COMPMID-355 Implement 3x3 CL direct convolution

Change-Id: I1b44dc375045964e65557f0ead57a7c12d6bf097
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81418
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 264 insertions, 0 deletions

diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp
new file mode 100644
index 0000000000..034a8b2045
--- /dev/null
+++ b/tests/validation/NEON/DirectConvolutionLayer.cpp
@@ -0,0 +1,264 @@
+/*
+ * 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 "AssetsLibrary.h"
+#include "Globals.h"
+#include "NEON/Accessor.h"
+#include "TypePrinter.h"
+#include "Utils.h"
+#include "validation/Datasets.h"
+#include "validation/Reference.h"
+#include "validation/Validation.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+
+#include "boost_wrapper.h"
+
+#include <random>
+#include <string>
+#include <tuple>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+using namespace arm_compute::test::validation;
+
+namespace
+{
+const float tolerance_fp32 = 1e-3f; /**< Tolerance for floating point tests */
+#ifdef ARM_COMPUTE_ENABLE_FP16
+const float tolerance_fp16 = 0.01f; /**< Tolerance for half precision floating point tests */
+#endif                              /* ARM_COMPUTE_ENABLE_FP16 */
+const float tolerance_qs8 = 1;      /**< Tolerance for fixed point tests */
+
+/** Compute NEON direct convolution layer function.
+ *
+ * @param[in] src_shape            Shape of the input tensor.
+ * @param[in] weights_shape        Shape of the weights.
+ * @param[in] bias_shape           Shape of the bias tensor.
+ * @param[in] dst_shape            Shape of the output tensor.
+ * @param[in] dt                   Data type of input, convolution matrix and output tensors.
+ * @param[in] conv_info            Padding and stride information.
+ * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers
+ *
+ * @return Computed output tensor.
+*/
+Tensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape,
+                                 DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0)
+{
+    // Create tensors
+    Tensor src     = create_tensor<Tensor>(src_shape, dt, 1, fixed_point_position);
+    Tensor weights = create_tensor<Tensor>(weights_shape, dt, 1, fixed_point_position);
+    Tensor bias    = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position);
+    Tensor dst     = create_tensor<Tensor>(dst_shape, dt, 1, fixed_point_position);
+
+    // Create and configure function
+    NEDirectConvolutionLayer conv_layer;
+    conv_layer.configure(&src, &weights, &bias, &dst, conv_info);
+
+    // Allocate tensors
+    src.allocator()->allocate();
+    weights.allocator()->allocate();
+    bias.allocator()->allocate();
+    dst.allocator()->allocate();
+
+    BOOST_TEST(!src.info()->is_resizable());
+    BOOST_TEST(!weights.info()->is_resizable());
+    BOOST_TEST(!bias.info()->is_resizable());
+    BOOST_TEST(!dst.info()->is_resizable());
+
+    // Fill tensors
+    if(dt == DataType::F16 || dt == DataType::F32)
+    {
+        std::uniform_real_distribution<> distribution(-1.f, 1.f);
+        library->fill(Accessor(src), distribution, 0);
+        library->fill(Accessor(weights), distribution, 1);
+        library->fill(Accessor(bias), distribution, 2);
+    }
+    else
+    {
+        library->fill_tensor_uniform(Accessor(src), 0);
+        library->fill_tensor_uniform(Accessor(weights), 1);
+        library->fill_tensor_uniform(Accessor(bias), 2);
+    }
+
+    // Compute function
+    conv_layer.run();
+
+    return dst;
+}
+
+TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info)
+{
+    TensorShape out_shape(in_shape);
+    const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
+                                                                                             in_shape.y(),
+                                                                                             kernel_shape.x(),
+                                                                                             kernel_shape.y(),
+                                                                                             conv_info);
+    out_shape.set(0, scaled_dims.first);
+    out_shape.set(1, scaled_dims.second);
+    out_shape.set(2, kernel_shape[3]);
+    return out_shape;
+}
+
+} // namespace
+
+#ifndef DOXYGEN_SKIP_THIS
+BOOST_AUTO_TEST_SUITE(NEON)
+BOOST_AUTO_TEST_SUITE(ConvolutionLayer)
+BOOST_AUTO_TEST_SUITE(Direct)
+
+#ifdef ARM_COMPUTE_ENABLE_FP16
+BOOST_AUTO_TEST_SUITE(Float16)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W1x1,
+                     DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+                     input_shape, dt, sx, sy, num_kernels)
+{
+    const unsigned int  kernel_size = 1;
+    const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+    // Validate output
+    validate(NEAccessor(dst), ref);
+}
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::make(DataType::F16) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3,
+                     1)
+                     * boost::unit_test::data::xrange(0, 2,
+                                                      1)
+                     * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+                     input_shape, dt, sx, sy, px, py, num_kernels)
+{
+    const unsigned int  kernel_size = 3;
+    const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+    // Validate output
+    validate(NEAccessor(dst), ref, tolerance_fp16);
+}
+BOOST_AUTO_TEST_SUITE_END()
+#endif /* ARM_COMPUTE_ENABLE_FP16 */
+
+BOOST_AUTO_TEST_SUITE(Float)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W1x1,
+                     DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+                     input_shape, dt, sx, sy, num_kernels)
+{
+    const unsigned int  kernel_size = 1;
+    const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+    // Validate output
+    validate(Accessor(dst), ref);
+}
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2,
+                     1)
+                     * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+                     input_shape, dt, sx, sy, px, py, num_kernels)
+{
+    const unsigned int  kernel_size = 3;
+    const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+    // Validate output
+    validate(Accessor(dst), ref, tolerance_fp32);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE(Quantized)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W1x1,
+                     DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }),
+                     input_shape, sx, sy, num_kernels, fixed_point_position)
+{
+    const unsigned int  kernel_size = 1;
+    const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+    // Validate output
+    validate(Accessor(dst), ref);
+}
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, 1)
+                     * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }),
+                     input_shape, sx, sy, px, py, num_kernels, fixed_point_position)
+{
+    const unsigned int  kernel_size = 3;
+    const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
+    const TensorShape   w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+    const TensorShape   b_shape(static_cast<unsigned int>(num_kernels));
+    const TensorShape   d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+    Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+    RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+    // Validate output
+    validate(Accessor(dst), ref, tolerance_qs8);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
+#endif /* DOXYGEN_SKIP_THIS */