diff options
Diffstat (limited to 'src/graph/nodes/ConvolutionLayer.cpp')
-rw-r--r-- | src/graph/nodes/ConvolutionLayer.cpp | 58 |
1 files changed, 30 insertions, 28 deletions
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 |