COMPMID-1933: Implement NEHeightConcatenateLayer.
Added support to concactenate tensors along the Y axis in NEConcatenateLayer.
Change-Id: Ib714bfcf9954cc35918efa7d52fc9164bb08bdf6
Signed-off-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/841
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/fixtures/ConcatenateLayerFixture.h b/tests/validation/fixtures/ConcatenateLayerFixture.h
new file mode 100644
index 0000000..db09957
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+++ b/tests/validation/fixtures/ConcatenateLayerFixture.h
@@ -0,0 +1,183 @@
+/*
+ * Copyright (c) 2018-2019 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_WIDTHCONCATENATE_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ConcatenateLayer.h"
+
+#include <random>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename ITensorType, typename AccessorType, typename FunctionType, typename T>
+class ConcatenateLayerValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape, DataType data_type, unsigned int axis)
+ {
+ // Create input shapes
+ std::mt19937 gen(library->seed());
+ std::uniform_int_distribution<> num_dis(2, 8);
+ std::uniform_int_distribution<> offset_dis(0, 20);
+
+ const int num_tensors = num_dis(gen);
+
+ std::vector<TensorShape> shapes(num_tensors, shape);
+
+ // vector holding the quantization info:
+ // the last element is the output quantization info
+ // all other elements are the quantization info for the input tensors
+ std::vector<QuantizationInfo> qinfo(num_tensors + 1, QuantizationInfo());
+ for(auto &qi : qinfo)
+ {
+ qi = QuantizationInfo(1.f / 255.f, offset_dis(gen));
+ }
+ std::bernoulli_distribution mutate_dis(0.5f);
+ std::uniform_real_distribution<> change_dis(-0.25f, 0.f);
+
+ // Generate more shapes based on the input
+ for(auto &s : shapes)
+ {
+ // Randomly change the first dimension
+ if(mutate_dis(gen))
+ {
+ // Decrease the dimension by a small percentage. Don't increase
+ // as that could make tensor too large.
+ s.set(axis, s[axis] + 2 * static_cast<int>(s[axis] * change_dis(gen)));
+ }
+ }
+
+ _target = compute_target(shapes, qinfo, data_type, axis);
+ _reference = compute_reference(shapes, qinfo, data_type, axis);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ library->fill_tensor_uniform(tensor, i);
+ }
+
+ TensorType compute_target(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
+ {
+ std::vector<TensorType> srcs;
+ std::vector<ITensorType *> src_ptrs;
+
+ // Create tensors
+ srcs.reserve(shapes.size());
+
+ for(size_t j = 0; j < shapes.size(); ++j)
+ {
+ srcs.emplace_back(create_tensor<TensorType>(shapes[j], data_type, 1, qinfo[j]));
+ src_ptrs.emplace_back(&srcs.back());
+ }
+
+ const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis);
+ TensorType dst = create_tensor<TensorType>(dst_shape, data_type, 1, qinfo[shapes.size()]);
+
+ // Create and configure function
+ FunctionType concat;
+ switch(axis)
+ {
+ case 0:
+ concat.configure(src_ptrs, &dst, DataLayoutDimension::WIDTH);
+ break;
+ case 1:
+ concat.configure(src_ptrs, &dst, DataLayoutDimension::HEIGHT);
+ break;
+ case 2:
+ concat.configure(src_ptrs, &dst, DataLayoutDimension::CHANNEL);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported");
+ break;
+ }
+
+ for(auto &src : srcs)
+ {
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ }
+
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ for(auto &src : srcs)
+ {
+ src.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ }
+
+ dst.allocator()->allocate();
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ int i = 0;
+ for(auto &src : srcs)
+ {
+ fill(AccessorType(src), i++);
+ }
+
+ // Compute function
+ concat.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
+ {
+ std::vector<SimpleTensor<T>> srcs;
+
+ // Create and fill tensors
+ for(size_t j = 0; j < shapes.size(); ++j)
+ {
+ srcs.emplace_back(shapes[j], data_type, 1, qinfo[j]);
+ fill(srcs.back(), j);
+ }
+
+ const TensorShape dst_shape = calculate_concatenate_shape(shapes, axis);
+ SimpleTensor<T> dst{ dst_shape, data_type, 1, qinfo[shapes.size()] };
+ return reference::concatenate_layer<T>(srcs, dst, axis);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE */