arm_compute v19.05
diff --git a/src/runtime/CL/functions/CLPadLayer.cpp b/src/runtime/CL/functions/CLPadLayer.cpp
index 3aa1b1e..99e3121 100644
--- a/src/runtime/CL/functions/CLPadLayer.cpp
+++ b/src/runtime/CL/functions/CLPadLayer.cpp
@@ -25,39 +25,293 @@
#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(), _concat_functions(), _slice_results(), _concat_results()
{
}
-void CLPadLayer::configure(ICLTensor *input, ICLTensor *output, const PaddingList &padding, PixelValue constant_value)
+void CLPadLayer::configure_constant_mode(ICLTensor *input, ICLTensor *output, const PaddingList &padding, const PixelValue constant_value)
{
- // Copy the input to the output
- _copy_kernel.configure(input, output, padding);
-
- // Set the pages of the output to zero
+ // Set the pages of the output to the constant_value.
_memset_kernel.configure(output, constant_value);
- // Fill padding on the first two dimensions with zeros
- _fillborder_kernel.configure(input, input->info()->padding(), BorderMode::CONSTANT, 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);
}
-Status CLPadLayer::validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding, PixelValue constant_value)
+void CLPadLayer::configure_reflect_symmetric_mode(ICLTensor *input, ICLTensor *output)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLMemsetKernel::validate(input, constant_value));
- ARM_COMPUTE_RETURN_ON_ERROR(CLCopyKernel::validate(input, output, padding));
+ 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.resize(2 * _num_dimensions);
+ _slice_results.resize(2 * _num_dimensions);
+ _concat_functions.resize(_num_dimensions);
+ _concat_results.resize(_num_dimensions - 1);
+
+ Coordinates starts_before{};
+ Coordinates ends_before{};
+ Coordinates starts_after{};
+ Coordinates ends_after{};
+ Coordinates 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, 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_ERROR_THROW_ON(validate(input->info(), output->info(), padding, constant_value, mode));
+
+ _padding = padding;
+ _mode = mode;
+
+ 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_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