| /* |
| * Copyright (c) 2018-2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/runtime/CL/functions/CLPadLayer.h" |
| |
| #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(), _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_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() |
| { |
| 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 |