From 5a7d1571a2de24eefc6f1d8d22deeef9f47521ee Mon Sep 17 00:00:00 2001 From: SiCong Li Date: Tue, 21 Mar 2023 12:00:15 +0000 Subject: Fix BatchToSpaceFixture * Use a vector to represent the (static) block shape instead of an N-D Tensor. The previous use of ND Tensor as block shape was wrong, not adhering to the specification, and non-functional (only first dim was used anyway). * The fixture now accepts a static block shape, because the dynamic case is not properly implemented and will be deprecated for now. * Fix an assertion error in reference implementation. Partially resolves COMPMID-5918 Change-Id: I5221e52ccc05e7c1249dec3a42426f954a73729a Signed-off-by: SiCong Li Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9357 Tested-by: Arm Jenkins Reviewed-by: Pablo Marquez Tello Reviewed-by: Omar Al Khatib Comments-Addressed: Arm Jenkins Benchmark: Arm Jenkins --- tests/datasets/BatchToSpaceDataset.h | 85 ++++++++++++++++++++++++------------ 1 file changed, 58 insertions(+), 27 deletions(-) (limited to 'tests/datasets/BatchToSpaceDataset.h') diff --git a/tests/datasets/BatchToSpaceDataset.h b/tests/datasets/BatchToSpaceDataset.h index 1edd457aad..2670af50df 100644 --- a/tests/datasets/BatchToSpaceDataset.h +++ b/tests/datasets/BatchToSpaceDataset.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 Arm Limited. + * Copyright (c) 2018-2019, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -38,15 +38,17 @@ namespace datasets class BatchToSpaceLayerDataset { public: - using type = std::tuple; + using type = std::tuple, CropInfo, TensorShape>; struct iterator { - iterator(std::vector::const_iterator src_it, - std::vector::const_iterator block_shape_it, - std::vector::const_iterator dst_it) + iterator(std::vector::const_iterator src_it, + std::vector>::const_iterator block_shape_it, + std::vector::const_iterator crop_info_it, + std::vector::const_iterator dst_it) : _src_it{ std::move(src_it) }, _block_shape_it{ std::move(block_shape_it) }, + _crop_info_it{ std::move(crop_info_it) }, _dst_it{ std::move(dst_it) } { } @@ -56,44 +58,48 @@ public: std::stringstream description; description << "In=" << *_src_it << ":"; description << "BlockShape=" << *_block_shape_it << ":"; + description << "CropInfo=" << *_crop_info_it << ":"; description << "Out=" << *_dst_it; return description.str(); } BatchToSpaceLayerDataset::type operator*() const { - return std::make_tuple(*_src_it, *_block_shape_it, *_dst_it); + return std::make_tuple(*_src_it, *_block_shape_it, *_crop_info_it, *_dst_it); } iterator &operator++() { ++_src_it; ++_block_shape_it; + ++_crop_info_it; ++_dst_it; return *this; } private: - std::vector::const_iterator _src_it; - std::vector::const_iterator _block_shape_it; - std::vector::const_iterator _dst_it; + std::vector::const_iterator _src_it; + std::vector>::const_iterator _block_shape_it; + std::vector::const_iterator _crop_info_it; + std::vector::const_iterator _dst_it; }; iterator begin() const { - return iterator(_src_shapes.begin(), _block_shape_shapes.begin(), _dst_shapes.begin()); + return iterator(_src_shapes.begin(), _block_shapes.begin(), _crop_infos.begin(), _dst_shapes.begin()); } int size() const { - return std::min(_src_shapes.size(), std::min(_block_shape_shapes.size(), _dst_shapes.size())); + return std::min(std::min(std::min(_src_shapes.size(), _block_shapes.size()), _crop_infos.size()), _dst_shapes.size()); } - void add_config(TensorShape src, TensorShape block_shape, TensorShape dst) + void add_config(const TensorShape &src, const std::vector &block_shape, const CropInfo &crop_info, const TensorShape &dst) { _src_shapes.emplace_back(std::move(src)); - _block_shape_shapes.emplace_back(std::move(block_shape)); + _block_shapes.emplace_back(std::move(block_shape)); + _crop_infos.emplace_back(std::move(crop_info)); _dst_shapes.emplace_back(std::move(dst)); } @@ -102,35 +108,60 @@ protected: BatchToSpaceLayerDataset(BatchToSpaceLayerDataset &&) = default; private: - std::vector _src_shapes{}; - std::vector _block_shape_shapes{}; - std::vector _dst_shapes{}; + std::vector _src_shapes{}; + std::vector> _block_shapes{}; + std::vector _crop_infos{}; + std::vector _dst_shapes{}; }; +/** Follow NCHW data layout across all datasets. I.e. + * TensorShape(Width(X), Height(Y), Channel(Z), Batch(W)) + */ + class SmallBatchToSpaceLayerDataset final : public BatchToSpaceLayerDataset { public: SmallBatchToSpaceLayerDataset() { - add_config(TensorShape(1U, 1U, 1U, 4U), TensorShape(2U), TensorShape(2U, 2U, 1U, 1U)); - add_config(TensorShape(3U, 1U, 1U, 4U), TensorShape(2U), TensorShape(6U, 2U, 1U, 1U)); - add_config(TensorShape(1U, 2U, 2U, 4U), TensorShape(2U), TensorShape(2U, 4U, 2U, 1U)); - add_config(TensorShape(1U, 3U, 1U, 8U), TensorShape(2U), TensorShape(2U, 6U, 1U, 2U)); - add_config(TensorShape(3U, 4U, 1U, 4U), TensorShape(2U), TensorShape(6U, 8U, 1U, 1U)); - add_config(TensorShape(1U, 1U, 1U, 8U), TensorShape(4U, 2U), TensorShape(4U, 2U, 1U, 1U)); - add_config(TensorShape(3U, 1U, 1U, 8U), TensorShape(2U, 4U), TensorShape(6U, 4U, 1U, 1U)); + // Block size = 1 (effectively no batch to space) + add_config(TensorShape(1U, 1U, 1U, 4U), { 1U, 1U }, CropInfo(), TensorShape(1U, 1U, 1U, 4U)); + add_config(TensorShape(8U, 2U, 4U, 3U), { 1U, 1U }, CropInfo(), TensorShape(8U, 2U, 4U, 3U)); + // Same block size in both x and y + add_config(TensorShape(3U, 2U, 1U, 4U), { 2U, 2U }, CropInfo(), TensorShape(6U, 4U, 1U, 1U)); + add_config(TensorShape(1U, 3U, 2U, 9U), { 3U, 3U }, CropInfo(), TensorShape(3U, 9U, 2U, 1U)); + // Different block size in x and y + add_config(TensorShape(5U, 7U, 7U, 4U), { 2U, 1U }, CropInfo(), TensorShape(10U, 7U, 7U, 2U)); + add_config(TensorShape(3U, 3U, 1U, 8U), { 1U, 2U }, CropInfo(), TensorShape(3U, 6U, 1U, 4U)); + add_config(TensorShape(5U, 2U, 2U, 6U), { 3U, 2U }, CropInfo(), TensorShape(15U, 4U, 2U, 1U)); } }; +/** Relative small shapes that are still large enough to leave room for testing cropping of the output shape + */ +class SmallBatchToSpaceLayerWithCroppingDataset final : public BatchToSpaceLayerDataset +{ +public: + SmallBatchToSpaceLayerWithCroppingDataset() + { + // Crop in both dims + add_config(TensorShape(5U, 3U, 2U, 8U), { 2U, 2U }, CropInfo(1U, 1U, 2U, 1U), TensorShape(8U, 3U, 2U, 2U)); + // Left crop in x dim + add_config(TensorShape(1U, 1U, 1U, 20U), { 4U, 5U }, CropInfo(2U, 1U, 0U, 2U), TensorShape(1U, 3U, 1U, 1U)); + // Left crop in y dim + add_config(TensorShape(3U, 1U, 1U, 8U), { 2U, 4U }, CropInfo(0U, 0U, 2U, 1U), TensorShape(6U, 1U, 1U, 1U)); + } +}; class LargeBatchToSpaceLayerDataset final : public BatchToSpaceLayerDataset { public: LargeBatchToSpaceLayerDataset() { - add_config(TensorShape(64U, 32U, 2U, 4U), TensorShape(2U), TensorShape(128U, 64U, 2U, 1U)); - add_config(TensorShape(128U, 16U, 2U, 16U), TensorShape(2U), TensorShape(512U, 64U, 2U, 1U)); - add_config(TensorShape(16U, 8U, 2U, 8U), TensorShape(4U, 2U), TensorShape(64U, 16U, 2U, 1U)); - add_config(TensorShape(8U, 16U, 2U, 8U), TensorShape(2U, 4U), TensorShape(16U, 64U, 2U, 1U)); + // Same block size in both x and y + add_config(TensorShape(64U, 32U, 2U, 4U), { 2U, 2U }, CropInfo(), TensorShape(128U, 64U, 2U, 1U)); + add_config(TensorShape(128U, 16U, 2U, 18U), { 3U, 3U }, CropInfo(), TensorShape(384U, 48U, 2U, 2U)); + // Different block size in x and y + add_config(TensorShape(16U, 8U, 2U, 8U), { 4U, 1U }, CropInfo(), TensorShape(64U, 8U, 2U, 2U)); + add_config(TensorShape(8U, 16U, 2U, 8U), { 2U, 4U }, CropInfo(), TensorShape(16U, 64U, 2U, 1U)); } }; } // namespace datasets -- cgit v1.2.1