COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 131 insertions, 0 deletions

diff --git a/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
new file mode 100644
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+++ b/src/runtime/NEON/functions/NELocallyConnectedLayer.cpp
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+/*
+ * Copyright (c) 2017 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/NEON/functions/NELocallyConnectedLayer.h"
+
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+#include <cmath>
+#include <tuple>
+
+using namespace arm_compute;
+
+NELocallyConnectedLayer::NELocallyConnectedLayer()
+    : _input_im2col_kernel(), _weights_reshape_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _weights_reshaped(), _gemm_output(), _is_first_run(false)
+{
+}
+
+void NELocallyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 2);
+    }
+
+    bool _has_bias = (biases != nullptr);
+    _is_first_run  = true;
+
+    // Get parameters for conv_info
+    unsigned int stride_x = 0;
+    unsigned int stride_y = 0;
+    unsigned int pad_x    = 0;
+    unsigned int pad_y    = 0;
+    std::tie(stride_x, stride_y) = conv_info.stride();
+    std::tie(pad_x, pad_y)       = conv_info.pad();
+
+    // Get convolved dimensions
+    unsigned int conv_w = 0;
+    unsigned int conv_h = 0;
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0),
+                                                 stride_x, stride_y, pad_x, pad_y, conv_info.round());
+
+    ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+    ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(4) != (conv_w * conv_h), "Weights shape does not match the expected one");
+
+    // Create tensor to store the reshaped weights
+    const size_t mat_weights_cols = weights->info()->dimension(3);
+    const size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
+    const size_t mat_weights_num  = weights->info()->dimension(4);
+
+    const TensorShape shape_wr(mat_weights_cols, mat_weights_rows, mat_weights_num);
+
+    _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
+
+    // Create tensor to store im2col reshaped inputs
+    const size_t mat_input_cols = mat_weights_rows;
+    const size_t mat_input_rows = conv_w * conv_h;
+    TensorShape  shape_im2col   = input->info()->tensor_shape();
+    shape_im2col.set(0, mat_input_cols);
+    shape_im2col.set(1, mat_input_rows);
+    shape_im2col.set(2, 1);
+
+    _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
+
+    // Create locally connected layer output tensor
+    TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
+    shape_gemm.set(0, mat_weights_cols);
+    shape_gemm.set(1, mat_input_rows);
+    _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
+
+    // Configure kernels
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, std::make_pair(conv_w, conv_h), conv_info, _has_bias);
+    _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+    _mm_kernel.configure(&_input_im2col_reshaped, &_weights_reshaped, &_gemm_output);
+    _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+
+    // Allocate intermediate tensors
+    _weights_reshaped.allocator()->allocate();
+    _input_im2col_reshaped.allocator()->allocate();
+    _gemm_output.allocator()->allocate();
+}
+
+void NELocallyConnectedLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(_is_first_run)
+    {
+        _is_first_run = false;
+        NEScheduler::get().schedule(&_weights_reshape_kernel, 3);
+    }
+
+    // Run input reshaping
+    NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+
+    // Runs GEMM on reshaped matrices
+    NEScheduler::get().schedule(&_mm_kernel, Window::DimX);
+
+    // Reshape output matrix
+    NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+}