COMPMID-344 Updated doxygen

Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae

1 files changed, 247 insertions, 0 deletions

diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
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+++ b/src/runtime/CL/functions/CLConvolutionLayer.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/CL/functions/CLConvolutionLayer.h"
+
+#include "arm_compute/core/PixelValue.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include <cmath>
+#include <tuple>
+
+using namespace arm_compute;
+
+CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights()
+    : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+{
+}
+
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 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(weights, biases, output);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases, output);
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+        ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    const bool _has_bias = (biases != nullptr);
+
+    _transpose1xW = transpose1xW;
+
+    if(transpose1xW)
+    {
+        // Create tensor to store the reshaped weights
+        const unsigned int mat_weights_cols = weights->info()->dimension(3);
+        const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0);
+        TensorShape        shape_wr(mat_weights_cols, mat_weights_rows);
+        const DataType     dt = weights->info()->data_type();
+        TensorInfo         info_wr(shape_wr, 1, dt);
+
+        _weights_reshaped.allocator()->init(info_wr);
+        _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+        _weights_transposed_kernel.configure(&_weights_reshaped, output);
+        _weights_reshaped.allocator()->allocate();
+    }
+    else
+    {
+        _weights_reshape_kernel.configure(weights, biases, output);
+    }
+}
+
+void CLConvolutionLayerReshapeWeights::run()
+{
+    cl::CommandQueue q = CLScheduler::get().queue();
+    CLScheduler::get().enqueue(_weights_reshape_kernel);
+    if(_transpose1xW)
+    {
+        CLScheduler::get().enqueue(_weights_transposed_kernel);
+    }
+}
+
+CLConvolutionLayer::CLConvolutionLayer()
+    : _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(),
+      _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+{
+}
+
+void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+{
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+    ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
+    ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F16, DataType::F32);
+        ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+        ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
+        ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+    }
+
+    _has_bias             = (biases != nullptr);
+    _are_weights_reshaped = weights_info.are_reshaped();
+
+    // 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;
+
+    const unsigned int kernel_width = _are_weights_reshaped ? weights_info.kernel_size() : weights->info()->dimension(0);
+    std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width,
+                                                 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");
+
+    // Check if its a "fully connected" convolution
+    _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+
+    // Create tensor to store the reshaped weights
+    size_t mat_weights_cols = weights->info()->dimension(3);
+    size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
+    if(_are_weights_reshaped)
+    {
+        mat_weights_cols                         = output->info()->dimension(2);
+        const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
+        mat_weights_rows                         = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols);
+    }
+    else
+    {
+        if(_is_fully_connected_convolution)
+        {
+            // Create tensor to store the reshaped weights
+            TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
+            TensorInfo  info_wr(shape_wr, 1, weights->info()->data_type());
+            _weights_reshaped.allocator()->init(info_wr);
+            _reshape_weights.configure(weights, biases, &_weights_reshaped, false);
+            weights = &_weights_reshaped;
+        }
+        else
+        {
+            // Create tensor to store transposed weights
+            TensorShape shape_wt(mat_weights_rows * 4, static_cast<size_t>(std::ceil(mat_weights_cols / 4.f)));
+            TensorInfo  info_wt(shape_wt, 1, weights->info()->data_type());
+            _weights_transposed.allocator()->init(info_wt);
+            _reshape_weights.configure(weights, biases, &_weights_transposed, true);
+            weights = &_weights_transposed;
+        }
+    }
+    // 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 tensor (interleave) to prepare input tensor for GEMM
+    if(!_is_fully_connected_convolution)
+    {
+        TensorShape shape_interleaved = shape_im2col;
+        shape_interleaved.set(0, shape_interleaved.x() * 4);
+        shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4.f));
+        _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type()));
+    }
+
+    // Create GEMM 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);
+    _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+
+    if(_is_fully_connected_convolution)
+    {
+        _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
+    }
+    else
+    {
+        _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+        _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
+    }
+
+    if(!_are_weights_reshaped)
+    {
+        if(!_is_fully_connected_convolution)
+        {
+            _weights_transposed.allocator()->allocate();
+        }
+        else
+        {
+            _weights_reshaped.allocator()->allocate();
+        }
+    }
+
+    _input_im2col_reshaped.allocator()->allocate();
+    if(!_is_fully_connected_convolution)
+    {
+        _input_interleaved_reshaped.allocator()->allocate();
+    }
+    _gemm_output.allocator()->allocate();
+}
+
+void CLConvolutionLayer::run()
+{
+    // Run weights reshaping (Runs once for every configure)
+    if(!_are_weights_reshaped)
+    {
+        _are_weights_reshaped = true;
+        _reshape_weights.run();
+    }
+
+    // Run input reshaping
+    CLScheduler::get().enqueue(_input_im2col_kernel);
+    if(!_is_fully_connected_convolution)
+    {
+        CLScheduler::get().enqueue(_input_interleave_kernel);
+    }
+
+    // Runs matrix multiply on reshaped matrices
+    CLScheduler::get().enqueue(_mm_kernel);
+
+    // Reshape output matrix
+    CLScheduler::get().enqueue(_output_col2im_kernel, false);
+}