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-rw-r--r--runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc172
1 files changed, 0 insertions, 172 deletions
diff --git a/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc b/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc
deleted file mode 100644
index 64236603f..000000000
--- a/runtimes/pure_arm_compute/src/internal/layers/SimplePadLayer.cc
+++ /dev/null
@@ -1,172 +0,0 @@
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "internal/layers/SimplePadLayer.h"
-#include <arm_compute/runtime/CL/CLScheduler.h>
-
-namespace
-{
-bool validate_arg(const ::arm_compute::ITensor *input, const ::arm_compute::ITensor *output,
- const ::arm_compute::ITensor *padding_size,
- const ::arm_compute::Coordinates &axises)
-{
- const int input_batch = input->info()->tensor_shape()[axises[0]];
- const int input_height = input->info()->tensor_shape()[axises[1]];
- const int input_width = input->info()->tensor_shape()[axises[2]];
- const int input_depth = input->info()->tensor_shape()[axises[3]];
-
- const int output_batch = output->info()->tensor_shape()[axises[0]];
- const int output_height = output->info()->tensor_shape()[axises[1]];
- const int output_width = output->info()->tensor_shape()[axises[2]];
- const int output_depth = output->info()->tensor_shape()[axises[3]];
-
- auto pad_batch_up = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 0}));
- auto pad_batch_down = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 0}));
- auto pad_height_top = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 1}));
- auto pad_height_bottom = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 1}));
- auto pad_width_left = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 2}));
- auto pad_width_right = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 2}));
- auto pad_depth_front = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 3}));
- auto pad_depth_back = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({1, 3}));
-
- const int padded_batch = input_batch + pad_batch_up + pad_batch_down;
- const int padded_height = input_height + pad_height_top + pad_height_bottom;
- const int padded_width = input_width + pad_width_left + pad_width_right;
- const int padded_depth = input_depth + pad_depth_front + pad_depth_back;
-
- return (padded_batch == output_batch) && (padded_height == output_height) &&
- (padded_width == output_width) && (padded_depth == output_depth);
-}
-} // namespace
-
-void SimplePadLayer::configure(::arm_compute::ITensor *input, ::arm_compute::ITensor *output,
- ::arm_compute::ITensor *padding_size,
- const ::arm_compute::Coordinates &axises)
-{
-
- const auto rank = axises.num_dimensions();
- assert(rank == 4);
- assert(input != nullptr && output != nullptr && padding_size != nullptr);
-
- for (int i = 0; i < rank; ++i)
- {
- assert(axises[i] >= 0);
- assert(axises[i] < rank);
- }
-
- _input = input;
- _output = output;
- _padding_size = padding_size;
- _axises = axises;
-}
-
-template <typename T>
-inline void ApplyPadding(const ::arm_compute::ITensor *input_data,
- const ::arm_compute::TensorShape &input_shape,
- const ::arm_compute::ITensor *padding_size,
- ::arm_compute::ITensor *output_data,
- const ::arm_compute::TensorShape &output_shape,
- const ::arm_compute::Coordinates &axises, T zero_value)
-{
-
- assert(validate_arg(input_data, output_data, padding_size, axises) &&
- "Padded Input shape does not match to output shape");
-
- const int input_batch = input_shape[axises[0]];
- const int input_height = input_shape[axises[1]];
- const int input_width = input_shape[axises[2]];
- const int input_depth = input_shape[axises[3]];
-
- const int output_batch = output_shape[axises[0]];
- const int output_height = output_shape[axises[1]];
- const int output_width = output_shape[axises[2]];
- const int output_depth = output_shape[axises[3]];
-
- // Padding size for Up, Top, Left and Front are required.
- auto pad_batch_up = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 0}));
- auto pad_height_top = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 1}));
- auto pad_width_left = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 2}));
- auto pad_depth_front = *reinterpret_cast<const int32_t *>(padding_size->ptr_to_element({0, 3}));
-
- for (int out_b = 0; out_b < output_batch; ++out_b)
- {
- for (int out_h = 0; out_h < output_height; ++out_h)
- {
- for (int out_w = 0; out_w < output_width; ++out_w)
- {
- for (int out_d = 0; out_d < output_depth; ++out_d)
- {
- auto output_id = asARMComputeCoordinates(
- ::arm_compute::Coordinates{out_b, out_h, out_w, out_d}, axises);
-
- if (out_b < pad_batch_up || out_b >= (input_batch + pad_batch_up) ||
- out_h < pad_height_top || out_h >= (input_height + pad_height_top) ||
- out_w < pad_width_left || out_w >= (input_width + pad_width_left) ||
- out_d < pad_depth_front || out_d >= (input_depth + pad_depth_front))
- {
- *reinterpret_cast<T *>(output_data->ptr_to_element(output_id)) = zero_value;
- }
- else
- {
- auto input_id = asARMComputeCoordinates(
- ::arm_compute::Coordinates{out_b - pad_batch_up, out_h - pad_height_top,
- out_w - pad_width_left, out_d - pad_depth_front},
- axises);
- *reinterpret_cast<T *>(output_data->ptr_to_element(output_id)) =
- *reinterpret_cast<T *>(input_data->ptr_to_element(input_id));
- }
- }
- }
- }
- }
-}
-void SimplePadLayer::run()
-{
- if (::internal::arm_compute::isGpuMode())
- {
- auto &q = ::arm_compute::CLScheduler::get().queue();
-
- CAST_CL(_input)->map(q);
- CAST_CL(_output)->map(q);
- CAST_CL(_padding_size)->map(q);
- }
-
- switch (_input->info()->data_type())
- {
- case ::arm_compute::DataType::U8:
- case ::arm_compute::DataType::QASYMM8:
- ApplyPadding<uint8_t>(_input, _input->info()->tensor_shape(), _padding_size, _output,
- _output->info()->tensor_shape(), _axises,
- _input->info()->quantization_info().offset);
- break;
- case ::arm_compute::DataType::F32:
- ApplyPadding<float>(_input, _input->info()->tensor_shape(), _padding_size, _output,
- _output->info()->tensor_shape(), _axises, 0.0f);
- break;
- default:
- ARM_COMPUTE_ERROR("DataType not supported");
- break;
- }
-
- if (::internal::arm_compute::isGpuMode())
- {
- auto &q = ::arm_compute::CLScheduler::get().queue();
-
- CAST_CL(_input)->unmap(q);
- CAST_CL(_output)->unmap(q);
- CAST_CL(_padding_size)->unmap(q);
- }
-}
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