diff options
| -rw-r--r-- | arm_compute/core/Utils.h | 14 | ||||
| -rw-r--r-- | arm_compute/core/utils/misc/ShapeCalculator.h | 30 | ||||
| -rw-r--r-- | src/core/Utils.cpp | 29 | ||||
| -rw-r--r-- | src/core/cpu/kernels/CpuPoolingKernel.cpp | 91 | ||||
| -rw-r--r-- | src/core/gpu/cl/kernels/ClPoolingKernel.cpp | 12 | ||||
| -rw-r--r-- | tests/datasets/PoolingLayerDataset.h | 4 | ||||
| -rw-r--r-- | tests/validation/CL/PoolingLayer.cpp | 5 | ||||
| -rw-r--r-- | tests/validation/NEON/PoolingLayer.cpp | 10 |
8 files changed, 113 insertions, 82 deletions
diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h index d5c365e6ab..af9a777a0c 100644 --- a/arm_compute/core/Utils.h +++ b/arm_compute/core/Utils.h @@ -779,6 +779,20 @@ std::pair<unsigned int, unsigned int> scaled_dimensions(int width, int height, const PadStrideInfo &pad_stride_info, const Size2D &dilation = Size2D(1U, 1U)); +/** Returns calculated width and height of output scaled tensor depending on dimensions rounding mode. + * + * @param[in] width Width of input tensor (Number of columns) + * @param[in] height Height of input tensor (Number of rows) + * @param[in] kernel_width Kernel width. + * @param[in] kernel_height Kernel height. + * @param[in] pad_stride_info Pad and stride information. + * + * @return A pair with the new width in the first position and the new height in the second, returned values can be < 1 + */ +std::pair<int, int> scaled_dimensions_signed(int width, int height, + int kernel_width, int kernel_height, + const PadStrideInfo &pad_stride_info); + /** Check if the given reduction operation should be handled in a serial way. * * @param[in] op Reduction operation to perform diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 8e49c068af..d0dc202f91 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -759,25 +759,27 @@ inline TensorShape compute_min_max_shape(const ITensorInfo *input) */ inline TensorShape compute_pool_shape(const ITensorInfo &input, PoolingLayerInfo pool_info) { - unsigned int pooled_w = 0; - unsigned int pooled_h = 0; + int pooled_w = 0; + int pooled_h = 0; TensorShape output_shape{ input.tensor_shape() }; - const bool is_global_pooling = pool_info.is_global_pooling; - const unsigned int idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH); - const unsigned int idx_height = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT); - const unsigned int pool_size_x = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size.width; - const unsigned int pool_size_y = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size.height; + const bool is_global_pooling = pool_info.is_global_pooling; + const int idx_width = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT); + const int input_width = input.tensor_shape()[idx_width]; + const int input_height = input.tensor_shape()[idx_height]; + const int pool_size_x = is_global_pooling ? output_shape[idx_width] : pool_info.pool_size.width; + const int pool_size_y = is_global_pooling ? output_shape[idx_height] : pool_info.pool_size.height; - std::tie(pooled_w, pooled_h) = scaled_dimensions(output_shape[idx_width], - output_shape[idx_height], - pool_size_x, - pool_size_y, - pool_info.pad_stride_info); + std::tie(pooled_w, pooled_h) = scaled_dimensions_signed(input_width, input_height, + pool_size_x, pool_size_y, + pool_info.pad_stride_info); - output_shape.set(idx_width, pooled_w); - output_shape.set(idx_height, pooled_h); + ARM_COMPUTE_ERROR_ON_MSG((pooled_w < 1 || pooled_h < 1), "Calculated output dimension size is invalid"); + + output_shape.set(idx_width, static_cast<size_t>(pooled_w)); + output_shape.set(idx_height, static_cast<size_t>(pooled_h)); return output_shape; } diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp index e44c86db88..b81b498ae5 100644 --- a/src/core/Utils.cpp +++ b/src/core/Utils.cpp @@ -426,6 +426,35 @@ std::pair<unsigned int, unsigned int> scaled_dimensions(int width, int height, return std::make_pair(static_cast<unsigned int>(w), static_cast<unsigned int>(h)); } +std::pair<int, int> scaled_dimensions_signed(int width, int height, + int kernel_width, int kernel_height, + const PadStrideInfo &pad_stride_info) +{ + const int pad_left = pad_stride_info.pad_left(); + const int pad_top = pad_stride_info.pad_top(); + const int pad_right = pad_stride_info.pad_right(); + const int pad_bottom = pad_stride_info.pad_bottom(); + const int stride_x = pad_stride_info.stride().first; + const int stride_y = pad_stride_info.stride().second; + int w = 0; + int h = 0; + switch(pad_stride_info.round()) + { + case DimensionRoundingType::FLOOR: + w = static_cast<int>(std::floor((static_cast<float>(width + pad_left + pad_right - kernel_width) / stride_x) + 1)); + h = static_cast<int>(std::floor((static_cast<float>(height + pad_top + pad_bottom - kernel_height) / stride_y) + 1)); + break; + case DimensionRoundingType::CEIL: + w = static_cast<int>(std::ceil((static_cast<float>(width + pad_left + pad_right - kernel_width) / stride_x) + 1)); + h = static_cast<int>(std::ceil((static_cast<float>(height + pad_top + pad_bottom - kernel_height) / stride_y) + 1)); + break; + default: + ARM_COMPUTE_ERROR("Unsupported rounding type"); + } + + return std::make_pair(static_cast<int>(w), static_cast<int>(h)); +} + bool needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis) { const bool is_min_max = (op == ReductionOperation::MAX || op == ReductionOperation::MIN); diff --git a/src/core/cpu/kernels/CpuPoolingKernel.cpp b/src/core/cpu/kernels/CpuPoolingKernel.cpp index 115a3a4c67..a55f60d7ad 100644 --- a/src/core/cpu/kernels/CpuPoolingKernel.cpp +++ b/src/core/cpu/kernels/CpuPoolingKernel.cpp @@ -183,14 +183,27 @@ const PoolingKernel *get_implementation(DataType dt, DataLayout dl, int pool_str } Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const PoolingLayerInfo &pool_info, - unsigned int &pooled_w, unsigned int pooled_h, const ITensorInfo *indices, Size2D pool_size) + const ITensorInfo *indices, Size2D pool_size) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); + ARM_COMPUTE_RETURN_ERROR_ON(pool_size.x() == 0); + ARM_COMPUTE_RETURN_ERROR_ON(pool_size.y() == 0); int pool_stride_x = 0; int pool_stride_y = 0; + int output_width = 0; + int output_height = 0; PoolingType pool_type = pool_info.pool_type; const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + + std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], + pool_size.x(), pool_size.y(), pool_info.pad_stride_info); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid"); + + TensorInfo out_info(TensorInfo(compute_pool_shape(*src, pool_info), 1, dst->data_type())); std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src); @@ -210,14 +223,11 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst); - ARM_COMPUTE_RETURN_ERROR_ON((dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) - || (dst->dimension(get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); - + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &out_info); if(indices) { ARM_COMPUTE_RETURN_ERROR_ON_MSG((pool_size != Size2D(2, 2)), "Pooling indices only supported for pool size 2x2"); - ARM_COMPUTE_RETURN_ERROR_ON((indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w) - || (indices->dimension(get_data_layout_dimension_index(indices->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(indices, &out_info); } } @@ -227,18 +237,10 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const return Status{}; } -Status validate_arguments_pool_info(const unsigned int pool_size_x, const unsigned int pool_size_y) -{ - ARM_COMPUTE_RETURN_ERROR_ON(pool_size_x == 0); - ARM_COMPUTE_RETURN_ERROR_ON(pool_size_y == 0); - - return Status{}; -} - std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, ITensorInfo *indices, const PoolingLayerInfo &pool_info, unsigned int &num_elems_processed_per_iteration, BorderSize &border_size, - unsigned int pooled_w, unsigned int pooled_h, int pool_size_x, int pool_size_y) + int pool_size_x, int pool_size_y) { // dst auto inizialitation if not yet initialized auto_init_if_empty(*dst, src->clone()->set_tensor_shape(compute_pool_shape(*src, pool_info))); @@ -260,18 +262,13 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITenso const int src_height = src->dimension(idx_height); const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); - const int pool_pad_right = pad_stride_info.pad_right(); - const int pool_pad_top = pad_stride_info.pad_top(); - const int pool_pad_left = pad_stride_info.pad_left(); - const int pool_pad_bottom = pad_stride_info.pad_bottom(); - const bool is_square = pool_size_x == pool_size_y; - - // Check dst dimensions - std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - pool_size_x, - pool_size_y, - pad_stride_info); + const int pool_pad_right = pad_stride_info.pad_right(); + const int pool_pad_top = pad_stride_info.pad_top(); + const int pool_pad_left = pad_stride_info.pad_left(); + const int pool_pad_bottom = pad_stride_info.pad_bottom(); + const bool is_square = pool_size_x == pool_size_y; + const unsigned int pooled_w = dst->dimension(idx_width); + const unsigned int pooled_h = dst->dimension(idx_height); //If it's not squared and optimized will be executed the MxN num_elems_read_per_iteration = 1; @@ -398,20 +395,8 @@ void CpuPoolingKernel::configure(ITensorInfo *src, ITensorInfo *dst, const Pooli is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width, is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height); - // Validate pool info before calling scaled_dimensions - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_pool_info(pool_size.x(), pool_size.y())); - - // Check dst dimensions - unsigned int pooled_w; - unsigned int pooled_h; - std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - pool_size.x(), - pool_size.y(), - pad_stride_info); - // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, pool_size)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, pool_info, indices, pool_size)); // Set instance variables _pool_info = pool_info; @@ -429,7 +414,7 @@ void CpuPoolingKernel::configure(ITensorInfo *src, ITensorInfo *dst, const Pooli { // Configure kernel window auto win_config = validate_and_configure_window(src, dst, indices, pool_info, _num_elems_processed_per_iteration, - _border_size, pooled_w, pooled_h, pool_size.x(), pool_size.y()); + _border_size, pool_size.x(), pool_size.y()); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICpuKernel::configure(win_config.second); } @@ -439,36 +424,22 @@ Status CpuPoolingKernel::validate(const ITensorInfo *src, const ITensorInfo *dst { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src); - unsigned int pooled_w = 0; - unsigned int pooled_h = 0; unsigned int num_elems_processed_per_iteration = 0; BorderSize border_size(0); - const bool is_global_pooling = pool_info.is_global_pooling; - unsigned int pool_size_x = 0; - unsigned int pool_size_y = 0; + const bool is_global_pooling = pool_info.is_global_pooling; // Get data layout const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; - pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; - - // Validate pool info before calling scaled_dimensions - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_pool_info(pool_size_x, pool_size_y)); - - // Check dst dimensions - std::tie(pooled_w, pooled_h) = scaled_dimensions(src->dimension(idx_width), - src->dimension(idx_height), - pool_size_x, - pool_size_y, - pool_info.pad_stride_info); + unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; + unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, pooled_w, pooled_h, indices, Size2D(pool_size_x, pool_size_y))); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, pool_info, indices, Size2D(pool_size_x, pool_size_y))); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), - (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size, pooled_w, pooled_h, + (indices) ? indices->clone().get() : nullptr, pool_info, num_elems_processed_per_iteration, border_size, pool_size_x, pool_size_y) .first); diff --git a/src/core/gpu/cl/kernels/ClPoolingKernel.cpp b/src/core/gpu/cl/kernels/ClPoolingKernel.cpp index a432877a1d..08a3ce3784 100644 --- a/src/core/gpu/cl/kernels/ClPoolingKernel.cpp +++ b/src/core/gpu/cl/kernels/ClPoolingKernel.cpp @@ -67,6 +67,18 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(src->data_type()) && pool_info.pool_type == PoolingType::L2), "Unsupported combination of parameters!"); + const auto data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? src->data_layout() : pool_info.data_layout; + const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); + const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); + const bool is_global_pooling = pool_info.is_global_pooling; + unsigned int pool_size_x = is_global_pooling ? src->dimension(idx_width) : pool_info.pool_size.width; + unsigned int pool_size_y = is_global_pooling ? src->dimension(idx_height) : pool_info.pool_size.height; + int output_width = 0; + int output_height = 0; + std::tie(output_width, output_height) = scaled_dimensions_signed(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height], + pool_size_x, pool_size_y, pool_info.pad_stride_info); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_width < 1 || output_height < 1), "Calculated output dimension size is invalid"); + // Check indices if(indices) { diff --git a/tests/datasets/PoolingLayerDataset.h b/tests/datasets/PoolingLayerDataset.h index 01b2491eb2..1557240fd2 100644 --- a/tests/datasets/PoolingLayerDataset.h +++ b/tests/datasets/PoolingLayerDataset.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -106,7 +106,7 @@ public: PoolingLayerDatasetSpecial() { // Special cases - add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(3, 3), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true)); + add_config(TensorShape(2U, 3U, 4U, 1U), PoolingLayerInfo(PoolingType::AVG, Size2D(2, 2), DataLayout::NCHW, PadStrideInfo(3, 3, 0, 0), true)); add_config(TensorShape(60U, 52U, 3U, 2U), PoolingLayerInfo(PoolingType::AVG, Size2D(100, 100), DataLayout::NCHW, PadStrideInfo(5, 5, 50, 50), true)); // Asymmetric padding add_config(TensorShape(112U, 112U, 32U), PoolingLayerInfo(PoolingType::MAX, 3, DataLayout::NCHW, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); diff --git a/tests/validation/CL/PoolingLayer.cpp b/tests/validation/CL/PoolingLayer.cpp index 0153e659ae..63dec3910f 100644 --- a/tests/validation/CL/PoolingLayer.cpp +++ b/tests/validation/CL/PoolingLayer.cpp @@ -101,6 +101,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), // Invalid output Global Pooling TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::QASYMM8), TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 16U, 1U), 1, DataType::F32), }), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16), TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32), @@ -110,6 +111,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32), TensorInfo(TensorShape(12U, 12U, 5U), 1, DataType::QASYMM8), TensorInfo(TensorShape(1U, 1U, 5U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 15U, 1U), 1, DataType::F32), })), framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NCHW, PadStrideInfo(1, 1, 2, 0)), @@ -119,8 +121,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW), PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NHWC, PadStrideInfo(), false), PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NHWC, PadStrideInfo(1, 1, 0, 0), false), })), - framework::dataset::make("Expected", { false, false, false, false, true, false, true, true })), + framework::dataset::make("Expected", { false, false, false, false, true, false, true, true , false})), input_info, output_info, pool_info, expected) { ARM_COMPUTE_EXPECT(bool(CLPoolingLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pool_info)) == expected, framework::LogLevel::ERRORS); diff --git a/tests/validation/NEON/PoolingLayer.cpp b/tests/validation/NEON/PoolingLayer.cpp index acc9c3e516..b70a18907f 100644 --- a/tests/validation/NEON/PoolingLayer.cpp +++ b/tests/validation/NEON/PoolingLayer.cpp @@ -97,6 +97,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), // Invalid output Global Pooling TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::QASYMM8), // Invalid exclude_padding = false with quantized type, no actual padding and NHWC TensorInfo(TensorShape(13U, 13U, 5U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 16U, 1U), 1, DataType::F32), }), framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F16), TensorInfo(TensorShape(25U, 10U, 2U), 1, DataType::F32), @@ -106,6 +107,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( TensorInfo(TensorShape(2U, 2U, 5U), 1, DataType::F32), TensorInfo(TensorShape(12U, 12U, 5U), 1, DataType::QASYMM8), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(1U, 15U, 1U), 1, DataType::F32), })), framework::dataset::make("PoolInfo", { PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), PoolingLayerInfo(PoolingType::AVG, 3, DataLayout::NCHW, PadStrideInfo(1, 1, 0, 0)), @@ -115,8 +117,9 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( PoolingLayerInfo(PoolingType::MAX, DataLayout::NCHW), PoolingLayerInfo(PoolingType::AVG, 2, DataLayout::NHWC, PadStrideInfo(), false), PoolingLayerInfo(PoolingType::AVG, DataLayout::NCHW), + PoolingLayerInfo(PoolingType::MAX, 2, DataLayout::NHWC, PadStrideInfo(1, 1, 0, 0), false), })), - framework::dataset::make("Expected", { false, false, false, false, true, false, false, false, true })), + framework::dataset::make("Expected", { false, false, false, false, true, false, true, false, false})), input_info, output_info, pool_info, expected) { bool is_valid = bool(NEPoolingLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), pool_info)); @@ -145,15 +148,12 @@ TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunIndices, NEPoolingLayerIndicesFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), combine(PoolingLayerIndicesDatasetFPSmall, framework::dataset::make("DataType", DataType::F32))), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }) - - )) + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, tolerance_f32); validate(Accessor(_target_indices), _ref_indices); } - FIXTURE_DATA_TEST_CASE(RunSpecial, NESpecialPoolingLayerFixture<float>, framework::DatasetMode::ALL, datasets::PoolingLayerDatasetSpecial() * framework::dataset::make("DataType", DataType::F32)) { // Validate output |