COMPMID-594: Implement reference and CL/NEON validation for LocallyConnected
Change-Id: I01e7abcf3f1b19458128e277044af850ad9fa224
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118610
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/reference/Convolution3d.h b/tests/validation/reference/Convolution3d.h
new file mode 100644
index 0000000..b99d534
--- /dev/null
+++ b/tests/validation/reference/Convolution3d.h
@@ -0,0 +1,223 @@
+/*
+ * 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:
+ *asymm_int_mult
+ * 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, asymm_int_multDAMAGES 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.
+ */
+#ifndef __ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__
+#define __ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__
+
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/UtilsQuantizedAsymm.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace convolution_3d
+{
+namespace detail
+{
+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 < validation::is_floating_point<T>::value &&validation::is_floating_point<TB>::value, int >::type = 0 >
+inline 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_start = width_weights / 2;
+ const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+ const int half_height_weights_start = height_weights / 2;
+ const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+ // 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_start; yk <= half_height_weights_end; ++yk)
+ {
+ for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
+ const int idy = yk + half_height_weights_start;
+
+ 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 >
+inline 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_start = width_weights / 2;
+ const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+ const int half_height_weights_start = height_weights / 2;
+ const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+ 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_start; yk <= half_height_weights_end; ++yk)
+ {
+ for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
+ const int idy = yk + half_height_weights_start;
+
+ 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 <>
+inline 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_start = width_weights / 2;
+ const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
+ const int half_height_weights_start = height_weights / 2;
+ const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
+
+ // 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_start; yk <= half_height_weights_end; ++yk)
+ {
+ for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
+ const int idy = yk + half_height_weights_start;
+
+ 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 = validation::asymm_rounding_divide_by_pow2(validation::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 detail
+} // namespace convolution_3d
+} // namespace test
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_TEST_VALIDATION_CONVOLUTION_H__ */
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp
index b7ed2f5..24bbf32 100644
--- a/tests/validation/reference/ConvolutionLayer.cpp
+++ b/tests/validation/reference/ConvolutionLayer.cpp
@@ -25,6 +25,7 @@
#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Convolution3d.h"
#include "tests/validation/reference/Utils.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
@@ -42,185 +43,6 @@
{
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_start = width_weights / 2;
- const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
- const int half_height_weights_start = height_weights / 2;
- const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
- // 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_start; yk <= half_height_weights_end; ++yk)
- {
- for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
- const int idy = yk + half_height_weights_start;
-
- 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_start = width_weights / 2;
- const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
- const int half_height_weights_start = height_weights / 2;
- const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
- 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_start; yk <= half_height_weights_end; ++yk)
- {
- for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
- const int idy = yk + half_height_weights_start;
-
- 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_start = width_weights / 2;
- const int half_width_weights_end = ((width_weights % 2) == 0) ? (half_width_weights_start - 1) : half_width_weights_start;
- const int half_height_weights_start = height_weights / 2;
- const int half_height_weights_end = ((height_weights % 2) == 0) ? (half_height_weights_start - 1) : half_height_weights_start;
-
- // 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_start; yk <= half_height_weights_end; ++yk)
- {
- for(int xk = -half_width_weights_start; xk <= half_width_weights_end; ++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_start;
- const int idy = yk + half_height_weights_start;
-
- 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>
@@ -270,11 +92,11 @@
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);
+ convolution_3d::detail::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);
}
}
}
diff --git a/tests/validation/reference/LocallyConnected.cpp b/tests/validation/reference/LocallyConnected.cpp
new file mode 100644
index 0000000..08e3f02
--- /dev/null
+++ b/tests/validation/reference/LocallyConnected.cpp
@@ -0,0 +1,111 @@
+/*
+ * 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 "LocallyConnected.h"
+
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Convolution3d.h"
+#include "tests/validation/reference/Utils.h"
+
+#include "tests/framework/Asserts.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename TB>
+SimpleTensor<T> locally_connected(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 = info.pad_left();
+ const int pad_top = info.pad_top();
+ const int stride_xi = info.stride().first;
+ const int stride_yi = info.stride().second;
+
+ auto output_wh = scaled_dimensions(width_in, height_in, width_weights, height_weights, info);
+
+ const int start_xi = width_weights / 2 - pad_left;
+ const int start_yi = height_weights / 2 - pad_top;
+ const int end_xi = output_wh.first * stride_xi;
+ const int end_yi = output_wh.second * stride_yi;
+ const int num_batches = src.shape().total_size() / (width_in * height_in * depth_in);
+
+ for(int r = 0; r < num_batches; ++r)
+ {
+ int count = 0;
+ 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
+ convolution_3d::detail::convolution3d(src, weights, bias, dst,
+ offset_in, count * width_weights * height_weights * depth_weights, count, offset_out,
+ xi, yi,
+ width_in, height_in, depth_in,
+ width_weights, height_weights);
+ count++;
+ }
+ }
+ }
+ }
+
+ return dst;
+}
+
+// Locally Connected only supports F32
+template SimpleTensor<float> locally_connected(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
+ const PadStrideInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/reference/LocallyConnected.h b/tests/validation/reference/LocallyConnected.h
new file mode 100644
index 0000000..bf78d2c
--- /dev/null
+++ b/tests/validation/reference/LocallyConnected.h
@@ -0,0 +1,44 @@
+/*
+ * 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.
+ */
+#ifndef __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__
+#define __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__
+
+#include "tests/SimpleTensor.h"
+#include "tests/validation/Helpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+template <typename T, typename TB>
+SimpleTensor<T> locally_connected(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_TEST_LOCALLY_CONNECTED_H__ */