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-rw-r--r--src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp194
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diff --git a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
deleted file mode 100644
index d391eddf84..0000000000
--- a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp
+++ /dev/null
@@ -1,194 +0,0 @@
-/*
- * Copyright (c) 2017-2020 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/GLES_COMPUTE/functions/GCFullyConnectedLayer.h"
-
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h"
-#include "support/MemorySupport.h"
-
-#include <algorithm>
-
-using namespace arm_compute;
-
-void GCFullyConnectedLayerReshapeWeights::configure(const IGCTensor *input, IGCTensor *output)
-{
- auto k = arm_compute::support::cpp14::make_unique<GCTransposeKernel>();
- k->configure(input, output);
- _kernel = std::move(k);
-}
-
-GCFullyConnectedLayer::GCFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
- : _memory_group(std::move(memory_manager)), _weights_manager(std::move(weights_manager)), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(),
- _reshape_weights_output(), _original_weights(nullptr), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false)
-{
-}
-
-void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
-{
- ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
-
- const DataType dt = input->info()->data_type();
-
- // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
-
- // Initialize output tensor for im2col
- TensorShape shape_im2col;
- shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
- shape_im2col.set(1, input->info()->dimension(3));
- shape_im2col.set(2, input->info()->dimension(4));
- shape_im2col.set(3, input->info()->dimension(5));
- _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt));
-
- // Configure im2col kernel
- _memory_group.manage(&_im2col_output);
- _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false);
-
- // Allocate the output tensor for im2col once all the configure methods have been called
- _im2col_output.allocator()->allocate();
-}
-
-void GCFullyConnectedLayer::configure_fc_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
-{
- ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
-
- // Configure matrix multiply kernel
- _mm_kernel.configure(input, weights, output, 1.0f, false);
-}
-
-void GCFullyConnectedLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output,
- FullyConnectedLayerInfo fc_info)
-{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
-
- _original_weights = weights;
- _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
- _is_fc_after_conv = true;
- _accumulate_biases = false;
-
- if(biases != nullptr)
- {
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
-
- _accumulate_biases = true;
-
- // Configure accumulate biases kernel
- _accumulate_biases_kernel.configure(output, biases);
- }
-
- // With the Fully Connected layer we can have 4 different cases:
- // 1) Convolution layer -> Fully Connected layer without batches
- // 2) Fully Connected layer -> Fully Connected layer without batches
- // 3) Convolution layer -> Fully Connected layer with batches
- // 4) Fully Connected layer -> Fully Connected layer with batches
-
- const IGCTensor *weights_to_use = weights;
-
- if(!_are_weights_reshaped)
- {
- weights_to_use = &_reshape_weights_output;
-
- // Reshape the weights
- _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
- }
-
- // Check if we have a fully connected layer with batches
- const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
-
- if(is_batched_fc_layer)
- {
- _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
- input->info()->tensor_shape().cend(),
- output->info()->tensor_shape().cbegin() + 1));
- }
- else
- {
- _is_fc_after_conv = input->info()->num_dimensions() > 1;
- }
-
- if(_is_fc_after_conv)
- {
- // Fully Connected layer after a Convolution Layer without batches
- configure_conv_fc(input, weights_to_use, output);
- }
- else
- {
- // Fully Connected layer after a Fully Connected Layer without batches
- configure_fc_fc(input, weights_to_use, output);
- }
-
- ARM_COMPUTE_ERROR_ON(fc_info.retain_internal_weights && _reshape_weights_output.gc_buffer() == 0);
- _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
-}
-
-void GCFullyConnectedLayer::run()
-{
- prepare();
-
- MemoryGroupResourceScope scope_mg(_memory_group);
-
- // Linearize input if it comes from a convolutional layer
- if(_is_fc_after_conv)
- {
- GCScheduler::get().dispatch(_im2col_kernel, false);
- }
-
- if(!_are_weights_reshaped || _is_fc_after_conv)
- {
- GCScheduler::get().memory_barrier();
- }
-
- // Run matrix multiply
- GCScheduler::get().dispatch(_mm_kernel, !_accumulate_biases);
-
- // Accumulate biases if provided
- if(_accumulate_biases)
- {
- GCScheduler::get().memory_barrier();
-
- GCScheduler::get().dispatch(_accumulate_biases_kernel);
- }
-}
-
-void GCFullyConnectedLayer::prepare()
-{
- // Reshape of the weights (happens only once)
- if(!_are_weights_reshaped)
- {
- ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
-
- // Run reshape weights kernel and mark weights as unused
- _reshape_weights_output.allocator()->allocate();
- _reshape_weights_kernel.run();
-
- // Mark original weights tensor as unused
- _original_weights->mark_as_unused();
-
- _are_weights_reshaped = true;
- }
-}
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