diff options
Diffstat (limited to 'tests/validation/fixtures/WinogradConvolutionLayerFixture.h')
| -rw-r--r-- | tests/validation/fixtures/WinogradConvolutionLayerFixture.h | 331 |
1 files changed, 172 insertions, 159 deletions
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h index 9c2df9ef4b..20b678b36c 100644 --- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h +++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2021, 2023-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE -#define ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE +#ifndef ACL_TESTS_VALIDATION_FIXTURES_WINOGRADCONVOLUTIONLAYERFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_WINOGRADCONVOLUTIONLAYERFIXTURE_H #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" @@ -51,125 +51,36 @@ namespace validation { using namespace arm_compute::misc::shape_calculator; -template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool use_bias = true> -class WinogradConvolutionLayerValidationFixture : public framework::Fixture +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename T1 = T, bool use_bias = true, bool mixed_layout = false> +class WinogradConvolutionLayerFastMathValidationFixture : public framework::Fixture { public: - template <typename...> void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, - DataType data_type, ActivationLayerInfo act_info) + DataType data_type, ActivationLayerInfo act_info, const DataLayout &data_layout) + { ARM_COMPUTE_UNUSED(dilation); - - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); - _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info); + _mixed_layout = mixed_layout; + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info, data_layout); + _reference = compute_reference(input_shape, weights_shape, bias_shape, info, data_type, act_info); } protected: - template <typename U> - void fill(U &&tensor, int i, float min, float max) + void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) { - switch(tensor.data_type()) - { - case DataType::F16: - case DataType::F32: - { - std::uniform_real_distribution<> distribution(min, max); - library->fill(tensor, distribution, i); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - } - } - } - - TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, - DataType data_type, ActivationLayerInfo act_info) - { - // Create tensors - TensorType src = create_tensor<TensorType>(input_shape, data_type, 1); - TensorType weights = create_tensor<TensorType>(weights_shape, data_type, 1); - TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1); - TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1); - - // Create and configure function - FunctionType conv; - ARM_COMPUTE_EXPECT(static_cast<bool>(conv.validate(src.info(), weights.info(), (use_bias) ? bias.info() : nullptr, dst.info(), info, act_info)), framework::LogLevel::ERRORS); - conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info); + const DataLayout data_layout = src.info()->data_layout(); + // Test Multi DataLayout graph cases, when the data layout changes after configure + src.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + dst.info()->set_data_layout(data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); - 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(); - dst.allocator()->allocate(); - bias.allocator()->allocate(); + // Compute Convolution function + layer.run(); - 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, -1.f, 1.f); - fill(AccessorType(weights), 1, -1.f, 1.f); - fill(AccessorType(bias), 2, -1.f, 1.f); - - // Compute Winograd Convolution 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, - DataType data_type, ActivationLayerInfo act_info) - { - // Create reference - SimpleTensor<T> src{ input_shape, data_type, 1 }; - SimpleTensor<T> weights{ weights_shape, data_type, 1 }; - SimpleTensor<T> bias{ bias_shape, data_type, 1 }; - - // Fill reference - fill(src, 0, -1.f, 1.f); - fill(weights, 1, -1.f, 1.f); - if(use_bias) - { - fill(bias, 2, -1.f, 1.f); - } - else - { - fill(bias, 2, 0.f, 0.f); - } - - SimpleTensor<T> conv_out = reference::convolution_layer<T>(src, weights, bias, output_shape, info); - - return (act_info.enabled()) ? reference::activation_layer<T>(conv_out, act_info) : conv_out; + // Reinstating original data layout for the test suite to properly check the values + src.info()->set_data_layout(data_layout); + dst.info()->set_data_layout(data_layout); } - TensorType _target{}; - SimpleTensor<T> _reference{}; -}; - -template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename T1 = T, bool use_bias = true> -class WinogradConvolutionLayerFastMathValidationFixture : public framework::Fixture -{ -public: - template <typename...> - void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, - DataType data_type, ActivationLayerInfo act_info, const DataLayout &data_layout) - - { - ARM_COMPUTE_UNUSED(dilation); - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info, data_layout); - _reference = compute_reference(input_shape, weights_shape, bias_shape, info, data_type, act_info); - } - -protected: template <typename U> void fill(U &&tensor, int i, float min, float max) { @@ -177,13 +88,13 @@ protected: { case DataType::F16: { - arm_compute::utils::uniform_real_distribution_fp16 distribution((half)min, (half)max); + arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(min), float(max) }; library->fill(tensor, distribution, i); break; } case DataType::F32: { - std::uniform_real_distribution<> distribution(min, max); + std::uniform_real_distribution<float> distribution(min, max); library->fill(tensor, distribution, i); break; } @@ -216,10 +127,12 @@ protected: framework::LogLevel::ERRORS); conv.configure(&src, &weights, (use_bias) ? &bias : nullptr, &dst, info, act_info, true /* Enable fast math */); - 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); + ARM_COMPUTE_ASSERT(src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(weights.info()->is_resizable()); + ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + add_padding_x({ &src, &weights, &bias, &dst }, data_layout); // Allocate tensors src.allocator()->allocate(); @@ -227,19 +140,25 @@ protected: dst.allocator()->allocate(); bias.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); + ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!weights.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(src), 0, -0.5f, 0.5f); fill(AccessorType(weights), 1, -0.5f, 0.5f); fill(AccessorType(bias), 2, -0.5f, 0.5f); - // Compute Winograd Convolution function - conv.run(); - + if(_mixed_layout) + { + mix_layout(conv, src, dst); + } + else + { + // Compute function + conv.run(); + } return dst; } @@ -310,37 +229,59 @@ protected: SimpleTensor<T1> filter_transform_out = reference::winograd_filter_transform<T1>(weights_t1, filter_transform_shape, winograd_info); SimpleTensor<T1> batched_gemm = reference::gemm<T1>(input_transform_out, filter_transform_out, dummy_c, 1.0f, 0.0f); SimpleTensor<T1> conv_out = reference::winograd_output_transform<T1>(batched_gemm, bias_t1, output_transform_shape, winograd_info); - SimpleTensor<T> conv_out_t(std::move(copy_tensor<T, T1>(conv_out))); + SimpleTensor<T> conv_out_t(copy_tensor<T, T1>(conv_out)); return (act_info.enabled()) ? reference::activation_layer<T>(conv_out_t, act_info) : conv_out_t; } TensorType _target{}; SimpleTensor<T> _reference{}; + bool _mixed_layout{ false }; }; -template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> class WinogradInputTransformValidationFixture : public framework::Fixture { public: - template <typename...> void setup(TensorShape input_shape, WinogradInfo winograd_info, DataLayout data_layout, DataType data_type) { TensorShape output_shape = compute_winograd_input_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); - - _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); - _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); + _mixed_layout = mixed_layout; + _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); + _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); } protected: + void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) + { + const DataLayout data_layout_src = src.info()->data_layout(); + const DataLayout data_layout_dst = dst.info()->data_layout(); + + // Test Multi DataLayout graph cases, when the data layout changes after configure + src.info()->set_data_layout(data_layout_src == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + dst.info()->set_data_layout(data_layout_dst == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + + // Compute Convolution function + layer.run(); + + // Reinstating original data layout for the test suite to properly check the values + src.info()->set_data_layout(data_layout_src); + dst.info()->set_data_layout(data_layout_dst); + } + template <typename U> void fill(U &&tensor, int i, float min, float max) { switch(tensor.data_type()) { case DataType::F16: + { + arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(min), float(max) }; + library->fill(tensor, distribution, i); + break; + } case DataType::F32: { - std::uniform_real_distribution<> distribution(min, max); + std::uniform_real_distribution<float> distribution(min, max); library->fill(tensor, distribution, i); break; } @@ -365,22 +306,30 @@ protected: FunctionType transf; transf.configure(&src, &dst, winograd_info); - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + add_padding_x({ &src, &dst }, data_layout); // Allocate tensors src.allocator()->allocate(); dst.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(src), 0, -1.f, 1.f); - // Compute Winograd input transform function - transf.run(); - + if(_mixed_layout) + { + mix_layout(transf, src, dst); + } + else + { + // Compute Winograd input transform function + transf.run(); + } return dst; } @@ -395,34 +344,57 @@ protected: return reference::winograd_input_transform<T>(src, output_shape, winograd_info); } + bool _mixed_layout{ false }; TensorType _target{}; SimpleTensor<T> _reference{}; }; -template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> class WinogradFilterTransformValidationFixture : public framework::Fixture { public: - template <typename...> void setup(TensorShape input_shape, Size2D output_tile, DataLayout data_layout, DataType data_type) { WinogradInfo winograd_info(output_tile, Size2D(input_shape[0], input_shape[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */); TensorShape output_shape = compute_winograd_filter_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info); - _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); - _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); + _mixed_layout = mixed_layout; + _target = compute_target(input_shape, output_shape, winograd_info, data_layout, data_type); + _reference = compute_reference(input_shape, output_shape, winograd_info, data_type); } protected: + void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) + { + const DataLayout data_layout_src = src.info()->data_layout(); + const DataLayout data_layout_dst = dst.info()->data_layout(); + + // Test Multi DataLayout graph cases, when the data layout changes after configure + src.info()->set_data_layout(data_layout_src == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + dst.info()->set_data_layout(data_layout_dst == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + + // Compute Convolution function + layer.run(); + + // Reinstating original data layout for the test suite to properly check the values + src.info()->set_data_layout(data_layout_src); + dst.info()->set_data_layout(data_layout_dst); + } + template <typename U> void fill(U &&tensor, int i, float min, float max) { switch(tensor.data_type()) { case DataType::F16: + { + arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(min), float(max) }; + library->fill(tensor, distribution, i); + break; + } case DataType::F32: { - std::uniform_real_distribution<> distribution(min, max); + std::uniform_real_distribution<float> distribution(min, max); library->fill(tensor, distribution, i); break; } @@ -448,21 +420,30 @@ protected: FunctionType filter_transform; filter_transform.configure(&src, &dst, winograd_info); - ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + add_padding_x({ &src, &dst }, data_layout); // Allocate tensors src.allocator()->allocate(); dst.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS); - ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(src), 0, -1.f, 1.f); - filter_transform.run(); - + if(_mixed_layout) + { + mix_layout(filter_transform, src, dst); + } + else + { + // Compute Winograd filter transform function + filter_transform.run(); + } return dst; } @@ -477,15 +458,15 @@ protected: return reference::winograd_filter_transform<T>(src, output_shape, winograd_info); } + bool _mixed_layout{ false }; TensorType _target{}; SimpleTensor<T> _reference{}; }; -template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false> class WinogradOutputTransformValidationFixture : public framework::Fixture { public: - template <typename...> void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info = ActivationLayerInfo()) { _target = compute_target(input_shape, winograd_info, data_type, act_info); @@ -493,15 +474,37 @@ public: } protected: + void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) + { + const DataLayout data_layout_src = src.info()->data_layout(); + const DataLayout data_layout_dst = dst.info()->data_layout(); + + // Test Multi DataLayout graph cases, when the data layout changes after configure + src.info()->set_data_layout(data_layout_src == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + dst.info()->set_data_layout(data_layout_dst == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); + + // Compute Convolution function + layer.run(); + + // Reinstating original data layout for the test suite to properly check the values + src.info()->set_data_layout(data_layout_src); + dst.info()->set_data_layout(data_layout_dst); + } + template <typename U> void fill(U &&tensor, int i, float min, float max) { switch(tensor.data_type()) { case DataType::F16: + { + arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(min), float(max) }; + library->fill(tensor, distribution, i); + break; + } case DataType::F32: { - std::uniform_real_distribution<> distribution(min, max); + std::uniform_real_distribution<float> distribution(min, max); library->fill(tensor, distribution, i); break; } @@ -525,25 +528,34 @@ protected: FunctionType output_transform; output_transform.configure(&src, &bias, &dst, winograd_info, act_info); - ARM_COMPUTE_EXPECT(src.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); + ARM_COMPUTE_ASSERT(src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + add_padding_x({ &src, &bias, &dst }, winograd_info.output_data_layout); // Allocate tensors src.allocator()->allocate(); bias.allocator()->allocate(); dst.allocator()->allocate(); - ARM_COMPUTE_EXPECT(!src.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); + ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors fill(AccessorType(src), 0, -1.f, 1.f); fill(AccessorType(bias), 1, -1.f, 1.f); - output_transform.run(); - + if(_mixed_layout) + { + mix_layout(output_transform, src, dst); + } + else + { + // Compute Winograd output transform function + output_transform.run(); + } return dst; } @@ -565,10 +577,11 @@ protected: return (act_info.enabled()) ? reference::activation_layer<T>(winograd_output, act_info) : winograd_output; } + bool _mixed_layout{ false }; TensorType _target{}; SimpleTensor<T> _reference{}; }; } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_WINOGRAD_LAYER_FIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_WINOGRADCONVOLUTIONLAYERFIXTURE_H |