| /* |
| * Copyright (c) 2017-2018 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/ToolchainSupport.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) |
| : _memory_group(std::move(memory_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(); |
| |
| _memory_group.acquire(); |
| |
| // 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); |
| } |
| |
| _memory_group.release(); |
| } |
| |
| 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; |
| } |
| } |