From 761c8d02ff875877db7aa7c850cf8d128592e822 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Mon, 10 Jun 2019 14:46:49 +0100 Subject: COMPMID-2398: Add test for CLFuseBatchNormalizationLayer Change-Id: I786df628ce15fc33fc42c9437fe82972e02e3b16 Signed-off-by: Gian Marco Iodice Reviewed-on: https://review.mlplatform.org/c/1317 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- .../fixtures/FuseBatchNormalizationFixture.h | 204 +++++++++++++++++++++ 1 file changed, 204 insertions(+) create mode 100644 tests/validation/fixtures/FuseBatchNormalizationFixture.h (limited to 'tests/validation/fixtures/FuseBatchNormalizationFixture.h') diff --git a/tests/validation/fixtures/FuseBatchNormalizationFixture.h b/tests/validation/fixtures/FuseBatchNormalizationFixture.h new file mode 100644 index 0000000000..864d627ed7 --- /dev/null +++ b/tests/validation/fixtures/FuseBatchNormalizationFixture.h @@ -0,0 +1,204 @@ +/* + * Copyright (c) 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_FUSEBATCHNORMALIZATION_FIXTURE +#define ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_FIXTURE + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/functions/CLFuseBatchNormalization.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/FuseBatchNormalization.h" + +#include +#include + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template +class FuseBatchNormalizationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta) + { + std::tie(_target_w, _target_b) = compute_target(shape_w, data_type, data_layout, in_place, with_bias, with_gamma, with_beta); + std::tie(_reference_w, _reference_b) = compute_reference(shape_w, data_type, data_layout, with_bias, with_gamma, with_beta); + } + +protected: + template + void fill(U &&tensor, int i, float min, float max) + { + library->fill_tensor_uniform(tensor, i, min, max); + } + + std::pair compute_target(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool in_place, bool with_bias, bool with_gamma, bool with_beta) + { + const TensorShape shape_v(shape_w[dims_weights - 1]); + + if(data_layout == DataLayout::NHWC) + { + permute(shape_w, PermutationVector(2U, 0U, 1U)); + } + + const bool in_place_w = in_place; + const bool in_place_b = with_bias ? in_place : false; + + // Create tensors + TensorType w = create_tensor(shape_w, data_type, 1, QuantizationInfo(), data_layout); + TensorType b = create_tensor(shape_v, data_type); + TensorType mean = create_tensor(shape_v, data_type); + TensorType var = create_tensor(shape_v, data_type); + TensorType w_fused = create_tensor(shape_w, data_type, 1, QuantizationInfo(), data_layout); + TensorType b_fused = create_tensor(shape_v, data_type); + TensorType beta = create_tensor(shape_v, data_type); + TensorType gamma = create_tensor(shape_v, data_type); + + auto b_to_use = with_bias ? &b : nullptr; + auto gamma_to_use = with_gamma ? &gamma : nullptr; + auto beta_to_use = with_beta ? &beta : nullptr; + auto w_fused_to_use = in_place_w ? nullptr : &w_fused; + auto b_fused_to_use = in_place_b ? nullptr : &b_fused; + + // Create and configure function + FunctionType fuse_batch_normalization; + fuse_batch_normalization.configure(&w, &mean, &var, w_fused_to_use, b_fused_to_use, b_to_use, beta_to_use, gamma_to_use, _epsilon); + + ARM_COMPUTE_EXPECT(w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(mean.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(var.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(w_fused.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(b_fused.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(beta.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(gamma.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Allocate tensors + w.allocator()->allocate(); + b.allocator()->allocate(); + mean.allocator()->allocate(); + var.allocator()->allocate(); + w_fused.allocator()->allocate(); + b_fused.allocator()->allocate(); + beta.allocator()->allocate(); + gamma.allocator()->allocate(); + + ARM_COMPUTE_EXPECT(!w.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!mean.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!var.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!w_fused.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!b_fused.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!beta.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(!gamma.info()->is_resizable(), framework::LogLevel::ERRORS); + + // Fill tensors + fill(AccessorType(w), 0U, -1.0f, 1.0f); + fill(AccessorType(b), 1U, -1.0f, 1.0f); + fill(AccessorType(mean), 2U, -1.0f, 1.0f); + fill(AccessorType(var), 3U, 0.0f, 1.0f); + fill(AccessorType(beta), 4U, -1.0f, 1.0f); + fill(AccessorType(gamma), 5U, -1.0f, 1.0f); + + // Compute function + fuse_batch_normalization.run(); + + return std::make_pair(std::move(in_place_w ? w : w_fused), std::move(in_place_b ? b : b_fused)); + } + + std::pair, SimpleTensor> compute_reference(TensorShape shape_w, DataType data_type, DataLayout data_layout, bool with_bias, bool with_gamma, bool with_beta) + { + const TensorShape shape_v(shape_w[dims_weights - 1]); + + SimpleTensor w{ shape_w, data_type }; + SimpleTensor b{ shape_v, data_type }; + SimpleTensor mean{ shape_v, data_type }; + SimpleTensor var{ shape_v, data_type }; + SimpleTensor w_fused{ shape_w, data_type }; + SimpleTensor b_fused{ shape_v, data_type }; + SimpleTensor beta{ shape_v, data_type }; + SimpleTensor gamma{ shape_v, data_type }; + + // Fill reference tensor + fill(w, 0U, -1.0f, 1.0f); + fill(b, 1U, -1.0f, 1.0f); + fill(mean, 2U, -1.0f, 1.0f); + fill(var, 3U, 0.0f, 1.0f); + fill(beta, 4U, -1.0f, 1.0f); + fill(gamma, 5U, -1.0f, 1.0f); + + if(!with_bias) + { + // Fill with zeros + fill(b, 0U, 0.0f, 0.0f); + } + + if(!with_gamma) + { + // Fill with ones + fill(gamma, 0U, 1.0f, 1.0f); + } + + if(!with_beta) + { + // Fill with zeros + fill(beta, 0U, 0.0f, 0.0f); + } + + switch(dims_weights) + { + case 3: + // Weights for depth wise convolution layer + reference::fuse_batch_normalization_dwc_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon); + break; + case 4: + // Weights for convolution layer + reference::fuse_batch_normalization_conv_layer(w, mean, var, w_fused, b_fused, b, beta, gamma, _epsilon); + break; + default: + ARM_COMPUTE_ERROR("Not supported number of dimensions for the input weights tensor"); + } + + return std::make_pair(std::move(w_fused), std::move(b_fused)); + } + + const float _epsilon{ 0.0001f }; + TensorType _target_w{}; + TensorType _target_b{}; + SimpleTensor _reference_w{}; + SimpleTensor _reference_b{}; +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_FUSEBATCHNORMALIZATION_FIXTURE */ -- cgit v1.2.1