| /* |
| * 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_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE |
| #define ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE |
| |
| #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/Helpers.h" |
| #include "tests/validation/reference/BatchNormalizationLayer.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename TensorType, typename AccessorType, typename ConvolutionFunctionType, typename FusionFunctionType, typename T> |
| class BatchNormalizationLayerFusionValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| void setup(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, |
| bool use_conv_b, bool use_beta, bool use_gamma, float epsilon, DataType dt, DataLayout data_layout) |
| { |
| ARM_COMPUTE_UNUSED(dilation); |
| |
| _data_type = dt; |
| _data_layout = data_layout; |
| _use_conv_b = use_conv_b; |
| _use_beta = use_beta; |
| _use_gamma = use_gamma; |
| |
| _target = compute_target(src_shape, w_shape, b_shape, dst_shape, info, epsilon); |
| _reference = compute_reference(src_shape, w_shape, b_shape, dst_shape, info, epsilon); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&src, U &&w_tensor, U &&b_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor) |
| { |
| std::uniform_real_distribution<> distribution(-1.f, 1.f); |
| std::uniform_real_distribution<> distribution_gz(0, 1.f); |
| |
| library->fill(src, distribution, 0); |
| library->fill(w_tensor, distribution, 1); |
| library->fill(mean_tensor, distribution, 2); |
| library->fill(var_tensor, distribution_gz, 3); |
| _use_conv_b ? library->fill(b_tensor, distribution, 4) : library->fill_tensor_value(b_tensor, 0.f); |
| _use_beta ? library->fill(beta_tensor, distribution, 5) : library->fill_tensor_value(beta_tensor, 0.f); |
| _use_gamma ? library->fill(gamma_tensor, distribution, 6) : library->fill_tensor_value(gamma_tensor, 1.f); |
| } |
| |
| TensorType compute_target(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, float epsilon) |
| { |
| if(_data_layout == DataLayout::NHWC) |
| { |
| permute(src_shape, PermutationVector(2U, 0U, 1U)); |
| permute(w_shape, PermutationVector(2U, 0U, 1U)); |
| permute(dst_shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(src_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType conv_w = create_tensor<TensorType>(w_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType conv_b = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType bn_mean = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType bn_var = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType bn_beta = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType bn_gamma = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType fused_w = create_tensor<TensorType>(w_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType fused_b = create_tensor<TensorType>(b_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType dst = create_tensor<TensorType>(dst_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| |
| // Create and configure function |
| FusionFunctionType fuse_fn; |
| ConvolutionFunctionType conv_fn; |
| TensorType *conv_b_ptr = _use_conv_b ? &conv_b : nullptr; |
| TensorType *beta_ptr = _use_beta ? &bn_beta : nullptr; |
| TensorType *gamma_ptr = _use_gamma ? &bn_gamma : nullptr; |
| fuse_fn.configure(&conv_w, &bn_mean, &bn_var, &fused_w, &fused_b, conv_b_ptr, beta_ptr, gamma_ptr, epsilon); |
| conv_fn.configure(&src, &fused_w, &fused_b, &dst, info); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(conv_w.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(conv_b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bn_mean.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bn_var.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bn_beta.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bn_gamma.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(fused_w.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(fused_b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| conv_w.allocator()->allocate(); |
| conv_b.allocator()->allocate(); |
| bn_mean.allocator()->allocate(); |
| bn_var.allocator()->allocate(); |
| bn_beta.allocator()->allocate(); |
| bn_gamma.allocator()->allocate(); |
| fused_w.allocator()->allocate(); |
| fused_b.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!conv_w.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!conv_b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!bn_mean.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!bn_var.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!bn_beta.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!bn_gamma.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!fused_w.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!fused_b.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Fill tensors |
| fill(AccessorType(src), |
| AccessorType(conv_w), AccessorType(conv_b), |
| AccessorType(bn_mean), AccessorType(bn_var), AccessorType(bn_beta), AccessorType(bn_gamma)); |
| |
| // Compute function |
| fuse_fn.run(); |
| conv_fn.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference(TensorShape src_shape, TensorShape w_shape, TensorShape b_shape, TensorShape dst_shape, PadStrideInfo info, float epsilon) |
| { |
| // Create reference |
| SimpleTensor<T> src{ src_shape, _data_type, 1 }; |
| SimpleTensor<T> conv_w{ w_shape, _data_type, 1 }; |
| SimpleTensor<T> conv_b{ b_shape, _data_type, 1 }; |
| SimpleTensor<T> bn_var{ b_shape, _data_type, 1 }; |
| SimpleTensor<T> bn_mean{ b_shape, _data_type, 1 }; |
| SimpleTensor<T> bn_beta{ b_shape, _data_type, 1 }; |
| SimpleTensor<T> bn_gamma{ b_shape, _data_type, 1 }; |
| |
| // Fill reference |
| fill(src, conv_w, conv_b, bn_mean, bn_var, bn_beta, bn_gamma); |
| |
| // Calculate Conv + BN |
| auto conv_res = reference::convolution_layer(src, conv_w, conv_b, dst_shape, info); |
| return reference::batch_normalization_layer(conv_res, bn_mean, bn_var, bn_beta, bn_gamma, epsilon, ActivationLayerInfo()); |
| } |
| |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| DataType _data_type{}; |
| DataLayout _data_layout{}; |
| bool _use_conv_b{}; |
| bool _use_beta{}; |
| bool _use_gamma{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_BATCH_NORMALIZATION_LAYER_FUSION_FIXTURE */ |