30 #include "tests/datasets/ShapeDatasets.h" 35 #include "tests/validation/fixtures/DeconvolutionLayerFixture.h" 45 constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f);
65 const unsigned int kernel_size_x = 3;
66 const unsigned int kernel_size_y = 3;
67 const unsigned int num_kernels = 1;
68 const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels);
69 const TensorShape bias_shape(num_kernels);
74 Tensor
src = create_tensor<Tensor>(input_shape,
data_type, 1);
75 Tensor weights = create_tensor<Tensor>(weights_shape,
data_type, 1);
76 Tensor bias = create_tensor<Tensor>(bias_shape,
data_type, 1);
85 NEDeconvolutionLayer deconv;
86 deconv.configure(&src, &weights, &bias, &dst, PadStrideInfo(1, 1, 1, 1,
DimensionRoundingType::CEIL), 0, 0);
94 validate(src.info()->valid_region(), src_valid_region);
95 validate(weights.info()->valid_region(), weights_valid_region);
96 validate(bias.info()->valid_region(), bias_valid_region);
107 TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1,
DataType::F32, 0),
111 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1,
DataType::F32, 0),
112 TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1,
DataType::QS8, 5),
113 TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1,
DataType::F32, 11),
115 TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1,
DataType::F32, 0),
128 TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1,
DataType::F32, 0),
132 PadStrideInfo(1, 1, 0, 0),
133 PadStrideInfo(1, 1, 0, 0),
134 PadStrideInfo(1, 1, 0, 0),
135 PadStrideInfo(1, 1, 1, 1),
136 PadStrideInfo(1, 1, 0, 0),
155 bool is_valid = bool(
NEDeconvolutionLayer::validate(&input_info.clone()->set_is_resizable(
false), &weights_info.clone()->set_is_resizable(
false), &bias_info.clone()->set_is_resizable(
false), &output_info.clone()->set_is_resizable(
false), pad_info, ax, ay));
161 template <
typename T>
164 template <
typename T>
167 template <
typename T>
const std::pair< unsigned int, unsigned int > deconvolution_output_dimensions(unsigned int in_width, unsigned int in_height, unsigned int kernel_width, unsigned int kernel_height, unsigned int padx, unsigned int pady, unsigned int inner_border_right, unsigned int inner_border_top, unsigned int stride_x, unsigned int stride_y)
Returns expected width and height of the deconvolution's output tensor.
TensorShape deconvolution_output_shape(const std::pair< unsigned int, unsigned int > &out_dims, TensorShape input, TensorShape weights)
Returns expected shape for the deconvolution output tensor.
quantized, symmetric fixed-point 8-bit number
DeconvolutionValidationFixture< Tensor, Accessor, NEDeconvolutionLayer, T, 3, 3 > NEDeconvolutionLayerFixture3x3
1 channel, 1 F32 per channel
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
This file contains all available output stages for GEMMLowp on OpenCL.
1 channel, 1 F16 per channel
#define TEST_SUITE(SUITE_NAME)
DeconvolutionValidationFixture< Tensor, Accessor, NEDeconvolutionLayer, T, 1, 1 > NEDeconvolutionLayerFixture1x1
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsoluteDifferenceFixture< uint8_t >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), AbsoluteDifferenceU8Dataset))
validate(dst.info() ->valid_region(), dst_valid_region)
Accessor implementation for Tensor objects.
DatasetMode
Possible dataset modes.
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), AbsoluteDifferenceU8Dataset), shape, data_type0, data_type1, output_data_type)
TEST_SUITE_END() DATA_TEST_CASE(Configuration
const ValidRegion dst_valid_region
ARM_COMPUTE_EXPECT(src.info() ->is_resizable(), framework::LogLevel::ERRORS)
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info, unsigned int inner_border_right, unsigned int inner_border_top)
Static function to check if given info will lead to a valid configuration of NEDeconvolutionLayer.
combine(combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), framework::dataset::make("DataType",{DataType::U8, DataType::S16})), datasets::BorderModes()), framework::dataset::make("filter_size",{5}))
DeconvolutionValidationFixture< Tensor, Accessor, NEDeconvolutionLayer, T, 4, 4 > NEDeconvolutionLayerFixture4x4
DataType
Available data types.
zip(zip(zip(framework::dataset::make("InputInfo",{TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16), TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::QASYMM8), TensorInfo(TensorShape(5U, 5U, 5U, 3U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 37U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 37U, 22U), 1, DataType::F32)}), framework::dataset::make("OutputInfo",{TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F16), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::QASYMM8), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), TensorInfo(TensorShape(1U, 1U, 16U), 1, DataType::F32), TensorInfo(TensorShape(3U, 5U, 16U), 1, DataType::F32), TensorInfo(TensorShape(2U, 37U, 16U), 1, DataType::F32), TensorInfo(TensorShape(22U, 37U, 36U), 1, DataType::F32)})), framework::dataset::make("WinogradInfo",{WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(3U, 3U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(), PadStrideInfo(), DataLayout::NCHW)})), framework::dataset::make("Expected",{false, false, false, false, true, true, true}))
convolution configure & src
ValidRegion shape_to_valid_region(const TensorShape &a_shape, bool border_undefined=false, BorderSize border_size=BorderSize(0))
Create a valid region based on tensor shape, border mode and border size.