| /* |
| * Copyright (c) 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_RNN_LAYER_FIXTURE |
| #define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE |
| |
| #include "tests/Globals.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/reference/ActivationLayer.h" |
| #include "tests/validation/reference/ArithmeticOperations.h" |
| #include "tests/validation/reference/FullyConnectedLayer.h" |
| #include "tests/validation/reference/GEMM.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> |
| class RNNLayerValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info, |
| DataType data_type) |
| { |
| _target = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); |
| _reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, |
| ActivationLayerInfo info, DataType data_type) |
| { |
| // Create tensors |
| TensorType input = create_tensor<TensorType>(input_shape, data_type); |
| TensorType weights = create_tensor<TensorType>(weights_shape, data_type); |
| TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type); |
| TensorType bias = create_tensor<TensorType>(bias_shape, data_type); |
| TensorType hidden_state = create_tensor<TensorType>(output_shape, data_type); |
| TensorType output = create_tensor<TensorType>(output_shape, data_type); |
| |
| // Create and configure function |
| FunctionType rnn; |
| rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info); |
| |
| ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| input.allocator()->allocate(); |
| weights.allocator()->allocate(); |
| recurrent_weights.allocator()->allocate(); |
| bias.allocator()->allocate(); |
| hidden_state.allocator()->allocate(); |
| output.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(input), 0); |
| fill(AccessorType(weights), 0); |
| fill(AccessorType(recurrent_weights), 0); |
| fill(AccessorType(bias), 0); |
| fill(AccessorType(hidden_state), 0); |
| |
| // Compute function |
| rnn.run(); |
| |
| return output; |
| } |
| |
| SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, |
| const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type) |
| { |
| // Create reference |
| SimpleTensor<T> input{ input_shape, data_type }; |
| SimpleTensor<T> weights{ weights_shape, data_type }; |
| SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type }; |
| SimpleTensor<T> bias{ bias_shape, data_type }; |
| SimpleTensor<T> hidden_state{ output_shape, data_type }; |
| |
| // Fill reference |
| fill(input, 0); |
| fill(weights, 0); |
| fill(recurrent_weights, 0); |
| fill(bias, 0); |
| fill(hidden_state, 0); |
| |
| TensorShape out_shape = recurrent_weights_shape; |
| out_shape.set(1, output_shape.y()); |
| |
| // Compute reference |
| SimpleTensor<T> out_w{ out_shape, data_type }; |
| SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape); |
| SimpleTensor<T> gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f); |
| SimpleTensor<T> add_res = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected, gemm, data_type, ConvertPolicy::SATURATE); |
| return reference::activation_layer(add_res, info); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */ |