COMPMID-470: Neon Deconvolution.
Implemented by up-sampling the input with zeros insertions between the input samples and convolving the Deconvolution kernels on the up-sampled result.
The upsampling is performed by the function NEDeconvolutionLayerUpsample.
Convolving is done by NEDirectConvolutionLayer.
Change-Id: I25f7ba7c6b99cd9310797972ede40aeff4a54900
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/85319
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/fixtures/DeconvolutionLayerFixture.h b/tests/validation/fixtures/DeconvolutionLayerFixture.h
new file mode 100644
index 0000000..8dff97d
--- /dev/null
+++ b/tests/validation/fixtures/DeconvolutionLayerFixture.h
@@ -0,0 +1,168 @@
+/*
+ * 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 "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.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/CPP/DeconvolutionLayer.h"
+#include "tests/validation/Helpers.h"
+
+#include <random>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class DeconvolutionLayerFixtureBase : public framework::Fixture
+{
+public:
+ /*
+ *
+ * @param[in] a The number of zeros added to right and bottom edges of the input.
+ * @param[in] u How much to scale the X and Y axis.
+ */
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info,
+ const std::pair<unsigned int, unsigned int> &a, const std::pair<unsigned int, unsigned int> &u, DataType data_type, int fractional_bits)
+ {
+ _fractional_bits = fractional_bits;
+ _data_type = data_type;
+
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, a, u, data_type, fractional_bits);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, a, data_type, fractional_bits);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ switch(tensor.data_type())
+ {
+ case DataType::F32:
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ break;
+ }
+ default:
+ library->fill_tensor_uniform(tensor, i);
+ }
+ }
+ /*
+ *
+ * @param[in] a The number of zeros added to right and bottom edges of the input.
+ * @param[in] u How much to scale the X and Y axis.
+ */
+ TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> &a, const std::pair<float, float> &u, DataType data_type, int fixed_point_position)
+ {
+ // Create tensors
+ TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
+ TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1, fixed_point_position);
+ TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
+ TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
+
+ // Create and configure function
+ FunctionType conv;
+ conv.configure(&src, &weights, &bias, &dst, info, a.first, a.second, u.first, u.second);
+
+ ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ src.allocator()->allocate();
+ weights.allocator()->allocate();
+ bias.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(src), 0);
+ fill(AccessorType(weights), 1);
+ fill(AccessorType(bias), 2);
+
+ // Compute NEConvolutionLayer function
+ conv.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
+ const PadStrideInfo &info, const std::pair<unsigned int, unsigned int> a, DataType data_type, int fixed_point_position)
+ {
+ // Create reference
+ SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
+ SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
+ SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
+
+ // Fill reference
+ fill(src, 0);
+ fill(weights, 1);
+ fill(bias, 2);
+
+ return reference::deconvolution_layer<T>(src, weights, bias, output_shape, info, a);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ int _fractional_bits{};
+ DataType _data_type{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, unsigned int kernel_size_x, unsigned int kernel_size_y>
+class DeconvolutionValidationFixture : public DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, unsigned int sx, unsigned int sy, unsigned int padx, unsigned int pady,
+ unsigned int ax, unsigned int ay, unsigned int ux, unsigned int uy, unsigned int num_kernels, DataType data_type)
+ {
+ ARM_COMPUTE_ERROR_ON_MSG(kernel_size_x != kernel_size_y, "Only square kernels supported");
+ const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
+ const TensorShape bias_shape(num_kernels);
+ const PadStrideInfo info(sx, sy, padx, pady, DimensionRoundingType::CEIL);
+ const std::pair<unsigned int, unsigned int> a(ax, ay);
+ const std::pair<float, float> u(ux, uy);
+ auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, padx, pady, a.first, a.second, u.first, u.second,
+ DimensionRoundingType::CEIL);
+ TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape);
+ DeconvolutionLayerFixtureBase<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, a, u, data_type, 0);
+ }
+};
+
+} // namespace validation
+} // namespace test
+} // namespace arm_compute