COMPMID-415: Move ConvolutionLayer to new validation
Change-Id: I1f40dff43142c4e2c096122bfa1ca08241ff80ff
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81952
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation_new/CPP/ConvolutionLayer.cpp b/tests/validation_new/CPP/ConvolutionLayer.cpp
new file mode 100644
index 0000000..a24621a
--- /dev/null
+++ b/tests/validation_new/CPP/ConvolutionLayer.cpp
@@ -0,0 +1,205 @@
+/*
+ * 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_new/FixedPoint.h"
+#include "tests/validation_new/Helpers.h"
+#include "tests/validation_new/half.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 std::enable_if<is_floating_point<T>::value, int>::type = 0>
+void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights, int fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ 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[offset_slice_in + xk + yk * width_in];
+ const T w_value = weights[idx + idy * width_weights + ifm * width_weights * height_weights];
+
+ acc += i_value * w_value;
+ }
+ }
+ }
+ }
+
+ // Accumulate the bias and store the result
+ *out = acc + (*bias);
+}
+
+// 3D convolution for fixed point type
+template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
+void convolution3d(const T *in, const T *weights, const T *bias, T *out, int xi, int yi, int width_in, int height_in, int depth_in, int width_weights, int height_weights,
+ int 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[offset_slice_in + xk + yk * width_in], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(weights[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(*bias, fixed_point_position, true);
+
+ // Accumulate the bias and covert back
+ acc = acc + b;
+ fixed_point<T> res(acc);
+ *out = res.raw();
+}
+} // namespace
+
+template <typename T>
+SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &output_shape, const PadStrideInfo &info)
+{
+ // Create reference
+ SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position() };
+
+ // 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_xi = std::min(static_cast<int>(info.pad().first), width_weights / 2);
+ const int pad_yi = std::min(static_cast<int>(info.pad().second), height_weights / 2);
+ const int start_xi = width_weights / 2 - pad_xi;
+ const int start_yi = height_weights / 2 - pad_yi;
+ const int end_xi = width_in - start_xi;
+ const int end_yi = height_in - start_yi;
+ 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 < end_yi; yi += stride_yi)
+ {
+ for(int xi = start_xi; 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;
+
+ // Compute 3D convolution
+ convolution3d(src.data() + offset_in,
+ weights.data() + ofm * width_weights * height_weights * depth_weights,
+ bias.data() + ofm,
+ dst.data() + offset_out,
+ xi, yi,
+ width_in, height_in, depth_in,
+ width_weights, height_weights,
+ src.fixed_point_position());
+ }
+ }
+ }
+ }
+
+ 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_float::half> convolution_layer(const SimpleTensor<half_float::half> &src, const SimpleTensor<half_float::half> &weights, const SimpleTensor<half_float::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);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute