From 6f58d1ec864b9a4960181ceeab177cd0db54e2e1 Mon Sep 17 00:00:00 2001 From: Pablo Tello Date: Fri, 8 Nov 2019 13:47:53 +0000 Subject: COMPMID-2855: NEReduceMean throws error for invalid configs Change-Id: I600507d0de19d7da6c1a13edcfff0a11ea6b5264 Signed-off-by: Pablo Tello Reviewed-on: https://review.mlplatform.org/c/2254 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Reviewed-by: Giorgio Arena Reviewed-by: Giuseppe Rossini Reviewed-by: Michalis Spyrou Reviewed-by: Manuel Bottini Reviewed-by: SiCong Li --- arm_compute/core/Helpers.h | 14 ++++ arm_compute/runtime/NEON/functions/NEReduceMean.h | 2 +- src/runtime/NEON/functions/NEReduceMean.cpp | 85 +++++++++++++---------- tests/validation/NEON/ReduceMean.cpp | 22 +++--- 4 files changed, 76 insertions(+), 47 deletions(-) diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h index 87b1fdf64c..8d526e96c0 100644 --- a/arm_compute/core/Helpers.h +++ b/arm_compute/core/Helpers.h @@ -766,6 +766,20 @@ inline T wrap_around(T x, T m) return x >= 0 ? x % m : (x % m + m) % m; } +/** Convert negative coordinates to positive in the range [0, num_dims_input] + * + * @param[out] coords Array of coordinates to be converted. + * @param[in] max_value Maximum value to be used when wrapping the negative values in coords + */ +inline Coordinates &convert_negative_axis(Coordinates &coords, int max_value) +{ + for(unsigned int i = 0; i < coords.num_dimensions(); ++i) + { + coords[i] = wrap_around(coords[i], max_value); + } + return coords; +} + /** Given an integer value, this function returns the next power of two * * @param[in] x Input value diff --git a/arm_compute/runtime/NEON/functions/NEReduceMean.h b/arm_compute/runtime/NEON/functions/NEReduceMean.h index fdd8edfe87..245f7577ce 100644 --- a/arm_compute/runtime/NEON/functions/NEReduceMean.h +++ b/arm_compute/runtime/NEON/functions/NEReduceMean.h @@ -72,7 +72,7 @@ private: std::vector _reduction_kernels; std::vector _reduced_outs; NEReshapeLayer _reshape; - unsigned int _reduction_ops; + int _reduction_ops; bool _keep_dims; }; } // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp index 0b145f034d..4547a1f9b0 100644 --- a/src/runtime/NEON/functions/NEReduceMean.cpp +++ b/src/runtime/NEON/functions/NEReduceMean.cpp @@ -24,6 +24,7 @@ #include "arm_compute/runtime/NEON/functions/NEReduceMean.h" #include "arm_compute/core/CPP/Validate.h" +#include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/runtime/NEON/NEScheduler.h" @@ -34,49 +35,64 @@ NEReduceMean::NEReduceMean(std::shared_ptr memory_manager) { } -Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output) +Status validate_config(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output) { ARM_COMPUTE_UNUSED(keep_dims); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() < 1); ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions()); - TensorShape out_shape = input->tensor_shape(); const unsigned int reduction_ops = reduction_axis.num_dimensions(); const int input_dims = input->num_dimensions(); Coordinates axis_local = reduction_axis; - // Convert negative axis - for(unsigned int i = 0; i < reduction_ops; ++i) + for(unsigned int i = 0; i < axis_local.num_dimensions(); ++i) { - axis_local[i] = wrap_around(axis_local[i], input_dims); + //axis: The dimensions to reduce. Must be in the range [-rank(input_tensor), rank(input_tensor)). + ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] < (-static_cast(input->num_dimensions()))); + ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] >= static_cast(input->num_dimensions())); } - std::sort(axis_local.begin(), axis_local.begin() + reduction_ops); - for(unsigned int i = 0; i < reduction_ops; ++i) + if(output->tensor_shape().total_size() != 0) { - ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3); - ARM_COMPUTE_RETURN_ERROR_ON(static_cast(axis_local[i]) > input->num_dimensions() - 1); - if(output->total_size() > 0 && keep_dims) - { - ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1); - } - if(keep_dims) + // Only validate if not using auto_init for the output tensor + TensorShape out_shape = input->tensor_shape(); + // Validate output_shape only if not using auto_init + convert_negative_axis(axis_local, input_dims); + std::sort(axis_local.begin(), axis_local.begin() + reduction_ops); + for(unsigned int i = 0; i < reduction_ops; ++i) { - out_shape.set(axis_local[i], 1); - } - else - { - out_shape.remove_dimension(axis_local[i] - i); + ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3); + ARM_COMPUTE_RETURN_ERROR_ON(static_cast(axis_local[i]) > input->num_dimensions() - 1); + if(output->total_size() > 0 && keep_dims) + { + ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1); + } + if(keep_dims) + { + out_shape.set(axis_local[i], 1); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON(i > static_cast(axis_local[i])); + const unsigned int remove_index = axis_local[i] - i; + ARM_COMPUTE_RETURN_ERROR_ON(remove_index >= out_shape.num_dimensions()); + out_shape.remove_dimension(remove_index); + } } + const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info); } - const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info); - return Status{}; } +Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduction_axis, bool keep_dims, const ITensorInfo *output) +{ + return validate_config(input, reduction_axis, keep_dims, output); +} + void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, bool keep_dims, ITensor *output) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); @@ -86,18 +102,13 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, _reduced_outs.resize(_reduction_ops - (keep_dims ? 1 : 0)); _keep_dims = keep_dims; - Coordinates axis_local = reduction_axis; - const int input_dims = input->info()->num_dimensions(); - const unsigned int reduction_ops = reduction_axis.num_dimensions(); + Coordinates axis_local = reduction_axis; + const int input_dims = input->info()->num_dimensions(); - // Convert negative axis - for(unsigned int i = 0; i < reduction_ops; ++i) - { - axis_local[i] = wrap_around(axis_local[i], input_dims); - } + convert_negative_axis(axis_local, input_dims); // Perform reduction for every axis - for(unsigned int i = 0; i < _reduction_ops; ++i) + for(int i = 0; i < _reduction_ops; ++i) { TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (&_reduced_outs[i - 1])->info()->tensor_shape(); out_shape.set(axis_local[i], 1); @@ -116,7 +127,7 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, } // Allocate intermediate tensors - for(unsigned int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i) + for(int i = 0; i < _reduction_ops - (keep_dims ? 1 : 0); ++i) { _reduced_outs[i].allocator()->allocate(); } @@ -125,11 +136,10 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, if(!keep_dims) { TensorShape out_shape = input->info()->tensor_shape(); - // We have to sort the reduction axis vectors in order for remove_dimension // to work properly std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops); - for(unsigned int i = 0; i < _reduction_ops; ++i) + for(int i = 0; i < _reduction_ops; ++i) { out_shape.remove_dimension(axis_local[i] - i); } @@ -141,10 +151,9 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis, void NEReduceMean::run() { MemoryGroupResourceScope scope_mg(_memory_group); - - for(unsigned int i = 0; i < _reduction_ops; ++i) + for(auto &kernel : _reduction_kernels) { - _reduction_kernels[i].run(); + kernel.run(); } if(!_keep_dims) diff --git a/tests/validation/NEON/ReduceMean.cpp b/tests/validation/NEON/ReduceMean.cpp index 3cd7ce362e..6d0caf7160 100644 --- a/tests/validation/NEON/ReduceMean.cpp +++ b/tests/validation/NEON/ReduceMean.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -57,20 +57,26 @@ TEST_SUITE(ReduceMean) // *INDENT-OFF* // clang-format off -DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 3U, 16U, 2U), 1, DataType::F32), // Invalid axis TensorInfo(TensorShape(27U, 3U, 16U, 2U), 1, DataType::F32), // Invalid output shape - TensorInfo(TensorShape(32U, 16U, 16U, 2U), 1, DataType::F32) + TensorInfo(TensorShape(32U, 16U, 16U, 2U), 1, DataType::F32),// OK + TensorInfo(TensorShape{228U, 19U, 2U, 2U}, 1, DataType::F32),// OK + TensorInfo(TensorShape{228U, 19U, 2U, 1U}, 1, DataType::F32) // Cannot support axis 3 not valid }), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(27U, 3U, 1U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 3U, 1U, 2U), 1, DataType::F32), - TensorInfo(TensorShape(32U, 16U, 1U, 2U), 1, DataType::F32) + TensorInfo(TensorShape(32U, 16U, 1U, 2U), 1, DataType::F32), + TensorInfo(TensorShape(19U), 1, DataType::F32), + TensorInfo(TensorShape(19U), 1, DataType::F32) + })), - framework::dataset::make("Axis", { Coordinates(4), Coordinates(0,2), Coordinates(2) })), - framework::dataset::make("Expected", { false, false, true })), - input_info, output_info, axis, expected) + framework::dataset::make("Axis", { Coordinates(4), Coordinates(0,2), Coordinates(2), Coordinates(3,2,0), Coordinates(3,2,0) })), + framework::dataset::make("Keep", { true, true, true, false, false })), + framework::dataset::make("Expected", { false, false, true, true, false })), + input_info, output_info, axis, keep, expected) { - const Status status = NEReduceMean::validate(&input_info.clone()->set_is_resizable(false), axis, true, &output_info.clone()->set_is_resizable(false)); + const Status status = NEReduceMean::validate(&input_info.clone()->set_is_resizable(false), axis, keep, &output_info.clone()->set_is_resizable(false)); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } // clang-format on -- cgit v1.2.1