diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2017-10-02 18:51:47 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | e2c82fee3b6d38f6e79412c78176792b817defd0 (patch) | |
tree | aa6821e33cfe8001c33086191c81c18d66ac7837 /src/graph/nodes/FullyConnectedLayer.cpp | |
parent | 48a60f9f7b0b7b5cf38253b7a2ac576aac43ef78 (diff) | |
download | ComputeLibrary-e2c82fee3b6d38f6e79412c78176792b817defd0.tar.gz |
COMPMID-550: Adds support for branches.
Change-Id: I778007c9221ce3156400284c4039b90245eb2b7f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90043
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src/graph/nodes/FullyConnectedLayer.cpp')
-rw-r--r-- | src/graph/nodes/FullyConnectedLayer.cpp | 46 |
1 files changed, 25 insertions, 21 deletions
diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp index 6b21810a36..fa5ead8bdd 100644 --- a/src/graph/nodes/FullyConnectedLayer.cpp +++ b/src/graph/nodes/FullyConnectedLayer.cpp @@ -45,7 +45,7 @@ TensorShape calculate_fullyconnected_layer_output_shape(const TensorShape &input return TensorShape(output_neurons, batches); } template <typename FullyConnectedType, typename TensorType, TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate_function(arm_compute::ITensor *input, Tensor &weights, Tensor &biases, arm_compute::ITensor *output) { bool weights_are_loaded = weights.tensor() != nullptr; bool biases_are_loaded = biases.tensor() != nullptr; @@ -69,27 +69,33 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Ten } template <TargetHint target_hint> -std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output); +std::unique_ptr<arm_compute::IFunction> instantiate(arm_compute::ITensor *input, Tensor &weights, Tensor &biases, arm_compute::ITensor *output); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(arm_compute::ITensor *input, Tensor &weights, Tensor &biases, arm_compute::ITensor *output) { - return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, weights, biases, output); + return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(arm_compute::ITensor *input, Tensor &weights, Tensor &biases, arm_compute::ITensor *output) { - return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, TargetHint::NEON>(input, weights, biases, output); + return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output); } } // namespace -std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) { + ARM_COMPUTE_ERROR_ON(input == nullptr || input->tensor() == nullptr); + ARM_COMPUTE_ERROR_ON(output == nullptr || output->tensor() == nullptr); + + arm_compute::ITensor *in = input->tensor(); + arm_compute::ITensor *out = output->tensor(); + if(_weights.tensor() == nullptr) { unsigned int num_weights = 1; - unsigned int num_dimensions = input->info()->num_dimensions(); + unsigned int num_dimensions = in->info()->num_dimensions(); // Ignore the batch dimension if there is one: if(num_dimensions == 2 || num_dimensions == 4) { @@ -97,39 +103,37 @@ std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Gr } for(unsigned int i = 0; i < num_dimensions; i++) { - num_weights *= input->info()->dimension(i); + num_weights *= in->info()->dimension(i); } - _weights.set_info(TensorInfo(TensorShape(num_weights, _num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _weights.set_info(TensorInfo(TensorShape(num_weights, _num_neurons), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } if(_biases.tensor() == nullptr) { - _biases.set_info(TensorInfo(TensorShape(_num_neurons), input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); + _biases.set_info(TensorInfo(TensorShape(_num_neurons), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position())); } // Auto configure output - arm_compute::auto_init_if_empty(*output->info(), - calculate_fullyconnected_layer_output_shape(input->info()->tensor_shape(), _num_neurons), - input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()); + arm_compute::auto_init_if_empty(*out->info(), + calculate_fullyconnected_layer_output_shape(in->info()->tensor_shape(), _num_neurons), + in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position()); std::unique_ptr<arm_compute::IFunction> func; _target_hint = ctx.hints().target_hint(); if(_target_hint == TargetHint::OPENCL) { - func = instantiate<TargetHint::OPENCL>(input, _weights, _biases, output); - ARM_COMPUTE_LOG("Instantiating CLFullyConnectedLayer"); + func = instantiate<TargetHint::OPENCL>(in, _weights, _biases, out); } else { - func = instantiate<TargetHint::NEON>(input, _weights, _biases, output); - ARM_COMPUTE_LOG("Instantiating NEFullyConnectedLayer"); + func = instantiate<TargetHint::NEON>(in, _weights, _biases, out); } - ARM_COMPUTE_LOG(" Type: " << input->info()->data_type() - << " Input Shape: " << input->info()->tensor_shape() + ARM_COMPUTE_LOG(" Type: " << in->info()->data_type() + << " Input Shape: " << in->info()->tensor_shape() << " Weights shape: " << _weights.info().tensor_shape() << " Biases Shape: " << _biases.info().tensor_shape() - << " Output Shape: " << output->info()->tensor_shape() + << " Output Shape: " << out->info()->tensor_shape() << std::endl); return func; |