diff options
Diffstat (limited to 'tests/validation/NEON/DeconvolutionLayer.cpp')
-rw-r--r-- | tests/validation/NEON/DeconvolutionLayer.cpp | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/tests/validation/NEON/DeconvolutionLayer.cpp b/tests/validation/NEON/DeconvolutionLayer.cpp index 566b75a827..3bb6d6f8fc 100644 --- a/tests/validation/NEON/DeconvolutionLayer.cpp +++ b/tests/validation/NEON/DeconvolutionLayer.cpp @@ -58,6 +58,106 @@ const auto data1x1 = datasets::SmallDeconvolutionShapes() * framework::dataset:: TEST_SUITE(NEON) TEST_SUITE(DeconvolutionLayer) +DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, (combine(datasets::SmallDeconvolutionShapes(), framework::dataset::make("DataType", DataType::F32))), + input_shape, data_type) +{ + // Create shapes + const unsigned int kernel_size_x = 3; + const unsigned int kernel_size_y = 3; + const unsigned int num_kernels = 1; + const TensorShape weights_shape(kernel_size_x, kernel_size_y, input_shape.z(), num_kernels); + const TensorShape bias_shape(num_kernels); + auto out_dim = deconvolution_output_dimensions(input_shape.x(), input_shape.y(), kernel_size_x, kernel_size_y, 1, 1, 0, 0, 1, 1); + TensorShape output_shape = deconvolution_output_shape(out_dim, input_shape, weights_shape); + + // Create tensors + Tensor src = create_tensor<Tensor>(input_shape, data_type, 1); + Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1); + Tensor bias = create_tensor<Tensor>(bias_shape, data_type, 1); + Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1); + + 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); + + // Create and configure function + NEDeconvolutionLayer deconv; + deconv.configure(&src, &weights, &bias, &dst, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), 0, 0); + + // Validate valid region + const ValidRegion src_valid_region = shape_to_valid_region(input_shape); + const ValidRegion weights_valid_region = shape_to_valid_region(weights_shape); + const ValidRegion bias_valid_region = shape_to_valid_region(bias_shape); + const ValidRegion dst_valid_region = shape_to_valid_region(output_shape); + + validate(src.info()->valid_region(), src_valid_region); + validate(weights.info()->valid_region(), weights_valid_region); + validate(bias.info()->valid_region(), bias_valid_region); + validate(dst.info()->valid_region(), dst_valid_region); +} + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Invalid weights shape + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QS8, 4), // Non supported data type + TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 11), // Invalid bias shape + TensorInfo(TensorShape(13U, 11U, 4U, 3U), 1, DataType::F32, 0), // Window shrink + TensorInfo(TensorShape(32U, 16U, 2U), 1, DataType::F32, 0), + }), + framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::F16, 0), + TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32, 0), + TensorInfo(TensorShape(3U, 3U, 2U, 2U), 1, DataType::QS8, 5), + TensorInfo(TensorShape(3U, 2U, 2U, 2U), 1, DataType::F32, 11), + TensorInfo(TensorShape(3U, 3U, 4U), 1, DataType::F32, 0), + TensorInfo(TensorShape(1U, 1U, 2U, 4U), 1, DataType::F32, 0), + })), + framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(1U), 1, DataType::F16, 0), + TensorInfo(TensorShape(1U), 1, DataType::F32, 0), + TensorInfo(TensorShape(1U), 1, DataType::F32, 5), + TensorInfo(TensorShape(25U, 11U), 1, DataType::F32, 11), + TensorInfo(TensorShape(1U), 1, DataType::F32, 0), + TensorInfo(TensorShape(4U), 1, DataType::F32, 0), + })), + framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16, 0), + TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32, 5), + TensorInfo(TensorShape(13U, 13U, 2U), 1, DataType::F32, 0), + TensorInfo(TensorShape(11U, 9U, 1U, 3U), 1, DataType::F32, 0), + TensorInfo(TensorShape(32U, 16U, 4U), 1, DataType::F32, 0), + })), + framework::dataset::make("PadStrideInfo", { PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 1, 1), + PadStrideInfo(1, 1, 0, 0), + })), + framework::dataset::make("ax", { 1U, + 1U, + 1U, + 1U, + 0U, + 0U, + })), + framework::dataset::make("ay", { 1U, + 1U, + 1U, + 1U, + 0U, + 0U, + })), + framework::dataset::make("Expected", { false, false, false, false, false, true })), + input_info, weights_info, bias_info, output_info, pad_info, ax, ay, expected) +{ + 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)); + ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + template <typename T> using NEDeconvolutionLayerFixture4x4 = DeconvolutionValidationFixture<Tensor, Accessor, NEDeconvolutionLayer, T, 4, 4>; |