst_shape);
+template SimpleTensor<half> fully_connected_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &dst_shape);
+template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape);
+template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape);
+template SimpleTensor<uint8_t> fully_connected_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute