diff options
author | Georgios Pinitas <georgios.pinitas@arm.com> | 2017-10-04 16:53:58 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:35:24 +0000 |
commit | ff421f2100e0e9e532f5fe78585300546af61690 (patch) | |
tree | 9ba5a1bfe64b5b10f70c64a965f9c5ca14de9ce3 /src/graph/nodes | |
parent | 925ca0f7402115da3bffb21c04fca0bc822c9b38 (diff) | |
download | ComputeLibrary-ff421f2100e0e9e532f5fe78585300546af61690.tar.gz |
COMPMID-601: Add GraphContext
GraphContext hold all the information about the hints that need to be
passed in the nodes. As these might expand, it serves as a centralized
class for such information.
Change-Id: I0b5527630fb97cc5fa500db0bac8307ff2ea36e6
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/90300
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src/graph/nodes')
-rw-r--r-- | src/graph/nodes/ActivationLayer.cpp | 28 | ||||
-rw-r--r-- | src/graph/nodes/ConvolutionLayer.cpp | 58 | ||||
-rw-r--r-- | src/graph/nodes/FullyConnectedLayer.cpp | 32 | ||||
-rw-r--r-- | src/graph/nodes/NormalizationLayer.cpp | 28 | ||||
-rw-r--r-- | src/graph/nodes/PoolingLayer.cpp | 28 | ||||
-rw-r--r-- | src/graph/nodes/SoftmaxLayer.cpp | 28 |
6 files changed, 102 insertions, 100 deletions
diff --git a/src/graph/nodes/ActivationLayer.cpp b/src/graph/nodes/ActivationLayer.cpp index b71e22c601..da2dac04e2 100644 --- a/src/graph/nodes/ActivationLayer.cpp +++ b/src/graph/nodes/ActivationLayer.cpp @@ -34,7 +34,7 @@ using namespace arm_compute::graph; namespace { -template <typename ActivationType, typename TensorType, Hint hint> +template <typename ActivationType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info) { auto activation = arm_compute::support::cpp14::make_unique<ActivationType>(); @@ -46,19 +46,19 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe return std::move(activation); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info) { - return instantiate_function<arm_compute::CLActivationLayer, arm_compute::CLTensor, Hint::OPENCL>(input, output, activation_info); + return instantiate_function<arm_compute::CLActivationLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, output, activation_info); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, const ActivationLayerInfo &activation_info) { - return instantiate_function<arm_compute::NEActivationLayer, arm_compute::Tensor, Hint::NEON>(input, output, activation_info); + return instantiate_function<arm_compute::NEActivationLayer, arm_compute::Tensor, TargetHint::NEON>(input, output, activation_info); } } // namespace @@ -67,27 +67,27 @@ ActivationLayer::ActivationLayer(const ActivationLayerInfo activation_info) { } -std::unique_ptr<arm_compute::IFunction> ActivationLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> ActivationLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(input, output, _activation_info); + func = instantiate<TargetHint::OPENCL>(input, output, _activation_info); } else { - func = instantiate<Hint::NEON>(input, output, _activation_info); + func = instantiate<TargetHint::NEON>(input, output, _activation_info); } return func; } void ActivationLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLActivationLayer"; } diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp index ce9f096719..a992095786 100644 --- a/src/graph/nodes/ConvolutionLayer.cpp +++ b/src/graph/nodes/ConvolutionLayer.cpp @@ -65,7 +65,7 @@ TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_sh } // Instantiate GEMM based convolution layer -template <typename ConvolutionType, typename TensorType, Hint hint> +template <typename ConvolutionType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); @@ -79,7 +79,7 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe } // Instantiate direct convolution layer -template <typename ConvolutionType, typename TensorType, Hint hint> +template <typename ConvolutionType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info) { auto conv = arm_compute::support::cpp14::make_unique<ConvolutionType>(); @@ -92,35 +92,37 @@ std::unique_ptr<arm_compute::IFunction> instantiate_direct_function(ITensor *inp return std::move(conv); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, ConvolutionMethodHint conv_method); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - ConvolutionMethodHint conv_method) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + ConvolutionMethodHint conv_method) { if(conv_method == ConvolutionMethodHint::GEMM) { - return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info, weights_info); + return instantiate_function<arm_compute::CLConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info, weights_info); } else { - return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, Hint::OPENCL>(input, weights, biases, output, conv_info); + return instantiate_direct_function<arm_compute::CLDirectConvolutionLayer, arm_compute::ICLTensor, TargetHint::OPENCL>(input, weights, biases, output, conv_info); } } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - ConvolutionMethodHint conv_method) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *weights, ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info, + ConvolutionMethodHint conv_method) { if(conv_method == ConvolutionMethodHint::GEMM) { - return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info, weights_info); + return instantiate_function<arm_compute::NEConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info, weights_info); } else { - return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, Hint::NEON>(input, weights, biases, output, conv_info); + return instantiate_direct_function<arm_compute::NEDirectConvolutionLayer, arm_compute::ITensor, TargetHint::NEON>(input, weights, biases, output, conv_info); } } } // namespace @@ -166,7 +168,7 @@ private: std::vector<std::unique_ptr<IFunction>> _convolutions; }; -std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { // Set weights and biases info if(_weights.tensor() == nullptr) @@ -181,17 +183,18 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint } std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; + const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint(); // Check if the weights and biases are loaded bool weights_are_loaded = _weights.tensor() != nullptr; bool biases_are_loaded = _weights.tensor() != nullptr; // Set bias and weights target - _weights.set_target(_hint); - _biases.set_target(_hint); + _weights.set_target(_target_hint); + _biases.set_target(_target_hint); // Calculate output shape TensorShape output_shape = calculate_convolution_layer_output_shape(_input->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info); @@ -200,14 +203,13 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint arm_compute::auto_init_if_empty(*_output->info(), output_shape, 1, _input->info()->data_type(), _input->info()->fixed_point_position()); // Create appropriate convolution function - // TODO(geopin01): Fix convolution layer hints once the GraphContext has been added if(_num_groups == 1) { - func = instantiate_convolution(ConvolutionMethodHint::GEMM); + func = instantiate_convolution(conv_method_hint); } else { - func = instantiate_grouped_convolution(ConvolutionMethodHint::GEMM); + func = instantiate_grouped_convolution(conv_method_hint); } // Fill weights @@ -226,7 +228,7 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_node(Hint void ConvolutionLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLConvolutionLayer"; } @@ -248,13 +250,13 @@ void ConvolutionLayer::print_info() std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_convolution(ConvolutionMethodHint conv_method_hint) { std::unique_ptr<arm_compute::IFunction> func; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); + func = instantiate<TargetHint::OPENCL>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); } else { - func = instantiate<Hint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); + func = instantiate<TargetHint::NEON>(_input, _weights.tensor(), _biases.tensor(), _output, _conv_info, _weights_info, conv_method_hint); } return func; } @@ -306,20 +308,20 @@ std::unique_ptr<arm_compute::IFunction> ConvolutionLayer::instantiate_grouped_co Coordinates biases_coord(biases_split * i); // Create sub-tensors for input, output, weights and bias - auto hint_to_use = (_hint == Hint::OPENCL) ? Hint::OPENCL : Hint::NEON; + auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON; _is[i] = SubTensor(_input, input_shape, input_coord, hint_to_use); _os[i] = SubTensor(_output, output_shape, output_coord, hint_to_use); _ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use); _bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use); // Instantiate convolution function - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); + func = instantiate<TargetHint::OPENCL>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); } else { - func = instantiate<Hint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); + func = instantiate<TargetHint::NEON>(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); } // Add convolution function to the list of convolutions for the grouped convolution diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp index fcc86be8fa..c317660b20 100644 --- a/src/graph/nodes/FullyConnectedLayer.cpp +++ b/src/graph/nodes/FullyConnectedLayer.cpp @@ -43,7 +43,7 @@ TensorShape calculate_fullyconnected_layer_output_shape(const TensorShape &input } return TensorShape(output_neurons, batches); } -template <typename FullyConnectedType, typename TensorType, Hint hint> +template <typename FullyConnectedType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) { bool weights_are_loaded = weights.tensor() != nullptr; @@ -52,8 +52,8 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Ten auto conv = arm_compute::support::cpp14::make_unique<FullyConnectedType>(); conv->configure( dynamic_cast<TensorType *>(input), - dynamic_cast<TensorType *>(weights.set_target(hint)), - dynamic_cast<TensorType *>(biases.set_target(hint)), + dynamic_cast<TensorType *>(weights.set_target(target_hint)), + dynamic_cast<TensorType *>(biases.set_target(target_hint)), dynamic_cast<TensorType *>(output)); if(!weights_are_loaded) { @@ -67,23 +67,23 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, Ten return std::move(conv); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) { - return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, Hint::OPENCL>(input, weights, biases, output); + return instantiate_function<arm_compute::CLFullyConnectedLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, weights, biases, output); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, Tensor &weights, Tensor &biases, ITensor *output) { - return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, Hint::NEON>(input, weights, biases, output); + return instantiate_function<arm_compute::NEFullyConnectedLayer, arm_compute::Tensor, TargetHint::NEON>(input, weights, biases, output); } } // namespace -std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { if(_weights.tensor() == nullptr) { @@ -111,17 +111,17 @@ std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hi input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position()); std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(input, _weights, _biases, output); + func = instantiate<TargetHint::OPENCL>(input, _weights, _biases, output); } else { - func = instantiate<Hint::NEON>(input, _weights, _biases, output); + func = instantiate<TargetHint::NEON>(input, _weights, _biases, output); } return func; @@ -129,7 +129,7 @@ std::unique_ptr<arm_compute::IFunction> FullyConnectedLayer::instantiate_node(Hi void FullyConnectedLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLFullyConnectedLayer"; } diff --git a/src/graph/nodes/NormalizationLayer.cpp b/src/graph/nodes/NormalizationLayer.cpp index 55ef9bf243..99d07dc8da 100644 --- a/src/graph/nodes/NormalizationLayer.cpp +++ b/src/graph/nodes/NormalizationLayer.cpp @@ -34,7 +34,7 @@ using namespace arm_compute::graph; namespace { -template <typename NormalizationType, typename TensorType, Hint hint> +template <typename NormalizationType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) { auto norm = arm_compute::support::cpp14::make_unique<NormalizationType>(); @@ -46,19 +46,19 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe return std::move(norm); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) { - return instantiate_function<arm_compute::CLNormalizationLayer, arm_compute::CLTensor, Hint::OPENCL>(input, output, norm_info); + return instantiate_function<arm_compute::CLNormalizationLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, output, norm_info); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, const NormalizationLayerInfo &norm_info) { - return instantiate_function<arm_compute::NENormalizationLayer, arm_compute::Tensor, Hint::NEON>(input, output, norm_info); + return instantiate_function<arm_compute::NENormalizationLayer, arm_compute::Tensor, TargetHint::NEON>(input, output, norm_info); } } // namespace @@ -67,20 +67,20 @@ NormalizationLayer::NormalizationLayer(const NormalizationLayerInfo norm_info) { } -std::unique_ptr<arm_compute::IFunction> NormalizationLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> NormalizationLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(input, output, _norm_info); + func = instantiate<TargetHint::OPENCL>(input, output, _norm_info); } else { - func = instantiate<Hint::NEON>(input, output, _norm_info); + func = instantiate<TargetHint::NEON>(input, output, _norm_info); } return func; @@ -88,7 +88,7 @@ std::unique_ptr<arm_compute::IFunction> NormalizationLayer::instantiate_node(Hin void NormalizationLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLNormalizationLayer"; } diff --git a/src/graph/nodes/PoolingLayer.cpp b/src/graph/nodes/PoolingLayer.cpp index f29332f65b..2a5e4cb3d8 100644 --- a/src/graph/nodes/PoolingLayer.cpp +++ b/src/graph/nodes/PoolingLayer.cpp @@ -34,7 +34,7 @@ using namespace arm_compute::graph; namespace { -template <typename PoolingType, typename TensorType, Hint hint> +template <typename PoolingType, typename TensorType, TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) { auto pool = arm_compute::support::cpp14::make_unique<PoolingType>(); @@ -46,19 +46,19 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe return std::move(pool); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) { - return instantiate_function<arm_compute::CLPoolingLayer, arm_compute::CLTensor, Hint::OPENCL>(input, output, pool_info); + return instantiate_function<arm_compute::CLPoolingLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, output, pool_info); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output, const PoolingLayerInfo &pool_info) { - return instantiate_function<arm_compute::NEPoolingLayer, arm_compute::Tensor, Hint::NEON>(input, output, pool_info); + return instantiate_function<arm_compute::NEPoolingLayer, arm_compute::Tensor, TargetHint::NEON>(input, output, pool_info); } } // namespace @@ -67,20 +67,20 @@ PoolingLayer::PoolingLayer(const PoolingLayerInfo pool_info) { } -std::unique_ptr<arm_compute::IFunction> PoolingLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> PoolingLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(input, output, _pool_info); + func = instantiate<TargetHint::OPENCL>(input, output, _pool_info); } else { - func = instantiate<Hint::NEON>(input, output, _pool_info); + func = instantiate<TargetHint::NEON>(input, output, _pool_info); } return func; @@ -88,7 +88,7 @@ std::unique_ptr<arm_compute::IFunction> PoolingLayer::instantiate_node(Hint hint void PoolingLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLPoolingLayer"; } diff --git a/src/graph/nodes/SoftmaxLayer.cpp b/src/graph/nodes/SoftmaxLayer.cpp index fee88970fc..9e798ef7cc 100644 --- a/src/graph/nodes/SoftmaxLayer.cpp +++ b/src/graph/nodes/SoftmaxLayer.cpp @@ -34,7 +34,7 @@ using namespace arm_compute::graph; namespace { -template <typename SoftmaxType, typename TensorType, Hint hint> +template <typename SoftmaxType, typename TensorType, TargetHint hint> std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITensor *output) { auto softmax = arm_compute::support::cpp14::make_unique<SoftmaxType>(); @@ -45,36 +45,36 @@ std::unique_ptr<arm_compute::IFunction> instantiate_function(ITensor *input, ITe return std::move(softmax); } -template <Hint hint> +template <TargetHint target_hint> std::unique_ptr<arm_compute::IFunction> instantiate(ITensor *input, ITensor *output); template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::OPENCL>(ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::OPENCL>(ITensor *input, ITensor *output) { - return instantiate_function<arm_compute::CLSoftmaxLayer, arm_compute::CLTensor, Hint::OPENCL>(input, output); + return instantiate_function<arm_compute::CLSoftmaxLayer, arm_compute::CLTensor, TargetHint::OPENCL>(input, output); } template <> -std::unique_ptr<arm_compute::IFunction> instantiate<Hint::NEON>(ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> instantiate<TargetHint::NEON>(ITensor *input, ITensor *output) { - return instantiate_function<arm_compute::NESoftmaxLayer, arm_compute::Tensor, Hint::NEON>(input, output); + return instantiate_function<arm_compute::NESoftmaxLayer, arm_compute::Tensor, TargetHint::NEON>(input, output); } } // namespace -std::unique_ptr<arm_compute::IFunction> SoftmaxLayer::instantiate_node(Hint hint, ITensor *input, ITensor *output) +std::unique_ptr<arm_compute::IFunction> SoftmaxLayer::instantiate_node(GraphContext &ctx, ITensor *input, ITensor *output) { std::unique_ptr<arm_compute::IFunction> func; - _hint = hint; - _input = input; - _output = output; + _target_hint = ctx.hints().target_hint(); + _input = input; + _output = output; - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { - func = instantiate<Hint::OPENCL>(input, output); + func = instantiate<TargetHint::OPENCL>(input, output); } else { - func = instantiate<Hint::NEON>(input, output); + func = instantiate<TargetHint::NEON>(input, output); } return func; @@ -82,7 +82,7 @@ std::unique_ptr<arm_compute::IFunction> SoftmaxLayer::instantiate_node(Hint hint void SoftmaxLayer::print_info() { - if(_hint == Hint::OPENCL) + if(_target_hint == TargetHint::OPENCL) { std::cout << "Instantiating CLSoftmaxLayer"; } |