| /* |
| * Copyright (c) 2019-2020 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/mutators/SyntheticDataTypeMutator.h" |
| |
| #include "arm_compute/graph/GraphBuilder.h" |
| #include "arm_compute/graph/ITensorAccessor.h" |
| #include "arm_compute/graph/Logger.h" |
| #include "arm_compute/graph/Utils.h" |
| #include "arm_compute/graph/nodes/Nodes.h" |
| |
| #include "support/Cast.h" |
| |
| #include <set> |
| |
| namespace arm_compute |
| { |
| namespace graph |
| { |
| namespace |
| { |
| /** Empty accessor class */ |
| class EmptyAccessor final : public graph::ITensorAccessor |
| { |
| public: |
| /** Default Constructor */ |
| EmptyAccessor() = default; |
| |
| // Inherited methods overriden: |
| bool access_tensor(ITensor &tensor) override |
| { |
| ARM_COMPUTE_UNUSED(tensor); |
| return true; |
| } |
| }; |
| |
| /** Check if the mutation pass can be applied |
| * |
| * @param[in] g Graph the mutation pass need to be applied on |
| * |
| * @return True if the pass can be applied else false |
| */ |
| bool is_mutation_supported(Graph &g) |
| { |
| const std::set<NodeType> unsupported_node_types = { NodeType::DetectionOutputLayer, |
| NodeType::NormalizationLayer, |
| NodeType::PriorBoxLayer |
| }; |
| |
| for(const auto &utype : unsupported_node_types) |
| { |
| if(!g.nodes(utype).empty()) |
| { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| /** Remove nodes that get optimized out during conversion |
| * |
| * @param[in, out] g Graph to remove the nodes from. |
| */ |
| void remove_optimized_nodes(Graph &g) |
| { |
| const std::set<NodeType> optimized_node_types = { NodeType::BatchNormalizationLayer }; |
| |
| for(const auto &opt_type : optimized_node_types) |
| { |
| const std::vector<NodeID> opt_nodes_ids = g.nodes(opt_type); |
| for(const auto &node_id : opt_nodes_ids) |
| { |
| INode *node = g.node(node_id); |
| |
| // Get input edge |
| Edge *input_edge = node->input_edge(0); |
| ARM_COMPUTE_ERROR_ON(input_edge == nullptr); |
| |
| // Get producer node |
| INode *producer = input_edge->producer(); |
| const EdgeID producer_edge_id = input_edge->producer_idx(); |
| ARM_COMPUTE_ERROR_ON(producer == nullptr); |
| |
| // Get driving nodes |
| std::vector<NodeIdxPair> driving_nodes = get_driving_nodes(*node); |
| |
| // Remove node |
| g.remove_node(node->id()); |
| |
| // Update connections |
| for(auto &driving_node : driving_nodes) |
| { |
| g.add_connection(producer->id(), producer_edge_id, driving_node.node_id, driving_node.index); |
| } |
| } |
| } |
| } |
| |
| /** Convert tensor meta-data |
| * |
| * @param[in,out] g Graph to convert tensors of. |
| */ |
| void convert_tensors(Graph &g) |
| { |
| auto &tensors = g.tensors(); |
| for(auto &tensor : tensors) |
| { |
| if(tensor != nullptr) |
| { |
| tensor->desc().data_type = DataType::QASYMM8; |
| tensor->desc().quant_info = QuantizationInfo(0.125f, -10); |
| } |
| } |
| } |
| |
| /** Convert special node |
| * |
| * @param[in,out] g Graph to convert tensors of. |
| * @param[in] fnc Conversion function. |
| * @param[in] optional_arguments Conversion function arguments. |
| */ |
| template <typename NT> |
| void convert_special_node(Graph &g, std::function<bool(INode *, Tensor *)> const &f) |
| { |
| const std::vector<NodeID> nodes_ids = g.nodes(NT::node_type); |
| for(const auto &nodes_id : nodes_ids) |
| { |
| INode *node = arm_compute::utils::cast::polymorphic_downcast<NT *>(g.node(nodes_id)); |
| ARM_COMPUTE_ERROR_ON(node == nullptr); |
| |
| Tensor *output_tensor = node->output(0); |
| ARM_COMPUTE_ERROR_ON(output_tensor == nullptr); |
| |
| f(node, output_tensor); |
| } |
| } |
| |
| /** Converts special tensors |
| * |
| * @param[in,out] g Graph to convert tensors of. |
| */ |
| void convert_special_tensors(Graph &g) |
| { |
| auto softmax_func = [](INode * node, Tensor * tensor) |
| { |
| ARM_COMPUTE_UNUSED(node); |
| tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, 0); |
| return true; |
| }; |
| |
| auto act_func = [](INode * node, Tensor * tensor) |
| { |
| auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(node); |
| if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::TANH) |
| { |
| tensor->desc().quant_info = QuantizationInfo(1.f / 128.f, 128); |
| } |
| else if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::LOGISTIC) |
| { |
| tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, 0); |
| } |
| return true; |
| }; |
| |
| convert_special_node<ActivationLayerNode>(g, act_func); |
| convert_special_node<SoftmaxLayerNode>(g, softmax_func); |
| } |
| |
| /** Handle nodes with bias |
| * |
| * @note Special tensors are for now biases that the data type differ |
| * |
| * @param[in,out] g Graph to convert tensors of. |
| */ |
| void handle_nodes_with_bias(Graph &g) |
| { |
| const std::set<NodeType> special_node_types = { NodeType::ConvolutionLayer, |
| NodeType::DeconvolutionLayer, |
| NodeType::DepthwiseConvolutionLayer, |
| NodeType::FullyConnectedLayer |
| }; |
| |
| for(const auto &spc_type : special_node_types) |
| { |
| const std::vector<NodeID> scp_nodes_ids = g.nodes(spc_type); |
| for(const auto &node_id : scp_nodes_ids) |
| { |
| INode *node = g.node(node_id); |
| if(node != nullptr) |
| { |
| Tensor *tensor = node->input(2); |
| if(tensor != nullptr) |
| { |
| tensor->desc().data_type = DataType::S32; |
| } |
| else |
| { |
| auto params = node->common_node_params(); |
| params.name = params.name.empty() ? "" : params.name + "Bias"; |
| |
| TensorDescriptor b_desc = node->input(1)->desc(); |
| auto depth = b_desc.shape[get_dimension_idx(b_desc.layout, DataLayoutDimension::BATCHES)]; |
| b_desc.shape = TensorShape(depth); |
| |
| auto accessor = std::make_unique<EmptyAccessor>(); |
| auto b_nid = GraphBuilder::add_const_node(g, params, b_desc, std::move(accessor)); |
| g.add_connection(b_nid, 0, node_id, 2); |
| } |
| } |
| } |
| } |
| } |
| } // namespace |
| |
| const char *SyntheticDataTypeMutator::name() |
| { |
| return "SyntheticDataTypeMutator"; |
| } |
| |
| IGraphMutator::MutationType SyntheticDataTypeMutator::type() const |
| { |
| return IGraphMutator::MutationType::IR; |
| } |
| |
| void SyntheticDataTypeMutator::mutate(Graph &g) |
| { |
| if(is_mutation_supported(g)) |
| { |
| // Remove nodes that get optimized out (e.g. BatchNorm) |
| remove_optimized_nodes(g); |
| |
| // Convert tensor |
| convert_tensors(g); |
| convert_special_tensors(g); |
| |
| // Handle special nodes |
| handle_nodes_with_bias(g); |
| } |
| else |
| { |
| ARM_COMPUTE_LOG_GRAPH_VERBOSE("Synthetic data type mutator couldn't be applied" << std::endl); |
| } |
| } |
| } // namespace graph |
| } // namespace arm_compute |