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authorMichalis Spyrou <michalis.spyrou@arm.com>2019-01-02 15:54:03 +0000
committerMichalis Spyrou <michalis.spyrou@arm.com>2019-01-08 13:11:25 +0000
commit8d1b7186ae18944678be04694d51ce7c0c9c9aa9 (patch)
tree0c9f372c7e488b59b999e1fcd0df7d5226ccd593
parent5a97b28d483eefa5810f5cf57356086090c8c894 (diff)
downloadComputeLibrary-8d1b7186ae18944678be04694d51ce7c0c9c9aa9.tar.gz
COMPMID-1865 NEReduceMean fails on shape validation
Also handle negative axis Change-Id: I28e48702d926c2f4aea7b1b674b51bebb01ce5f8 Reviewed-on: https://review.mlplatform.org/464 Reviewed-by: Matthew Bentham <matthew.bentham@arm.com> Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
-rw-r--r--src/runtime/CL/functions/CLReduceMean.cpp57
-rw-r--r--src/runtime/NEON/functions/NEReduceMean.cpp55
-rw-r--r--tests/validation/NEON/ReductionOperation.cpp8
3 files changed, 91 insertions, 29 deletions
diff --git a/src/runtime/CL/functions/CLReduceMean.cpp b/src/runtime/CL/functions/CLReduceMean.cpp
index 1016ff76e..b2d0f81f5 100644
--- a/src/runtime/CL/functions/CLReduceMean.cpp
+++ b/src/runtime/CL/functions/CLReduceMean.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -45,22 +45,31 @@ void CLReduceMean::configure(ICLTensor *input, const Coordinates &reduction_axis
_reduced_outs = arm_compute::support::cpp14::make_unique<CLTensor[]>(_reduction_ops - (keep_dims ? 1 : 0));
_keep_dims = keep_dims;
+ 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);
+ }
+
// Perform reduction for every axis
for(unsigned int i = 0; i < _reduction_ops; ++i)
{
TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (_reduced_outs.get() + i - 1)->info()->tensor_shape();
- out_shape.set(reduction_axis[i], 1);
+ out_shape.set(axis_local[i], 1);
auto in = (i == 0) ? input : (_reduced_outs.get() + i - 1);
if(i == _reduction_ops - 1 && keep_dims)
{
- _reduction_kernels[i].configure(in, output, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ _reduction_kernels[i].configure(in, output, axis_local[i], ReductionOperation::MEAN_SUM);
}
else
{
_reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type(), input->info()->quantization_info()));
_memory_group.manage(_reduced_outs.get() + i);
- _reduction_kernels[i].configure(in, _reduced_outs.get() + i, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ _reduction_kernels[i].configure(in, _reduced_outs.get() + i, axis_local[i], ReductionOperation::MEAN_SUM);
}
}
@@ -77,11 +86,10 @@ void CLReduceMean::configure(ICLTensor *input, const Coordinates &reduction_axis
// We have to sort the reduction axis vectors in order for remove_dimension
// to work properly
- Coordinates axis_copy = reduction_axis;
- std::sort(axis_copy.begin(), axis_copy.begin() + _reduction_ops);
+ std::sort(axis_local.begin(), axis_local.begin() + _reduction_ops);
for(unsigned int i = 0; i < _reduction_ops; ++i)
{
- out_shape.remove_dimension(axis_copy[i] - i);
+ out_shape.remove_dimension(axis_local[i] - i);
}
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
_reshape.configure(_reduced_outs.get() + _reduction_ops - 1, output);
@@ -90,22 +98,43 @@ void CLReduceMean::configure(ICLTensor *input, const Coordinates &reduction_axis
Status CLReduceMean::validate(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(reduction_axis.num_dimensions() > input->num_dimensions());
- for(unsigned int i = 0; i < reduction_axis.num_dimensions(); ++i)
+ TensorShape out_shape = input->tensor_shape();
+
+ Coordinates axis_sorted = reduction_axis;
+ const unsigned int reduction_ops = reduction_axis.num_dimensions();
+ const int input_dims = input->num_dimensions();
+
+ // Convert negative axis
+ for(unsigned int i = 0; i < reduction_ops; ++i)
{
- ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis[i] > 3);
- ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(reduction_axis[i]) > input->num_dimensions() - 1);
+ axis_sorted[i] = wrap_around(axis_sorted[i], input_dims);
+ }
+
+ std::sort(axis_sorted.begin(), axis_sorted.begin() + reduction_ops);
+ for(unsigned int i = 0; i < reduction_ops; ++i)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(axis_sorted[i] > 3);
+ ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_sorted[i]) > input->num_dimensions() - 1);
if(output->total_size() > 0 && keep_dims)
{
- ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(reduction_axis[i]) != 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_sorted[i]) != 1);
+ }
+ if(keep_dims)
+ {
+ out_shape.set(axis_sorted[i], 1);
+ }
+ else
+ {
+ out_shape.remove_dimension(axis_sorted[i] - i);
}
-
- ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperation::validate(input, output, reduction_axis[i], ReductionOperation::MEAN_SUM));
}
+ const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
+
return Status{};
}
diff --git a/src/runtime/NEON/functions/NEReduceMean.cpp b/src/runtime/NEON/functions/NEReduceMean.cpp
index c5229daae..dc610d55d 100644
--- a/src/runtime/NEON/functions/NEReduceMean.cpp
+++ b/src/runtime/NEON/functions/NEReduceMean.cpp
@@ -39,16 +39,37 @@ Status NEReduceMean::validate(const ITensorInfo *input, const Coordinates &reduc
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
ARM_COMPUTE_RETURN_ERROR_ON(reduction_axis.num_dimensions() > input->num_dimensions());
- for(unsigned int i = 0; i < reduction_axis.num_dimensions(); ++i)
+ 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)
+ {
+ axis_local[i] = wrap_around(axis_local[i], input_dims);
+ }
+
+ std::sort(axis_local.begin(), axis_local.begin() + reduction_ops);
+ for(unsigned int i = 0; i < reduction_ops; ++i)
{
- if(output->total_size() > 0)
+ ARM_COMPUTE_RETURN_ERROR_ON(axis_local[i] > 3);
+ ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(axis_local[i]) > input->num_dimensions() - 1);
+ if(output->total_size() > 0 && keep_dims)
{
- ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(reduction_axis[i]) != 1);
- ARM_COMPUTE_RETURN_ERROR_ON(static_cast<unsigned int>(reduction_axis[i]) > input->num_dimensions() - 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(axis_local[i]) != 1);
+ }
+ if(keep_dims)
+ {
+ out_shape.set(axis_local[i], 1);
+ }
+ else
+ {
+ out_shape.remove_dimension(axis_local[i] - i);
}
-
- ARM_COMPUTE_RETURN_ON_ERROR(NEReductionOperationKernel::validate(input, output, reduction_axis[i], ReductionOperation::MEAN_SUM));
}
+ const TensorInfo out_info = input->clone()->set_tensor_shape(out_shape);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
return Status{};
}
@@ -62,22 +83,32 @@ void NEReduceMean::configure(ITensor *input, const Coordinates &reduction_axis,
_reduced_outs = arm_compute::support::cpp14::make_unique<Tensor[]>(_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();
+
+ // Convert negative axis
+ for(unsigned int i = 0; i < reduction_ops; ++i)
+ {
+ axis_local[i] = wrap_around(axis_local[i], input_dims);
+ }
+
// Perform reduction for every axis
for(unsigned int i = 0; i < _reduction_ops; ++i)
{
TensorShape out_shape = i == 0 ? input->info()->tensor_shape() : (_reduced_outs.get() + i - 1)->info()->tensor_shape();
- out_shape.set(reduction_axis[i], 1);
+ out_shape.set(axis_local[i], 1);
auto in = (i == 0) ? input : (_reduced_outs.get() + i - 1);
if(i == _reduction_ops - 1 && keep_dims)
{
- _reduction_kernels[i].configure(in, output, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ _reduction_kernels[i].configure(in, output, axis_local[i], ReductionOperation::MEAN_SUM);
}
else
{
_reduced_outs[i].allocator()->init(TensorInfo(out_shape, input->info()->num_channels(), input->info()->data_type()));
_memory_group.manage(_reduced_outs.get() + i);
- _reduction_kernels[i].configure(in, _reduced_outs.get() + i, reduction_axis[i], ReductionOperation::MEAN_SUM);
+ _reduction_kernels[i].configure(in, _reduced_outs.get() + i, axis_local[i], ReductionOperation::MEAN_SUM);
}
}
@@ -91,9 +122,13 @@ 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)
{
- out_shape.remove_dimension(reduction_axis[i]);
+ out_shape.remove_dimension(axis_local[i] - i);
}
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(out_shape));
_reshape.configure(_reduced_outs.get() + _reduction_ops - 1, output);
diff --git a/tests/validation/NEON/ReductionOperation.cpp b/tests/validation/NEON/ReductionOperation.cpp
index 8a9d21b09..d06494036 100644
--- a/tests/validation/NEON/ReductionOperation.cpp
+++ b/tests/validation/NEON/ReductionOperation.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -60,18 +60,16 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
TensorInfo(TensorShape(128U, 64U), 2, DataType::F32), // Number of Input channels != 1
TensorInfo(TensorShape(128U, 64U), 1, DataType::S16), // DataType != F32
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis >= num_max_dimensions
- TensorInfo(TensorShape(128U, 64U), 1, DataType::F32), // Axis > 0
TensorInfo(TensorShape(128U, 64U), 1, DataType::F32)
}),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(1U, 64U), 1, DataType::F16),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
TensorInfo(TensorShape(1U, 64U), 1, DataType::S16),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
- TensorInfo(TensorShape(1U, 64U), 1, DataType::F32),
TensorInfo(TensorShape(1U, 64U), 1, DataType::F32)
})),
- framework::dataset::make("Axis", { 0U, 0U, 0U, static_cast<unsigned int>(TensorShape::num_max_dimensions), 1U, 0U })),
- framework::dataset::make("Expected", { false, false, false, false, false, true })),
+ framework::dataset::make("Axis", { 0U, 0U, 0U, static_cast<unsigned int>(TensorShape::num_max_dimensions), 0U })),
+ framework::dataset::make("Expected", { false, false, false, false, true })),
input_info, output_info, axis, expected)
{
bool is_valid = bool(NEReductionOperation::validate(&input_info.clone()->set_is_resizable(false),