COMPMID-2008: Add support for "reflect" padding mode in CLPad
Change-Id: I469f8173d5c4a1b6f03b52b9ddd33928dacd1e7b
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/869
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
diff --git a/arm_compute/runtime/CL/functions/CLPadLayer.h b/arm_compute/runtime/CL/functions/CLPadLayer.h
index 0179441..33b09d6 100644
--- a/arm_compute/runtime/CL/functions/CLPadLayer.h
+++ b/arm_compute/runtime/CL/functions/CLPadLayer.h
@@ -25,10 +25,13 @@
#define __ARM_COMPUTE_CLPADLAYER_H__
#include "arm_compute/core/CL/kernels/CLCopyKernel.h"
-#include "arm_compute/core/CL/kernels/CLFillBorderKernel.h"
#include "arm_compute/core/CL/kernels/CLMemsetKernel.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/CL/functions/CLConcatenateLayer.h"
+
#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/functions/CLStridedSlice.h"
#include "arm_compute/runtime/IFunction.h"
namespace arm_compute
@@ -77,9 +80,18 @@
void run() override;
private:
- CLCopyKernel _copy_kernel;
- CLFillBorderKernel _fillborder_kernel;
- CLMemsetKernel _memset_kernel;
+ void configure_constant_mode(ICLTensor *input, ICLTensor *output, const PaddingList &padding, const PixelValue constant_value);
+ void configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output);
+
+ CLCopyKernel _copy_kernel;
+ PaddingMode _mode;
+ PaddingList _padding;
+ CLMemsetKernel _memset_kernel;
+ size_t _num_dimensions;
+ std::unique_ptr<CLStridedSlice[]> _slice_functions;
+ std::unique_ptr<CLConcatenateLayer[]> _concat_functions;
+ std::unique_ptr<CLTensor[]> _slice_results;
+ std::unique_ptr<CLTensor[]> _concat_results;
};
} // namespace arm_compute
#endif /*__ARM_COMPUTE_PADLAYER_H__ */
diff --git a/src/runtime/CL/functions/CLPadLayer.cpp b/src/runtime/CL/functions/CLPadLayer.cpp
index fac2364..f88cb38 100644
--- a/src/runtime/CL/functions/CLPadLayer.cpp
+++ b/src/runtime/CL/functions/CLPadLayer.cpp
@@ -25,41 +25,288 @@
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "support/ToolchainSupport.h"
namespace arm_compute
{
CLPadLayer::CLPadLayer()
- : _copy_kernel(), _fillborder_kernel(), _memset_kernel()
+ : _copy_kernel(), _mode(), _padding(), _memset_kernel(), _num_dimensions(0), _slice_functions(nullptr), _concat_functions(nullptr), _slice_results(nullptr), _concat_results(nullptr)
{
}
+void CLPadLayer::configure_constant_mode(ICLTensor *input, ICLTensor *output, const PaddingList &padding, const PixelValue constant_value)
+{
+ // Set the pages of the output to the constant_value.
+ _memset_kernel.configure(output, constant_value);
+
+ // Fill out padding list with zeroes.
+ PaddingList padding_extended = padding;
+ for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
+ {
+ padding_extended.emplace_back(PaddingInfo{ 0, 0 });
+ }
+
+ // Create a window within the output tensor where the input will be copied.
+ Window copy_window = Window();
+ for(uint32_t i = 0; i < output->info()->num_dimensions(); ++i)
+ {
+ copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->info()->dimension(i), 1));
+ }
+ // Copy the input to the output, leaving the padding filled with the constant_value.
+ _copy_kernel.configure(input, output, PaddingList(), ©_window);
+}
+
+void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output)
+{
+ int64_t last_padding_dimension = _padding.size() - 1;
+ // Reflecting can be performed by effectively unfolding the input as follows:
+ // For each dimension starting at DimX:
+ // Create a before and after slice, which values depend on the selected padding mode
+ // Concatenate the before and after padding with the tensor to be padded
+
+ // Two strided slice functions will be required for each dimension padded as well as a
+ // concatenate function and the tensors to hold the temporary results.
+ _slice_functions = arm_compute::support::cpp14::make_unique<CLStridedSlice[]>(2 * _num_dimensions);
+ _slice_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(2 * _num_dimensions);
+ _concat_functions = arm_compute::support::cpp14::make_unique<CLConcatenateLayer[]>(_num_dimensions);
+ _concat_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_dimensions - 1);
+ Coordinates starts_before, ends_before, starts_after, ends_after, strides;
+ ICLTensor *prev = input;
+ for(uint32_t i = 0; i < _num_dimensions; ++i)
+ {
+ // Values in strides from the previous dimensions need to be set to 1 to avoid reversing again.
+ if(i > 0)
+ {
+ strides.set(i - 1, 1);
+ }
+
+ if(_padding[i].first > 0 || _padding[i].second > 0)
+ {
+ // Set the starts, ends, and strides values for the current dimension.
+ // Due to the bit masks passed to strided slice, the values below the current dimension in
+ // starts and ends will be ignored so do not need to be modified.
+ if(_mode == PaddingMode::REFLECT)
+ {
+ starts_before.set(i, _padding[i].first);
+ ends_before.set(i, 0);
+ starts_after.set(i, input->info()->dimension(i) - 2);
+ ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 2);
+ strides.set(i, -1);
+ }
+ else
+ {
+ starts_before.set(i, _padding[i].first - 1);
+ ends_before.set(i, -1);
+ starts_after.set(i, input->info()->dimension(i) - 1);
+ ends_after.set(i, input->info()->dimension(i) - _padding[i].second - 1);
+ strides.set(i, -1);
+ }
+
+ // Strided slice wraps negative indexes around to the end of the range,
+ // instead this should indicate use of the full range and so the bit mask will be modified.
+ const int32_t begin_mask_before = starts_before[i] < 0 ? ~0 : ~(1u << i);
+ const int32_t end_mask_before = ends_before[i] < 0 ? ~0 : ~(1u << i);
+ const int32_t begin_mask_after = starts_after[i] < 0 ? ~0 : ~(1u << i);
+ const int32_t end_mask_after = ends_after[i] < 0 ? ~0 : ~(1u << i);
+
+ // Reflect the input values for the padding before and after the input.
+ std::vector<ICLTensor *> concat_vector;
+ if(_padding[i].first > 0)
+ {
+ if(i < prev->info()->num_dimensions())
+ {
+ _slice_functions[2 * i].configure(prev, &_slice_results[2 * i], starts_before, ends_before, strides, begin_mask_before, end_mask_before);
+ concat_vector.push_back(&_slice_results[2 * i]);
+ }
+ else
+ {
+ // Performing the slice is unnecessary if the result would simply be a copy of the tensor.
+ concat_vector.push_back(prev);
+ }
+ }
+ concat_vector.push_back(prev);
+ if(_padding[i].second > 0)
+ {
+ if(i < prev->info()->num_dimensions())
+ {
+ _slice_functions[2 * i + 1].configure(prev, &_slice_results[2 * i + 1], starts_after, ends_after, strides, begin_mask_after, end_mask_after);
+ concat_vector.push_back(&_slice_results[2 * i + 1]);
+ }
+ else
+ {
+ // Performing the slice is unnecessary if the result would simply be a copy of the tensor.
+ concat_vector.push_back(prev);
+ }
+ }
+ // Concatenate the padding before and after with the input.
+ ICLTensor *out = (static_cast<int32_t>(i) == last_padding_dimension) ? output : &_concat_results[i];
+ _concat_functions[i].configure(concat_vector, out, get_index_data_layout_dimension(prev->info()->data_layout(), i));
+ prev = out;
+ }
+ }
+ for(uint32_t i = 0; i < _num_dimensions; ++i)
+ {
+ if((static_cast<int32_t>(i) != last_padding_dimension))
+ {
+ _concat_results[i].allocator()->allocate();
+ }
+ _slice_results[2 * i].allocator()->allocate();
+ _slice_results[2 * i + 1].allocator()->allocate();
+ }
+}
+
void CLPadLayer::configure(ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
{
- ARM_COMPUTE_UNUSED(mode);
- // Copy the input to the output
- _copy_kernel.configure(input, output, padding);
+ ARM_COMPUTE_ERROR_THROW_ON(validate(input->info(), output->info(), padding, constant_value, mode));
- // Set the pages of the output to zero
- _memset_kernel.configure(output, constant_value);
+ _padding = padding;
+ _mode = mode;
- // Fill padding on the first two dimensions with zeros
- _fillborder_kernel.configure(input, input->info()->padding(), BorderMode::CONSTANT, constant_value);
+ TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->info()->tensor_shape(), _padding);
+
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(padded_shape));
+
+ // Find the last dimension requiring padding so that it is known when to write to output and whether any padding is applied.
+ int64_t last_padding_dimension = _padding.size() - 1;
+ for(; last_padding_dimension >= 0; --last_padding_dimension)
+ {
+ if(_padding[last_padding_dimension].first > 0 || _padding[last_padding_dimension].second > 0)
+ {
+ break;
+ }
+ }
+ _num_dimensions = last_padding_dimension + 1;
+ if(_num_dimensions > 0)
+ {
+ switch(_mode)
+ {
+ case PaddingMode::CONSTANT:
+ {
+ configure_constant_mode(input, output, padding, constant_value);
+ break;
+ }
+ case PaddingMode::REFLECT:
+ case PaddingMode::SYMMETRIC:
+ {
+ configure_reflect_symmetric_mode(input, output);
+ break;
+ }
+ default:
+ ARM_COMPUTE_ERROR("Padding mode not supported.");
+ }
+ }
+ else
+ {
+ // Copy the input to the whole output if no padding is applied
+ _copy_kernel.configure(input, output);
+ }
}
Status CLPadLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, PixelValue constant_value, PaddingMode mode)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(input, constant_value));
- ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, padding));
- ARM_COMPUTE_RETURN_ERROR_ON(mode != PaddingMode::CONSTANT);
+ ARM_COMPUTE_RETURN_ERROR_ON(padding.size() > input->num_dimensions());
+ TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input->tensor_shape(), padding);
+
+ // Use CLCopyKernel and CLMemsetKernel to validate all padding modes as this includes all of the shape and info validation.
+ PaddingList padding_extended = padding;
+ for(size_t i = padding.size(); i < TensorShape::num_max_dimensions; i++)
+ {
+ padding_extended.emplace_back(PaddingInfo{ 0, 0 });
+ }
+
+ Window copy_window = Window();
+ for(uint32_t i = 0; i < padded_shape.num_dimensions(); ++i)
+ {
+ copy_window.set(i, Window::Dimension(padding_extended[i].first, padding_extended[i].first + input->dimension(i), 1));
+ }
+ if(output->total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), padded_shape);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(output, input);
+ ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, PaddingList(), ©_window));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(output, constant_value));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, &input->clone()->set_tensor_shape(padded_shape), PaddingList(), ©_window));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(&input->clone()->set_tensor_shape(padded_shape), constant_value));
+ }
+
+ switch(mode)
+ {
+ case PaddingMode::CONSTANT:
+ {
+ break;
+ }
+ case PaddingMode::REFLECT:
+ case PaddingMode::SYMMETRIC:
+ {
+ for(uint32_t i = 0; i < padding.size(); ++i)
+ {
+ if(mode == PaddingMode::REFLECT)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first >= input->dimension(i));
+ ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second >= input->dimension(i));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(padding[i].first > input->dimension(i));
+ ARM_COMPUTE_RETURN_ERROR_ON(padding[i].second > input->dimension(i));
+ }
+ }
+ break;
+ }
+ default:
+ {
+ ARM_COMPUTE_ERROR("Invalid mode");
+ }
+ }
return Status{};
}
void CLPadLayer::run()
{
- CLScheduler::get().enqueue(_memset_kernel, false);
- CLScheduler::get().enqueue(_fillborder_kernel, false);
- CLScheduler::get().enqueue(_copy_kernel, true);
+ if(_num_dimensions > 0)
+ {
+ switch(_mode)
+ {
+ case PaddingMode::CONSTANT:
+ {
+ CLScheduler::get().enqueue(_memset_kernel, false);
+ CLScheduler::get().enqueue(_copy_kernel, true);
+ break;
+ }
+ case PaddingMode::REFLECT:
+ case PaddingMode::SYMMETRIC:
+ {
+ for(uint32_t i = 0; i < _num_dimensions; ++i)
+ {
+ if(_padding[i].first > 0 || _padding[i].second > 0)
+ {
+ if(_padding[i].first > 0 && _slice_results[2 * i].info()->total_size() > 0)
+ {
+ _slice_functions[2 * i].run();
+ }
+ if(_padding[i].second > 0 && _slice_results[2 * i + 1].info()->total_size() > 0)
+ {
+ _slice_functions[2 * i + 1].run();
+ }
+ CLScheduler::get().sync();
+ _concat_functions[i].run();
+ CLScheduler::get().sync();
+ }
+ }
+ break;
+ }
+ default:
+ ARM_COMPUTE_ERROR("Padding mode not supported.");
+ }
+ }
+ else
+ {
+ CLScheduler::get().enqueue(_copy_kernel, true);
+ }
}
} // namespace arm_compute
diff --git a/tests/validation/CL/PadLayer.cpp b/tests/validation/CL/PadLayer.cpp
index 9430b12..2ad29fc 100644
--- a/tests/validation/CL/PadLayer.cpp
+++ b/tests/validation/CL/PadLayer.cpp
@@ -43,9 +43,9 @@
const auto PaddingSizesDataset = framework::dataset::make("PaddingSize", { PaddingList{ { 0, 0 } },
PaddingList{ { 1, 1 } },
PaddingList{ { 1, 1 }, { 2, 2 } },
- PaddingList{ { 1, 1 }, { 1, 1 }, { 1, 1 }, { 1, 1 } },
- PaddingList{ { 0, 0 }, { 1, 0 }, { 0, 1 }, { 1, 2 } },
- PaddingList{ { 0, 0 }, { 0, 0 }, { 0, 0 }, { 1, 1 } }
+ PaddingList{ { 1, 1 }, { 1, 1 }, { 1, 1 } },
+ PaddingList{ { 0, 0 }, { 1, 0 }, { 0, 1 } },
+ PaddingList{ { 0, 0 }, { 0, 0 }, { 0, 0 } }
});
} // namespace
@@ -55,32 +55,44 @@
// *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, 13U, 2U), 1, DataType::F32), // Mismatching data type input/output
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes with padding
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes dimension
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
- TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32)
+ TensorInfo(TensorShape(32U, 13U), 1, DataType::F32) // Invalid padding list
}),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
TensorInfo(TensorShape(28U, 11U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(29U, 17U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(29U, 15U, 4U, 3U), 1, DataType::F32),
- TensorInfo(TensorShape(27U, 14U, 3U, 4U), 1, DataType::F32),
- TensorInfo(TensorShape(32U, 13U, 2U, 3U), 1, DataType::F32)
+ TensorInfo(TensorShape(29U, 17U, 2U), 1, DataType::F32),
+ TensorInfo(TensorShape(32U, 13U), 1, DataType::F32)
})),
framework::dataset::make("PaddingSize", { PaddingList{{0, 0}},
- PaddingList{{1, 1}},
- PaddingList{{1, 1}, {2, 2}},
- PaddingList{{1,1}, {1,1}, {1,1}, {1,1}},
- PaddingList{{0,0}, {1,0}, {0,1}, {1,2}},
- PaddingList{{0,0}, {0,0}, {0,0}, {1,1}}
- })),
- framework::dataset::make("Expected", { false, false, true, true, true, true })),
- input_info, output_info, padding, expected)
+ PaddingList{{1, 1}},
+ PaddingList{{1, 1}, {2, 2}},
+ PaddingList{{1,1}, {1,1}, {1,1}},
+ PaddingList{{1, 1}, {2, 2}},
+ PaddingList{{0,0}, {0,0}, {1,1}}
+ })),
+ framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT,
+ PaddingMode::CONSTANT,
+ PaddingMode::CONSTANT,
+ PaddingMode::SYMMETRIC,
+ PaddingMode::REFLECT,
+ PaddingMode::REFLECT
+})),
+ framework::dataset::make("Expected", { false,
+ false,
+ true,
+ false,
+ true,
+ false })),
+ input_info, output_info, padding, mode, expected)
{
- ARM_COMPUTE_EXPECT(bool(CLPadLayer::validate(&input_info.clone()->set_is_resizable(true), &output_info.clone()->set_is_resizable(true), padding)) == expected, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bool(CLPadLayer::validate(&input_info.clone()->set_is_resizable(true), &output_info.clone()->set_is_resizable(true), padding, PixelValue(), mode)) == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -92,11 +104,9 @@
TEST_SUITE(Float)
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunPadding, CLPaddingFixture<float>, framework::DatasetMode::ALL,
- combine(
- combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", { DataType::F32 })),
- PaddingSizesDataset),
- framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLPaddingFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(datasets::Small3DShapes(), framework::dataset::make("DataType", { DataType::F32 })), PaddingSizesDataset),
+ framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT, PaddingMode::REFLECT, PaddingMode::SYMMETRIC })))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -104,11 +114,9 @@
TEST_SUITE_END() // FP32
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunPadding, CLPaddingFixture<half>, framework::DatasetMode::ALL,
- combine(
- combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", { DataType::F16 })),
- PaddingSizesDataset),
- framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLPaddingFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::Large3DShapes(), framework::dataset::make("DataType", { DataType::F16 })), PaddingSizesDataset),
+ framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT, PaddingMode::REFLECT })))
{
// Validate output
validate(CLAccessor(_target), _reference);
@@ -116,27 +124,18 @@
TEST_SUITE_END() // FP16
TEST_SUITE_END() // Float
-TEST_SUITE(Integer)
-TEST_SUITE(S8)
-FIXTURE_DATA_TEST_CASE(RunPadding, CLPaddingFixture<int8_t>, framework::DatasetMode::ALL,
- combine(
- combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", { DataType::S8 })),
- PaddingSizesDataset),
- framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT })))
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLPaddingFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(datasets::Small3DShapes(), framework::dataset::make("DataType", { DataType::QASYMM8 })), PaddingSizesDataset),
+ framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT, PaddingMode::REFLECT })))
{
// Validate output
validate(CLAccessor(_target), _reference);
}
-TEST_SUITE_END() // S8
-TEST_SUITE_END() // Integer
-
-TEST_SUITE(Quantized)
-TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunPadding, CLPaddingFixture<uint8_t>, framework::DatasetMode::ALL,
- combine(
- combine(combine(datasets::SmallShapes(), framework::dataset::make("DataType", { DataType::QASYMM8 })),
- PaddingSizesDataset),
- framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLPaddingFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(datasets::Large3DShapes(), framework::dataset::make("DataType", { DataType::QASYMM8 })), PaddingSizesDataset),
+ framework::dataset::make("PaddingMode", { PaddingMode::CONSTANT, PaddingMode::REFLECT })))
{
// Validate output
validate(CLAccessor(_target), _reference);