| /* | |
| * Copyright (c) 2017-2022 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_DEPTHWISE_CONVOLUTION_FIXTURE | |
| #define ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_FIXTURE | |
| #include "arm_compute/core/TensorShape.h" | |
| #include "arm_compute/core/Types.h" | |
| #include "arm_compute/core/utils/misc/ShapeCalculator.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/ActivationLayer.h" | |
| #include "tests/validation/reference/DepthwiseConvolutionLayer.h" | |
| #include "utils/Utils.h" | |
| #include <random> | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| using namespace arm_compute::misc::shape_calculator; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW> | |
| class DepthwiseConvolutionLayerValidationGenericFixture : public framework::Fixture | |
| { | |
| public: | |
| using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type; | |
| public: | |
| template <typename...> | |
| void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, | |
| unsigned int depth_multiplier, DataType input_data_type, DataType weights_data_type, | |
| QuantizationInfo input_quantization_info, QuantizationInfo weights_quantization_info, QuantizationInfo output_quantization_info, | |
| DataLayout data_layout, ActivationLayerInfo act_info, bool mixed_layout = false, bool in_place = false, bool run_twice = false) | |
| { | |
| ARM_COMPUTE_ERROR_ON(mixed_layout && in_place); | |
| _mixed_layout = mixed_layout; | |
| _input_shape = in_shape; | |
| _input_data_type = input_data_type; | |
| _weights_data_type = weights_data_type; | |
| _input_quantization_info = input_quantization_info; | |
| _weights_quantization_info = weights_quantization_info; | |
| _output_quantization_info = output_quantization_info; | |
| _data_layout = data_layout; | |
| _pad_stride_info = pad_stride_info; | |
| _act_info = act_info; | |
| _depth_multiplier = depth_multiplier; | |
| _dilation = dilation; | |
| _in_place = in_place; | |
| _run_twice = run_twice; | |
| _bias_data_type = is_data_type_quantized(_input_data_type) ? DataType::S32 : _input_data_type; | |
| _weights_shape = TensorShape(kernel_size.width, kernel_size.height); | |
| const TensorInfo in_info(_input_shape, 1, _input_data_type); | |
| const TensorInfo we_info(_weights_shape, 1, _weights_data_type); | |
| const ConvolutionInfo info{ _pad_stride_info, _depth_multiplier, _act_info, _dilation }; | |
| _output_shape = compute_depthwise_convolution_shape(in_info, we_info, info); | |
| _weights_shape.set(2, _output_shape.z()); | |
| _biases_shape = TensorShape(_weights_shape[2]); | |
| } | |
| void configure_target() | |
| { | |
| TensorShape input_shape = _input_shape; | |
| TensorShape weights_shape = _weights_shape; | |
| TensorShape output_shape = _output_shape; | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); | |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); | |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); | |
| } | |
| // Create tensors | |
| _src = create_tensor<TensorType>(input_shape, _input_data_type, 1, _input_quantization_info, _data_layout); | |
| _weights = create_tensor<TensorType>(weights_shape, _weights_data_type, 1, _weights_quantization_info, _data_layout); | |
| if(_run_twice) { | |
| _weights.info()->set_are_values_constant(false); | |
| } | |
| _biases = create_tensor<TensorType>(_biases_shape, _bias_data_type, 1, _input_quantization_info, _data_layout); | |
| TensorType *target_to_use = nullptr; | |
| if(!_in_place) | |
| { | |
| _target = create_tensor<TensorType>(output_shape, _input_data_type, 1, _output_quantization_info, _data_layout); | |
| target_to_use = &_target; | |
| } | |
| add_padding_x({ &_src, &_biases }, _data_layout); | |
| add_padding_x({ &_weights }, _data_layout, true); | |
| if(!_in_place) | |
| { | |
| add_padding_x({ &_target }, _data_layout); | |
| } | |
| // Create Depthwise Convolution configure function | |
| _dwc.configure(&_src, &_weights, &_biases, target_to_use, _pad_stride_info, _depth_multiplier, _act_info, _dilation); | |
| ARM_COMPUTE_ASSERT(_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_biases.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_target.info()->is_resizable()); | |
| } | |
| void allocate_and_run_target() | |
| { | |
| // Allocate tensors | |
| _src.allocator()->allocate(); | |
| _weights.allocator()->allocate(); | |
| _biases.allocator()->allocate(); | |
| ARM_COMPUTE_ASSERT(!_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_biases.info()->is_resizable()); | |
| if(!_in_place) | |
| { | |
| _target.allocator()->allocate(); | |
| ARM_COMPUTE_ASSERT(!_target.info()->is_resizable()); | |
| } | |
| // Fill tensors | |
| fill(AccessorType(_src), 0); | |
| fill(AccessorType(_weights), 1); | |
| fill(AccessorType(_biases), 2); | |
| // Run with variable input | |
| if(_run_twice) { | |
| _dwc.run(); | |
| // Fill tensors with a new seed | |
| fill(AccessorType(_src), 3); | |
| fill(AccessorType(_weights), 4); | |
| fill(AccessorType(_biases), 5); | |
| } | |
| if(_mixed_layout) | |
| { | |
| mix_layout(_dwc, _src, _target); | |
| } | |
| else | |
| { | |
| // Compute function | |
| _dwc.run(); | |
| } | |
| } | |
| void compute_reference() | |
| { | |
| SimpleTensor<T> src{ _input_shape, _input_data_type, 1, _input_quantization_info }; | |
| SimpleTensor<TW> weights{ _weights_shape, _weights_data_type, 1, _weights_quantization_info }; | |
| SimpleTensor<TBias> biases{ _biases_shape, _bias_data_type, 1, _input_quantization_info }; | |
| fill(src, 0); | |
| fill(weights, 1); | |
| fill(biases, 2); | |
| if(_run_twice) { | |
| SimpleTensor<T> depth_out = reference::depthwise_convolution(src, weights, biases, _output_shape, _pad_stride_info, _depth_multiplier, _dilation, _output_quantization_info); | |
| if(_act_info.enabled()) { | |
| reference::activation_layer<T>(depth_out, _act_info); | |
| } | |
| fill(src, 3); | |
| fill(weights, 4); | |
| fill(biases, 5); | |
| } | |
| SimpleTensor<T> depth_out = reference::depthwise_convolution(src, weights, biases, _output_shape, _pad_stride_info, _depth_multiplier, _dilation, _output_quantization_info); | |
| _reference = (_act_info.enabled()) ? reference::activation_layer<T>(depth_out, _act_info) : depth_out; | |
| } | |
| protected: | |
| void mix_layout(FunctionType &layer, TensorType &src, TensorType &dst) | |
| { | |
| ARM_COMPUTE_ERROR_ON(_in_place); | |
| // 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); | |
| // 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); | |
| dst.info()->set_data_layout(_data_layout); | |
| } | |
| template <typename U> | |
| void fill(U &&tensor, int i) | |
| { | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::QASYMM8: | |
| { | |
| std::uniform_int_distribution<uint32_t> distribution(0, 15); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| case DataType::QASYMM8_SIGNED: | |
| case DataType::QSYMM8_PER_CHANNEL: | |
| { | |
| std::uniform_int_distribution<int32_t> distribution(-10, 10); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| case DataType::F16: | |
| { | |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f }; | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| case DataType::F32: | |
| { | |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| case DataType::S32: | |
| { | |
| std::uniform_int_distribution<int32_t> distribution(-100, 100); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| default: | |
| library->fill_tensor_uniform(tensor, i); | |
| } | |
| } | |
| TensorType _target{}; | |
| SimpleTensor<T> _reference{}; | |
| TensorType _src{}; | |
| TensorType _weights{}; | |
| TensorType _biases{}; | |
| FunctionType _dwc{}; | |
| TensorShape _input_shape{}; | |
| TensorShape _weights_shape{}; | |
| TensorShape _biases_shape{}; | |
| TensorShape _output_shape{}; | |
| DataType _input_data_type{}; | |
| DataType _weights_data_type{}; | |
| DataType _bias_data_type{}; | |
| QuantizationInfo _input_quantization_info{}; | |
| QuantizationInfo _weights_quantization_info{}; | |
| QuantizationInfo _output_quantization_info{}; | |
| DataLayout _data_layout{}; | |
| PadStrideInfo _pad_stride_info{}; | |
| ActivationLayerInfo _act_info{}; | |
| unsigned int _depth_multiplier{}; | |
| Size2D _dilation{}; | |
| bool _mixed_layout{ false }; | |
| bool _in_place{ false }; | |
| bool _run_twice{ false }; | |
| }; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false, bool in_place = false, bool run_twice = false> | |
| class DepthwiseConvolutionLayerValidationFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> | |
| { | |
| public: | |
| template <typename...> | |
| void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType data_type, DataLayout data_layout, | |
| ActivationLayerInfo act_info) | |
| { | |
| DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(in_shape, kernel_size, pad_stride_info, dilation, depth_multiplier, | |
| data_type, data_type, QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), | |
| data_layout, act_info, mixed_layout, in_place, run_twice); | |
| } | |
| }; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T> | |
| class DepthwiseConvolutionLayerNativeValidationFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> | |
| { | |
| public: | |
| template <typename...> | |
| void setup(size_t width, size_t height, size_t channel, size_t batch, Size2D kernel_size, size_t depth_multiplier, Size2D dilation, Size2D stride, bool padding_valid, DataType data_type, | |
| DataLayout data_layout) | |
| { | |
| _dilation = dilation; | |
| _depth_multiplier = depth_multiplier; | |
| _data_type = data_type; | |
| _data_layout = data_layout; | |
| _input_shape = TensorShape(width, height, channel, batch); | |
| _weights_shape = TensorShape(kernel_size.width, kernel_size.height, channel * _depth_multiplier); | |
| _biases_shape = TensorShape(_weights_shape.z()); | |
| if(padding_valid) | |
| { | |
| _conv_info = PadStrideInfo(stride.width, stride.height); | |
| } | |
| else | |
| { | |
| _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation); | |
| } | |
| } | |
| void configure_target() | |
| { | |
| TensorShape input_shape = _input_shape; | |
| TensorShape weights_shape = _weights_shape; | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); | |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); | |
| } | |
| // Create tensors | |
| _src = create_tensor<TensorType>(input_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| _weights = create_tensor<TensorType>(weights_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| _biases = create_tensor<TensorType>(_biases_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| _target = create_tensor<TensorType>(TensorShape(), _data_type, 1, QuantizationInfo(), _data_layout); | |
| add_padding_x({ &_src, &_biases, &_target }, _data_layout); | |
| add_padding_x({ &_weights }, _data_layout, true); | |
| add_padding_y({ &_src, &_target }, _data_layout); | |
| // Create Depthwise Convolution configure function | |
| const ConvolutionInfo info | |
| { | |
| _conv_info, _depth_multiplier, ActivationLayerInfo(), _dilation | |
| }; | |
| _dwc.configure(_src.info(), _weights.info(), _biases.info(), _target.info(), info); | |
| ARM_COMPUTE_ASSERT(_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_biases.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_target.info()->is_resizable()); | |
| } | |
| void allocate_and_run_target() | |
| { | |
| // Allocate tensors | |
| _src.allocator()->allocate(); | |
| _weights.allocator()->allocate(); | |
| _biases.allocator()->allocate(); | |
| _target.allocator()->allocate(); | |
| ARM_COMPUTE_ASSERT(!_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_biases.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_target.info()->is_resizable()); | |
| // Fill tensors | |
| fill(AccessorType(_src), 0); | |
| fill(AccessorType(_weights), 1); | |
| fill(AccessorType(_biases), 2); | |
| arm_compute::ITensorPack pack; | |
| pack.add_const_tensor(arm_compute::TensorType::ACL_SRC_0, &_src); | |
| pack.add_const_tensor(arm_compute::TensorType::ACL_SRC_1, &_weights); | |
| pack.add_const_tensor(arm_compute::TensorType::ACL_SRC_2, &_biases); | |
| pack.add_tensor(arm_compute::TensorType::ACL_DST, &_target); | |
| // Compute function | |
| _dwc.run(pack); | |
| } | |
| void compute_reference() | |
| { | |
| SimpleTensor<T> src{ _input_shape, _data_type }; | |
| SimpleTensor<T> weights{ _weights_shape, _data_type }; | |
| SimpleTensor<T> biases{ _biases_shape, _data_type }; | |
| fill(src, 0); | |
| fill(weights, 1); | |
| fill(biases, 2); | |
| const ConvolutionInfo info{ _conv_info, _depth_multiplier, ActivationLayerInfo(), _dilation }; | |
| const TensorShape dst_shape = compute_depthwise_convolution_shape(TensorInfo(_input_shape, 1, _data_type), TensorInfo(_weights_shape, 1, _data_type), info); | |
| _reference = reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation); | |
| } | |
| protected: | |
| template <typename U> | |
| void fill(U &&tensor, int i) | |
| { | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::F32: | |
| { | |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| default: | |
| library->fill_tensor_uniform(tensor, i); | |
| } | |
| } | |
| TensorType _target{}; | |
| SimpleTensor<T> _reference{}; | |
| TensorType _src{}; | |
| TensorType _weights{}; | |
| TensorType _biases{}; | |
| FunctionType _dwc{}; | |
| TensorShape _input_shape{}; | |
| TensorShape _weights_shape{}; | |
| TensorShape _biases_shape{}; | |
| DataType _data_type{}; | |
| DataLayout _data_layout{}; | |
| PadStrideInfo _conv_info{}; | |
| Size2D _dilation{}; | |
| unsigned int _depth_multiplier{}; | |
| }; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool in_place = false> | |
| class DepthwiseConvolutionLayerNativeConfigurableValidationFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> | |
| { | |
| public: | |
| template <typename...> | |
| void setup(size_t width, size_t height, size_t channel, size_t batch, Size2D kernel_size, size_t depth_multiplier, Size2D dilation, Size2D stride, bool padding_valid, DataType data_type, | |
| DataLayout data_layout, const ActivationLayerInfo &act_info, unsigned int n0, bool export_to_cl_image) | |
| { | |
| _dilation = dilation; | |
| _depth_multiplier = depth_multiplier; | |
| _data_type = data_type; | |
| _data_layout = data_layout; | |
| _act_info = act_info; | |
| _n0 = n0; | |
| _export_to_cl_image = export_to_cl_image; | |
| _in_place = in_place; | |
| _input_shape = TensorShape(width, height, channel, batch); | |
| _weights_shape = TensorShape(kernel_size.width, kernel_size.height, channel * _depth_multiplier); | |
| _biases_shape = TensorShape(_weights_shape.z()); | |
| if(padding_valid) | |
| { | |
| _conv_info = calculate_same_pad(_input_shape, _weights_shape, PadStrideInfo(stride.width, stride.height), DataLayout::NCHW, _dilation); | |
| } | |
| else | |
| { | |
| _conv_info = PadStrideInfo(stride.width, stride.height); | |
| } | |
| } | |
| void configure_target() | |
| { | |
| #if defined(ARM_COMPUTE_OPENCL_ENABLED) | |
| if(_export_to_cl_image) | |
| { | |
| _validate_output &= image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); | |
| _validate_output &= (get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) != 0); | |
| } | |
| #endif // ARM_COMPUTE_OPENCL_ENABLED | |
| if(!_validate_output) | |
| { | |
| return; | |
| } | |
| TensorShape input_shape = _input_shape; | |
| TensorShape weights_shape = _weights_shape; | |
| if(_data_layout == DataLayout::NHWC) | |
| { | |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); | |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); | |
| } | |
| // Create tensors | |
| _src = create_tensor<TensorType>(input_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| _weights = create_tensor<TensorType>(weights_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| _biases = create_tensor<TensorType>(_biases_shape, _data_type, 1, QuantizationInfo(), _data_layout); | |
| TensorType *target_to_use = nullptr; | |
| if(!_in_place) | |
| { | |
| _target = create_tensor<TensorType>(TensorShape(), _data_type, 1, QuantizationInfo(), _data_layout); | |
| target_to_use = &_target; | |
| } | |
| DWCComputeKernelInfo dwc_info; | |
| dwc_info.n0 = _n0; | |
| dwc_info.m0 = _conv_info.stride().first == 1 && _dilation.x() == 1 ? 8 : 1; | |
| dwc_info.export_input_to_cl_image = false; | |
| dwc_info.export_weights_to_cl_image = _export_to_cl_image; | |
| const ConvolutionInfo conv_kernel_info | |
| { | |
| _conv_info, _depth_multiplier, _act_info, _dilation | |
| }; | |
| add_padding_x({ &_src, &_biases, &_target }, _data_layout); | |
| add_padding_x({ &_weights }, _data_layout, _export_to_cl_image); // Don't add left padding if cl image will be used | |
| // Create Depthwise Convolution configure function | |
| _dwc.configure(&_src, &_weights, &_biases, target_to_use, dwc_info, conv_kernel_info); | |
| ARM_COMPUTE_ASSERT(_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_biases.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(_target.info()->is_resizable()); | |
| } | |
| void allocate_and_run_target() | |
| { | |
| if(!_validate_output) | |
| { | |
| return; | |
| } | |
| // Allocate tensors | |
| _src.allocator()->allocate(); | |
| _weights.allocator()->allocate(); | |
| _biases.allocator()->allocate(); | |
| ARM_COMPUTE_ASSERT(!_src.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_weights.info()->is_resizable()); | |
| ARM_COMPUTE_ASSERT(!_biases.info()->is_resizable()); | |
| if(!_in_place) | |
| { | |
| _target.allocator()->allocate(); | |
| ARM_COMPUTE_ASSERT(!_target.info()->is_resizable()); | |
| } | |
| // Fill tensors | |
| fill(AccessorType(_src), 0); | |
| fill(AccessorType(_weights), 1); | |
| fill(AccessorType(_biases), 2); | |
| // 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); | |
| if(!_in_place) | |
| { | |
| _target.info()->set_data_layout(_data_layout == DataLayout::NCHW ? DataLayout::NHWC : DataLayout::NCHW); | |
| } | |
| // Compute function | |
| _dwc.run(); | |
| // Reinstating original data layout for the test suite to properly check the values | |
| if(!_in_place) | |
| { | |
| _target.info()->set_data_layout(_data_layout); | |
| } | |
| } | |
| void compute_reference() | |
| { | |
| if(!_validate_output) | |
| { | |
| return; | |
| } | |
| SimpleTensor<T> src{ _input_shape, _data_type }; | |
| SimpleTensor<T> weights{ _weights_shape, _data_type }; | |
| SimpleTensor<T> biases{ _biases_shape, _data_type }; | |
| fill(src, 0); | |
| fill(weights, 1); | |
| fill(biases, 2); | |
| const ConvolutionInfo info{ _conv_info, _depth_multiplier, _act_info, _dilation }; | |
| const TensorShape dst_shape = compute_depthwise_convolution_shape(TensorInfo(_input_shape, 1, _data_type), TensorInfo(_weights_shape, 1, _data_type), info); | |
| _reference = reference::activation_layer(reference::depthwise_convolution(src, weights, biases, dst_shape, _conv_info, _depth_multiplier, _dilation), _act_info); | |
| } | |
| protected: | |
| template <typename U> | |
| void fill(U &&tensor, int i) | |
| { | |
| switch(tensor.data_type()) | |
| { | |
| case DataType::F32: | |
| { | |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| case DataType::F16: | |
| { | |
| arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f }; | |
| library->fill(tensor, distribution, i); | |
| break; | |
| } | |
| default: | |
| library->fill_tensor_uniform(tensor, i); | |
| } | |
| } | |
| TensorType _target{}; | |
| SimpleTensor<T> _reference{}; | |
| TensorType _src{}; | |
| TensorType _weights{}; | |
| TensorType _biases{}; | |
| FunctionType _dwc{}; | |
| TensorShape _input_shape{}; | |
| TensorShape _weights_shape{}; | |
| TensorShape _biases_shape{}; | |
| DataType _data_type{}; | |
| DataLayout _data_layout{}; | |
| PadStrideInfo _conv_info{}; | |
| ActivationLayerInfo _act_info{}; | |
| Size2D _dilation{}; | |
| unsigned int _depth_multiplier{}; | |
| unsigned int _n0{}; | |
| bool _export_to_cl_image{}; | |
| bool _validate_output{ true }; | |
| bool _in_place{ false }; | |
| }; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool mixed_layout = false, bool in_place = false> | |
| class DepthwiseConvolutionLayerValidationQuantizedFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T> | |
| { | |
| public: | |
| template <typename...> | |
| void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType data_type, | |
| QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, DataLayout data_layout, ActivationLayerInfo act_info) | |
| { | |
| DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, T>::setup(in_shape, kernel_size, pad_stride_info, dilation, depth_multiplier, data_type, | |
| data_type, input_quantization_info, input_quantization_info, output_quantization_info, | |
| data_layout, act_info, mixed_layout, in_place); | |
| } | |
| }; | |
| template <typename TensorType, typename AccessorType, typename FunctionType, typename T, typename TW, bool in_place = false> | |
| class DepthwiseConvolutionLayerValidationQuantizedPerChannelFixture : public DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW> | |
| { | |
| public: | |
| template <typename...> | |
| void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType input_data_type, DataType weights_data_type, | |
| QuantizationInfo input_quantization_info, QuantizationInfo output_quantization_info, DataLayout data_layout, ActivationLayerInfo act_info) | |
| { | |
| const float out_scale = output_quantization_info.uniform().scale; | |
| const float in_scale = input_quantization_info.uniform().scale; | |
| std::vector<float> weights_scales{}; | |
| std::mt19937 gen(library->seed()); | |
| std::uniform_real_distribution<float> dis(0.01f, out_scale / in_scale); | |
| for(size_t i = 0; i < in_shape.z() * depth_multiplier; ++i) | |
| { | |
| weights_scales.push_back(dis(gen)); | |
| } | |
| DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, TW>::setup(in_shape, kernel_size, pad_stride_info, dilation, depth_multiplier, | |
| input_data_type, weights_data_type, | |
| input_quantization_info, QuantizationInfo(weights_scales), output_quantization_info, | |
| data_layout, act_info, false, in_place); | |
| } | |
| }; | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute | |
| #endif /* ARM_COMPUTE_TEST_DEPTHWISE_CONVOLUTION_FIXTURE */ |