| /* |
| * Copyright (c) 2018-2020 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/graph/GraphBuilder.h" |
| |
| #include "arm_compute/graph/Graph.h" |
| #include "arm_compute/graph/Utils.h" |
| #include "arm_compute/graph/algorithms/TopologicalSort.h" |
| #include "arm_compute/graph/nodes/Nodes.h" |
| |
| #include "support/ToolchainSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace graph |
| { |
| namespace |
| { |
| inline void check_nodeidx_pair(const NodeIdxPair &pair, const Graph &g) |
| { |
| ARM_COMPUTE_UNUSED(pair); |
| ARM_COMPUTE_UNUSED(g); |
| ARM_COMPUTE_ERROR_ON((pair.node_id >= g.nodes().size()) || (g.node((pair).node_id) == nullptr) || (pair.index >= g.node(pair.node_id)->num_outputs())); |
| } |
| |
| Status set_node_params(Graph &g, NodeID nid, NodeParams ¶ms) |
| { |
| INode *node = g.node(nid); |
| ARM_COMPUTE_RETURN_ERROR_ON(!node); |
| |
| node->set_common_node_parameters(params); |
| |
| return Status{}; |
| } |
| |
| Status set_accessor_on_node(Graph &g, NodeID nid, bool is_output, size_t idx, ITensorAccessorUPtr accessor) |
| { |
| INode *node = g.node(nid); |
| ARM_COMPUTE_RETURN_ERROR_ON(!node); |
| |
| Tensor *tensor = is_output ? node->output(idx) : node->input(idx); |
| ARM_COMPUTE_RETURN_ERROR_ON(!tensor); |
| |
| tensor->set_accessor(std::move(accessor)); |
| |
| return Status{}; |
| } |
| |
| NodeID add_const_node_with_name(Graph &g, NodeParams params, const std::string &name, const TensorDescriptor &desc, ITensorAccessorUPtr accessor) |
| { |
| params.name = params.name.empty() ? "" : params.name + name; |
| auto nid = GraphBuilder::add_const_node(g, params, desc, std::move(accessor)); |
| set_node_params(g, nid, params); |
| return nid; |
| } |
| |
| template <typename NT, typename... Args> |
| NodeID create_simple_single_input_output_node(Graph &g, NodeParams ¶ms, NodeIdxPair input, Args &&... args) |
| { |
| check_nodeidx_pair(input, g); |
| |
| NodeID nid = g.add_node<NT>(std::forward<Args>(args)...); |
| g.add_connection(input.node_id, input.index, nid, 0); |
| set_node_params(g, nid, params); |
| |
| return nid; |
| } |
| |
| template <typename NT, typename... Args> |
| NodeID create_simple_multiple_input_single_output_node(Graph &g, NodeParams ¶ms, const std::vector<NodeIdxPair> &inputs, Args &&... args) |
| { |
| ARM_COMPUTE_ERROR_ON(inputs.size() == 0); |
| |
| NodeID nid = g.add_node<NT>(std::forward<Args>(args)...); |
| |
| unsigned int i = 0; |
| for(const auto &input : inputs) |
| { |
| check_nodeidx_pair(input, g); |
| g.add_connection(input.node_id, input.index, nid, i++); |
| } |
| set_node_params(g, nid, params); |
| |
| return nid; |
| } |
| } // namespace |
| |
| NodeID GraphBuilder::add_const_node(Graph &g, NodeParams params, const TensorDescriptor &desc, ITensorAccessorUPtr accessor) |
| { |
| auto nid = g.add_node<ConstNode>(desc); |
| set_node_params(g, nid, params); |
| set_accessor_on_node(g, nid, true, 0, std::move(accessor)); |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_input_node(Graph &g, NodeParams params, const TensorDescriptor &desc, ITensorAccessorUPtr accessor) |
| { |
| auto nid = g.add_node<InputNode>(desc); |
| set_node_params(g, nid, params); |
| set_accessor_on_node(g, nid, true, 0, std::move(accessor)); |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_output_node(Graph &g, NodeParams params, NodeIdxPair input, ITensorAccessorUPtr accessor) |
| { |
| check_nodeidx_pair(input, g); |
| |
| NodeID nid = g.add_node<OutputNode>(); |
| g.add_connection(input.node_id, input.index, nid, 0); |
| set_node_params(g, nid, params); |
| set_accessor_on_node(g, nid, false, 0, std::move(accessor)); |
| |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_activation_node(Graph &g, NodeParams params, NodeIdxPair input, ActivationLayerInfo act_info, |
| const QuantizationInfo &out_quant_info) |
| { |
| return create_simple_single_input_output_node<ActivationLayerNode>(g, params, input, act_info, out_quant_info); |
| } |
| |
| NodeID GraphBuilder::add_arg_min_max_node(Graph &g, NodeParams params, NodeIdxPair input, ReductionOperation op, unsigned int axis, |
| DataType out_data_type, const QuantizationInfo &out_quant_info) |
| { |
| return create_simple_single_input_output_node<ArgMinMaxLayerNode>(g, params, input, op, axis, out_data_type, out_quant_info); |
| } |
| |
| NodeID GraphBuilder::add_batch_normalization_node(Graph &g, NodeParams params, NodeIdxPair input, float epsilon, |
| ITensorAccessorUPtr mean_accessor, ITensorAccessorUPtr var_accessor, |
| ITensorAccessorUPtr beta_accessor, ITensorAccessorUPtr gamma_accessor) |
| { |
| check_nodeidx_pair(input, g); |
| |
| bool has_beta = (beta_accessor != nullptr); |
| bool has_gamma = (gamma_accessor != nullptr); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| |
| // Calculate Common Descriptor |
| TensorDescriptor common_desc = input_tensor_desc; |
| common_desc.shape = TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL)); |
| |
| // Create mean and var nodes |
| auto mean_nid = add_const_node_with_name(g, params, "Mean", common_desc, std::move(mean_accessor)); |
| auto var_nid = add_const_node_with_name(g, params, "Variance", common_desc, std::move(var_accessor)); |
| |
| // Create beta node |
| NodeID beta_nid = EmptyNodeID; |
| if(has_beta) |
| { |
| beta_nid = add_const_node_with_name(g, params, "Beta", common_desc, std::move(beta_accessor)); |
| } |
| |
| // Create gamma node |
| NodeID gamma_nid = EmptyNodeID; |
| if(has_gamma) |
| { |
| gamma_nid = add_const_node_with_name(g, params, "Gamma", common_desc, std::move(gamma_accessor)); |
| } |
| |
| // Create batch normalization node and add connections |
| NodeID batch_norm_nid = g.add_node<BatchNormalizationLayerNode>(epsilon); |
| g.add_connection(input.node_id, input.index, batch_norm_nid, 0); |
| g.add_connection(mean_nid, 0, batch_norm_nid, 1); |
| g.add_connection(var_nid, 0, batch_norm_nid, 2); |
| if(has_beta) |
| { |
| g.add_connection(beta_nid, 0, batch_norm_nid, 3); |
| } |
| if(has_gamma) |
| { |
| g.add_connection(gamma_nid, 0, batch_norm_nid, 4); |
| } |
| set_node_params(g, batch_norm_nid, params); |
| |
| return batch_norm_nid; |
| } |
| |
| NodeID GraphBuilder::add_bounding_box_transform_node(Graph &g, NodeParams params, NodeIdxPair input, NodeIdxPair deltas, BoundingBoxTransformInfo info) |
| { |
| check_nodeidx_pair(input, g); |
| check_nodeidx_pair(deltas, g); |
| |
| NodeID nid = g.add_node<BoundingBoxTransformLayerNode>(info); |
| |
| g.add_connection(input.node_id, input.index, nid, 0); |
| g.add_connection(deltas.node_id, deltas.index, nid, 1); |
| |
| set_node_params(g, nid, params); |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_channel_shuffle_node(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_groups) |
| { |
| return create_simple_single_input_output_node<ChannelShuffleLayerNode>(g, params, input, num_groups); |
| } |
| |
| NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, |
| Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info, |
| unsigned int num_groups, ConvolutionMethod method, FastMathHint fast_math_hint, |
| ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, |
| const QuantizationInfo &weights_quant_info, |
| const QuantizationInfo &out_quant_info) |
| { |
| check_nodeidx_pair(input, g); |
| ARM_COMPUTE_ERROR_ON(depth == 0); |
| ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0)); |
| |
| bool has_bias = (bias_accessor != nullptr); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| const DataLayout input_data_layout = input_tensor_desc.layout; |
| |
| // Create weights node |
| TensorDescriptor w_desc = input_tensor_desc; |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL), |
| get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) / num_groups); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::BATCHES), depth); |
| if(!weights_quant_info.empty()) |
| { |
| w_desc.quant_info = weights_quant_info; |
| } |
| |
| NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); |
| |
| // Create bias nodes |
| NodeID b_nid = EmptyNodeID; |
| if(has_bias) |
| { |
| TensorDescriptor b_desc = input_tensor_desc; |
| b_desc.shape = TensorShape(depth); |
| if(is_data_type_quantized_asymmetric(input_tensor_desc.data_type)) |
| { |
| b_desc.data_type = DataType::S32; |
| } |
| b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor)); |
| } |
| |
| // Create convolution node and connect |
| NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, num_groups, method, fast_math_hint, out_quant_info); |
| g.add_connection(input.node_id, input.index, conv_nid, 0); |
| g.add_connection(w_nid, 0, conv_nid, 1); |
| if(has_bias) |
| { |
| g.add_connection(b_nid, 0, conv_nid, 2); |
| } |
| set_node_params(g, conv_nid, params); |
| |
| return conv_nid; |
| } |
| |
| NodeID GraphBuilder::add_deconvolution_node(Graph &g, NodeParams params, NodeIdxPair input, |
| Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo deconv_info, |
| ITensorAccessorUPtr weights_accessor, |
| ITensorAccessorUPtr bias_accessor) |
| { |
| check_nodeidx_pair(input, g); |
| ARM_COMPUTE_ERROR_ON(depth == 0); |
| ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0)); |
| |
| bool has_bias = (bias_accessor != nullptr); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| const DataLayout input_data_layout = input_tensor_desc.layout; |
| |
| // Create weights node |
| TensorDescriptor w_desc = input_tensor_desc; |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL), |
| get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL)); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::BATCHES), depth); |
| |
| NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); |
| |
| // Create bias nodes |
| NodeID b_nid = EmptyNodeID; |
| if(has_bias) |
| { |
| TensorDescriptor b_desc = input_tensor_desc; |
| b_desc.shape = TensorShape(depth); |
| if(is_data_type_quantized_asymmetric(input_tensor_desc.data_type)) |
| { |
| b_desc.data_type = DataType::S32; |
| } |
| b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor)); |
| } |
| |
| // Create convolution node and connect |
| NodeID deconv_nid = g.add_node<DeconvolutionLayerNode>(descriptors::DeconvolutionLayerDescriptor{ deconv_info }); |
| g.add_connection(input.node_id, input.index, deconv_nid, 0); |
| g.add_connection(w_nid, 0, deconv_nid, 1); |
| if(has_bias) |
| { |
| g.add_connection(b_nid, 0, deconv_nid, 2); |
| } |
| set_node_params(g, deconv_nid, params); |
| |
| return deconv_nid; |
| } |
| |
| NodeID GraphBuilder::add_concatenate_node(Graph &g, NodeParams params, const std::vector<NodeIdxPair> &inputs, const descriptors::ConcatLayerDescriptor &concat_descriptor) |
| { |
| return create_simple_multiple_input_single_output_node<ConcatenateLayerNode>(g, params, inputs, inputs.size(), concat_descriptor); |
| } |
| |
| NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, Size2D kernel_spatial_extend, |
| PadStrideInfo conv_info, int depth_multiplier, DepthwiseConvolutionMethod method, |
| ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, const QuantizationInfo &quant_info, const QuantizationInfo &out_quant_info) |
| { |
| check_nodeidx_pair(input, g); |
| ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0)); |
| |
| bool has_bias = (bias_accessor != nullptr); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| const DataLayout input_data_layout = input_tensor_desc.layout; |
| |
| // Create weights node |
| TensorDescriptor w_desc = input_tensor_desc; |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), kernel_spatial_extend.width); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height); |
| w_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL), |
| get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) * depth_multiplier); |
| if(!quant_info.empty()) |
| { |
| w_desc.quant_info = quant_info; |
| } |
| |
| NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); |
| |
| // Create bias nodes |
| NodeID b_nid = EmptyNodeID; |
| if(has_bias) |
| { |
| TensorDescriptor b_desc = input_tensor_desc; |
| b_desc.shape = TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) * depth_multiplier); |
| |
| if(is_data_type_quantized_asymmetric(b_desc.data_type)) |
| { |
| b_desc.data_type = DataType::S32; |
| } |
| |
| b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor)); |
| } |
| |
| // Create convolution node and connect |
| NodeID conv_nid = g.add_node<DepthwiseConvolutionLayerNode>(conv_info, depth_multiplier, method, out_quant_info); |
| g.add_connection(input.node_id, input.index, conv_nid, 0); |
| g.add_connection(w_nid, 0, conv_nid, 1); |
| if(has_bias) |
| { |
| g.add_connection(b_nid, 0, conv_nid, 2); |
| } |
| set_node_params(g, conv_nid, params); |
| |
| return conv_nid; |
| } |
| |
| NodeID GraphBuilder::add_depth_to_space_node(Graph &g, NodeParams params, NodeIdxPair input, int32_t block_shape) |
| { |
| return create_simple_single_input_output_node<DepthToSpaceLayerNode>(g, params, input, block_shape); |
| } |
| |
| NodeID GraphBuilder::add_dequantization_node(Graph &g, NodeParams params, NodeIdxPair input) |
| { |
| return create_simple_single_input_output_node<DequantizationLayerNode>(g, params, input); |
| } |
| |
| NodeID GraphBuilder::add_detection_output_node(Graph &g, NodeParams params, NodeIdxPair input_loc, NodeIdxPair input_conf, NodeIdxPair input_priorbox, const DetectionOutputLayerInfo &detect_info) |
| { |
| check_nodeidx_pair(input_loc, g); |
| check_nodeidx_pair(input_conf, g); |
| check_nodeidx_pair(input_priorbox, g); |
| |
| // Create detection_output node and connect |
| NodeID detect_nid = g.add_node<DetectionOutputLayerNode>(detect_info); |
| g.add_connection(input_loc.node_id, input_loc.index, detect_nid, 0); |
| g.add_connection(input_conf.node_id, input_conf.index, detect_nid, 1); |
| g.add_connection(input_priorbox.node_id, input_priorbox.index, detect_nid, 2); |
| |
| set_node_params(g, detect_nid, params); |
| |
| return detect_nid; |
| } |
| |
| NodeID GraphBuilder::add_detection_post_process_node(Graph &g, NodeParams params, NodeIdxPair input_box_encoding, NodeIdxPair input_class_prediction, const DetectionPostProcessLayerInfo &detect_info, |
| ITensorAccessorUPtr anchors_accessor, const QuantizationInfo &anchor_quant_info) |
| { |
| check_nodeidx_pair(input_box_encoding, g); |
| check_nodeidx_pair(input_class_prediction, g); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_box_encoding_tensor_desc = get_tensor_descriptor(g, g.node(input_box_encoding.node_id)->outputs()[0]); |
| |
| // Calculate anchor descriptor |
| TensorDescriptor anchor_desc = input_box_encoding_tensor_desc; |
| if(!anchor_quant_info.empty()) |
| { |
| anchor_desc.quant_info = anchor_quant_info; |
| } |
| |
| // Create anchors nodes |
| auto anchors_nid = add_const_node_with_name(g, params, "Anchors", anchor_desc, std::move(anchors_accessor)); |
| |
| // Create detection_output node and connect |
| NodeID detect_nid = g.add_node<DetectionPostProcessLayerNode>(detect_info); |
| g.add_connection(input_box_encoding.node_id, input_box_encoding.index, detect_nid, 0); |
| g.add_connection(input_class_prediction.node_id, input_class_prediction.index, detect_nid, 1); |
| g.add_connection(anchors_nid, 0, detect_nid, 2); |
| |
| set_node_params(g, detect_nid, params); |
| |
| return detect_nid; |
| } |
| |
| NodeID GraphBuilder::add_dummy_node(Graph &g, NodeParams params, NodeIdxPair input, TensorShape shape) |
| { |
| return create_simple_single_input_output_node<DummyNode>(g, params, input, shape); |
| } |
| |
| NodeID GraphBuilder::add_elementwise_node(Graph &g, NodeParams params, NodeIdxPair input0, NodeIdxPair input1, EltwiseOperation operation) |
| { |
| check_nodeidx_pair(input0, g); |
| check_nodeidx_pair(input1, g); |
| |
| NodeID nid = g.add_node<EltwiseLayerNode>(descriptors::EltwiseLayerDescriptor{ operation }); |
| |
| g.add_connection(input0.node_id, input0.index, nid, 0); |
| g.add_connection(input1.node_id, input1.index, nid, 1); |
| |
| set_node_params(g, nid, params); |
| |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_flatten_node(Graph &g, NodeParams params, NodeIdxPair input) |
| { |
| return create_simple_single_input_output_node<FlattenLayerNode>(g, params, input); |
| } |
| |
| NodeID GraphBuilder::add_fully_connected_layer(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_outputs, |
| NodeID weights_nid, NodeID bias_nid, |
| const FullyConnectedLayerInfo fc_info, const QuantizationInfo &out_quant_info) |
| { |
| check_nodeidx_pair(input, g); |
| ARM_COMPUTE_ERROR_ON(num_outputs == 0); |
| ARM_COMPUTE_ERROR_ON(weights_nid == EmptyNodeID); |
| |
| const bool has_bias = (bias_nid != EmptyNodeID); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| |
| // Create fully connected node and connect |
| NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs, out_quant_info, fc_info); |
| g.add_connection(input.node_id, input.index, fc_nid, 0); |
| g.add_connection(weights_nid, 0, fc_nid, 1); |
| if(has_bias) |
| { |
| g.add_connection(bias_nid, 0, fc_nid, 2); |
| } |
| |
| set_node_params(g, fc_nid, params); |
| |
| return fc_nid; |
| } |
| |
| NodeID GraphBuilder::add_fully_connected_layer(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_outputs, |
| ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, |
| const FullyConnectedLayerInfo fc_info, |
| const QuantizationInfo &weights_quant_info, const QuantizationInfo &out_quant_info) |
| { |
| check_nodeidx_pair(input, g); |
| ARM_COMPUTE_ERROR_ON(num_outputs == 0); |
| |
| bool has_bias = (bias_accessor != nullptr); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| |
| // Create weights node |
| TensorDescriptor w_desc = FullyConnectedLayerNode::compute_weights_descriptor(input_tensor_desc, num_outputs, fc_info, weights_quant_info); |
| NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); |
| |
| // Create bias nodes |
| NodeID b_nid = EmptyNodeID; |
| if(has_bias) |
| { |
| TensorDescriptor b_desc = input_tensor_desc; |
| b_desc.shape = TensorShape(num_outputs); |
| if(is_data_type_quantized_asymmetric(input_tensor_desc.data_type)) |
| { |
| b_desc.data_type = DataType::S32; |
| } |
| b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor)); |
| } |
| |
| // Create fully connected node and connect |
| NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs, out_quant_info, fc_info); |
| g.add_connection(input.node_id, input.index, fc_nid, 0); |
| g.add_connection(w_nid, 0, fc_nid, 1); |
| if(has_bias) |
| { |
| g.add_connection(b_nid, 0, fc_nid, 2); |
| } |
| |
| set_node_params(g, fc_nid, params); |
| |
| return fc_nid; |
| } |
| |
| NodeID GraphBuilder::add_generate_proposals_node(Graph &g, NodeParams params, NodeIdxPair scores, NodeIdxPair deltas, NodeIdxPair anchors, GenerateProposalsInfo info) |
| { |
| check_nodeidx_pair(scores, g); |
| check_nodeidx_pair(deltas, g); |
| check_nodeidx_pair(anchors, g); |
| |
| NodeID nid = g.add_node<GenerateProposalsLayerNode>(info); |
| |
| g.add_connection(scores.node_id, scores.index, nid, 0); |
| g.add_connection(deltas.node_id, deltas.index, nid, 1); |
| g.add_connection(anchors.node_id, anchors.index, nid, 2); |
| |
| set_node_params(g, nid, params); |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_l2_normalize_node(Graph &g, NodeParams params, NodeIdxPair input, int axis, float epsilon) |
| { |
| return create_simple_single_input_output_node<L2NormalizeLayerNode>(g, params, input, axis, epsilon); |
| } |
| |
| NodeID GraphBuilder::add_normalization_node(Graph &g, NodeParams params, NodeIdxPair input, NormalizationLayerInfo norm_info) |
| { |
| return create_simple_single_input_output_node<NormalizationLayerNode>(g, params, input, norm_info); |
| } |
| |
| NodeID GraphBuilder::add_normalize_planar_yuv_node(Graph &g, NodeParams params, NodeIdxPair input, |
| ITensorAccessorUPtr mean_accessor, ITensorAccessorUPtr std_accessor) |
| { |
| check_nodeidx_pair(input, g); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| |
| // Calculate Common Descriptor |
| TensorDescriptor common_desc = input_tensor_desc; |
| common_desc.shape = TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL)); |
| |
| // Create mean and std nodes |
| auto mean_nid = add_const_node_with_name(g, params, "Mean", common_desc, std::move(mean_accessor)); |
| auto std_nid = add_const_node_with_name(g, params, "Std", common_desc, std::move(std_accessor)); |
| |
| // Create normalize planar YUV node and add connections |
| NodeID norm_planar_yuv_nid = g.add_node<NormalizePlanarYUVLayerNode>(); |
| g.add_connection(input.node_id, input.index, norm_planar_yuv_nid, 0); |
| g.add_connection(mean_nid, 0, norm_planar_yuv_nid, 1); |
| g.add_connection(std_nid, 0, norm_planar_yuv_nid, 2); |
| set_node_params(g, norm_planar_yuv_nid, params); |
| |
| return norm_planar_yuv_nid; |
| } |
| |
| NodeID GraphBuilder::add_pad_node(Graph &g, NodeParams params, NodeIdxPair input, const PaddingList &paddings, PixelValue pad_value) |
| { |
| return create_simple_single_input_output_node<PadLayerNode>(g, params, input, paddings, pad_value); |
| } |
| |
| NodeID GraphBuilder::add_permute_node(Graph &g, NodeParams params, NodeIdxPair input, PermutationVector perm, DataLayout layout) |
| { |
| return create_simple_single_input_output_node<PermuteLayerNode>(g, params, input, perm, layout); |
| } |
| |
| NodeID GraphBuilder::add_prelu_node(Graph &g, NodeParams params, NodeIdxPair input, NodeIdxPair alpha) |
| { |
| check_nodeidx_pair(input, g); |
| check_nodeidx_pair(alpha, g); |
| |
| NodeID prelu_nid = g.add_node<PReluLayerNode>(); |
| g.add_connection(input.node_id, input.index, prelu_nid, 0); |
| g.add_connection(alpha.node_id, alpha.index, prelu_nid, 1); |
| |
| set_node_params(g, prelu_nid, params); |
| |
| return prelu_nid; |
| } |
| |
| NodeID GraphBuilder::add_pooling_node(Graph &g, NodeParams params, NodeIdxPair input, PoolingLayerInfo pool_info) |
| { |
| return create_simple_single_input_output_node<PoolingLayerNode>(g, params, input, pool_info); |
| } |
| |
| NodeID GraphBuilder::add_print_node(Graph &g, NodeParams params, NodeIdxPair input, std::ostream &stream, const IOFormatInfo &format_info, const std::function<ITensor *(ITensor *)> transform) |
| { |
| return create_simple_single_input_output_node<PrintLayerNode>(g, params, input, stream, format_info, transform); |
| } |
| |
| NodeID GraphBuilder::add_priorbox_node(Graph &g, NodeParams params, NodeIdxPair input0, NodeIdxPair input1, const PriorBoxLayerInfo &prior_info) |
| { |
| check_nodeidx_pair(input0, g); |
| check_nodeidx_pair(input1, g); |
| |
| // Create priorbox node and connect |
| NodeID prior_nid = g.add_node<PriorBoxLayerNode>(prior_info); |
| g.add_connection(input0.node_id, input0.index, prior_nid, 0); |
| g.add_connection(input1.node_id, input1.index, prior_nid, 1); |
| |
| set_node_params(g, prior_nid, params); |
| |
| return prior_nid; |
| } |
| |
| NodeID GraphBuilder::add_quantization_node(Graph &g, NodeParams params, NodeIdxPair input, const QuantizationInfo &out_quant_info) |
| { |
| return create_simple_single_input_output_node<QuantizationLayerNode>(g, params, input, out_quant_info); |
| } |
| |
| NodeID GraphBuilder::add_reduction_operation_node(Graph &g, NodeParams params, NodeIdxPair input, ReductionOperation op, int axis, bool keep_dims) |
| { |
| return create_simple_single_input_output_node<ReductionLayerNode>(g, params, input, op, axis, keep_dims); |
| } |
| |
| NodeID GraphBuilder::add_reorg_node(Graph &g, NodeParams params, NodeIdxPair input, int stride) |
| { |
| return create_simple_single_input_output_node<ReorgLayerNode>(g, params, input, stride); |
| } |
| |
| NodeID GraphBuilder::add_reshape_node(Graph &g, NodeParams params, NodeIdxPair input, TensorShape shape) |
| { |
| return create_simple_single_input_output_node<ReshapeLayerNode>(g, params, input, shape); |
| } |
| |
| NodeID GraphBuilder::add_resize_node(Graph &g, NodeParams params, NodeIdxPair input, InterpolationPolicy policy, |
| float width_scale, float height_scale) |
| { |
| return create_simple_single_input_output_node<ResizeLayerNode>(g, params, input, policy, width_scale, height_scale); |
| } |
| |
| NodeID GraphBuilder::add_roi_align_node(Graph &g, NodeParams params, NodeIdxPair input, NodeIdxPair rois, ROIPoolingLayerInfo pool_info) |
| { |
| check_nodeidx_pair(input, g); |
| check_nodeidx_pair(rois, g); |
| |
| NodeID nid = g.add_node<ROIAlignLayerNode>(pool_info); |
| |
| g.add_connection(input.node_id, input.index, nid, 0); |
| g.add_connection(rois.node_id, rois.index, nid, 1); |
| |
| set_node_params(g, nid, params); |
| return nid; |
| } |
| |
| NodeID GraphBuilder::add_scale_layer(Graph &g, const NodeParams ¶ms, NodeIdxPair input, ITensorAccessorUPtr mul_accessor, ITensorAccessorUPtr add_accessor) |
| { |
| check_nodeidx_pair(input, g); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| const DataLayout input_data_layout = input_tensor_desc.layout; |
| |
| // Create mul node |
| TensorDescriptor mul_desc = input_tensor_desc; |
| const size_t C = input_tensor_desc.shape[get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL)]; |
| mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::WIDTH), 1); |
| mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::HEIGHT), 1); |
| mul_desc.shape.set(get_dimension_idx(input_data_layout, DataLayoutDimension::CHANNEL), C); |
| NodeID mul_const_nid = add_const_node_with_name(g, params, "Mul", mul_desc, std::move(mul_accessor)); |
| NodeIdxPair mul_const_nidxp = { mul_const_nid, 0 }; |
| |
| // Create add node |
| TensorDescriptor add_desc = mul_desc; |
| NodeID add_const_nid = add_const_node_with_name(g, params, "Add", add_desc, std::move(add_accessor)); |
| NodeIdxPair add_const_nidxp = { add_const_nid, 0 }; |
| |
| // Create node and connect |
| NodeID mul_node = GraphBuilder::add_elementwise_node(g, params, input, mul_const_nidxp, EltwiseOperation::Mul); |
| NodeIdxPair mulnode_nidxp = { mul_node, 0 }; |
| NodeID add_node = GraphBuilder::add_elementwise_node(g, params, mulnode_nidxp, add_const_nidxp, EltwiseOperation::Add); |
| |
| return add_node; |
| } |
| |
| NodeID GraphBuilder::add_softmax_node(Graph &g, NodeParams params, NodeIdxPair input, float beta) |
| { |
| return create_simple_single_input_output_node<SoftmaxLayerNode>(g, params, input, beta); |
| } |
| |
| NodeID GraphBuilder::add_slice_node(Graph &g, NodeParams params, NodeIdxPair input, Coordinates &starts, Coordinates &ends) |
| { |
| return create_simple_single_input_output_node<SliceLayerNode>(g, params, input, starts, ends); |
| } |
| |
| NodeID GraphBuilder::add_split_node(Graph &g, NodeParams params, NodeIdxPair input, unsigned int num_splits, unsigned int axis) |
| { |
| return create_simple_single_input_output_node<SplitLayerNode>(g, params, input, num_splits, axis); |
| } |
| |
| NodeID GraphBuilder::add_strided_slice_node(Graph &g, NodeParams params, NodeIdxPair input, Coordinates &starts, Coordinates &ends, BiStrides &strides, StridedSliceLayerInfo info) |
| { |
| return create_simple_single_input_output_node<StridedSliceLayerNode>(g, params, input, starts, ends, strides, info); |
| } |
| |
| NodeID GraphBuilder::add_stack_node(Graph &g, NodeParams params, const std::vector<NodeIdxPair> &inputs, int axis) |
| { |
| return create_simple_multiple_input_single_output_node<StackLayerNode>(g, params, inputs, inputs.size(), axis); |
| } |
| |
| NodeID GraphBuilder::add_yolo_node(Graph &g, NodeParams params, NodeIdxPair input, ActivationLayerInfo act_info) |
| { |
| check_nodeidx_pair(input, g); |
| |
| // Get input tensor descriptor |
| const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); |
| const bool is_nhwc = input_tensor_desc.layout == DataLayout::NHWC; |
| |
| // Box format: [Objectness:1][Box:4][Classes:N] |
| |
| // Activate objectness and front part of the box |
| const Coordinates box_start(0, 0, 0); |
| const Coordinates box_end = is_nhwc ? Coordinates(3, -1, -1) : Coordinates(-1, -1, 3); |
| NodeID box = g.add_node<SliceLayerNode>(box_start, box_end); |
| NodeID act_box = g.add_node<ActivationLayerNode>(act_info); |
| set_node_params(g, box, params); |
| set_node_params(g, act_box, params); |
| g.add_connection(input.node_id, input.index, box, 0); |
| g.add_connection(box, 0, act_box, 0); |
| |
| // Immutable part |
| const Coordinates imm_start = is_nhwc ? Coordinates(3, 0, 0) : Coordinates(0, 0, 3); |
| const Coordinates imm_end = is_nhwc ? Coordinates(5, -1, -1) : Coordinates(-1, -1, 5); |
| NodeID imm = g.add_node<SliceLayerNode>(imm_start, imm_end); |
| set_node_params(g, imm, params); |
| g.add_connection(input.node_id, input.index, imm, 0); |
| |
| // Activation classes and end part of box |
| const Coordinates cls_start = is_nhwc ? Coordinates(5, 0, 0) : Coordinates(0, 0, 5); |
| const Coordinates cls_end = is_nhwc ? Coordinates(-1, -1, -1) : Coordinates(-1, -1, -1); |
| NodeID cls = g.add_node<SliceLayerNode>(cls_start, cls_end); |
| NodeID cls_act = g.add_node<ActivationLayerNode>(act_info); |
| set_node_params(g, cls, params); |
| set_node_params(g, cls_act, params); |
| g.add_connection(input.node_id, input.index, cls, 0); |
| g.add_connection(cls, 0, cls_act, 0); |
| |
| NodeID concat = g.add_node<ConcatenateLayerNode>(3, descriptors::ConcatLayerDescriptor(DataLayoutDimension::CHANNEL)); |
| set_node_params(g, concat, params); |
| g.add_connection(act_box, 0, concat, 0); |
| g.add_connection(imm, 0, concat, 1); |
| g.add_connection(cls_act, 0, concat, 2); |
| |
| return concat; |
| } |
| } // namespace graph |
| } // namespace arm_compute |