arm_compute v18.05
diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
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+++ b/src/graph/GraphBuilder.cpp
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+/*
+ * Copyright (c) 2018 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/BFS.h"
+#include "arm_compute/graph/nodes/Nodes.h"
+
+#define CHECK_NODEIDX_PAIR(pair, 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()));
+
+namespace arm_compute
+{
+namespace graph
+{
+namespace
+{
+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, TensorDescriptor desc, ITensorAccessorUPtr accessor)
+{
+ params.name = params.name.empty() ? "" : params.name + name;
+ auto nid = GraphBuilder::add_const_node(g, params, std::move(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;
+}
+
+NodeID create_grouped_convolution(Graph &g, NodeParams ¶ms, NodeIdxPair input, NodeID weights, NodeID bias,
+ PadStrideInfo conv_info, ConvolutionMethod method, FastMathHint fast_math_hint, unsigned int num_groups)
+{
+ bool has_bias = (bias != EmptyNodeID);
+
+ // Split input
+ NodeID input_split = GraphBuilder::add_split_node(g, params, input, num_groups, 2);
+
+ // Split weights
+ NodeID weights_split = GraphBuilder::add_split_node(g, params, { weights, 0 }, num_groups, 3);
+
+ // Split bias
+ NodeID bias_split = EmptyNodeID;
+ if(has_bias)
+ {
+ // Split bias
+ bias_split = GraphBuilder::add_split_node(g, params, { bias, 0 }, num_groups, 0);
+ }
+
+ std::vector<NodeIdxPair> convolution_outputs;
+ for(unsigned int i = 0; i < num_groups; ++i)
+ {
+ NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, fast_math_hint);
+ g.add_connection(input_split, i, conv_nid, 0);
+ g.add_connection(weights_split, i, conv_nid, 1);
+ if(has_bias)
+ {
+ g.add_connection(bias_split, i, conv_nid, 2);
+ }
+ set_node_params(g, conv_nid, params);
+ convolution_outputs.push_back({ conv_nid, 0 });
+ }
+
+ // Depth concatenate output
+ return GraphBuilder::add_depth_concatenate_node(g, params, convolution_outputs);
+}
+} // namespace
+
+NodeID GraphBuilder::add_const_node(Graph &g, NodeParams params, 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, 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)
+{
+ return create_simple_single_input_output_node<ActivationLayerNode>(g, params, input, act_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 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_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]);
+
+ // Create weights node
+ TensorDescriptor w_desc = input_tensor_desc;
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL),
+ get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) / num_groups);
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, 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);
+ b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+ }
+
+ if(num_groups == 1)
+ {
+ // Create convolution node and connect
+ NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, 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;
+ }
+ else
+ {
+ return create_grouped_convolution(g, params, input, w_nid, b_nid, conv_info, method, fast_math_hint, num_groups);
+ }
+}
+
+NodeID GraphBuilder::add_depth_concatenate_node(Graph &g, NodeParams params, std::vector<NodeIdxPair> inputs)
+{
+ ARM_COMPUTE_ERROR_ON(inputs.size() == 0);
+
+ NodeID nid = g.add_node<DepthConcatenateLayerNode>(inputs.size());
+
+ 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;
+}
+
+NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, Size2D kernel_spatial_extend, PadStrideInfo conv_info,
+ DepthwiseConvolutionMethod method,
+ ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, const QuantizationInfo 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]);
+
+ // Create weights node
+ TensorDescriptor w_desc = input_tensor_desc;
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), kernel_spatial_extend.width);
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height);
+ w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL),
+ get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL));
+ 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(b_desc.shape.z());
+ 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, method);
+ 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_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>(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,
+ ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor)
+{
+ 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);
+ 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);
+ b_nid = add_const_node_with_name(g, params, "Bias", b_desc, std::move(bias_accessor));
+ }
+
+ // Create convolution node and connect
+ NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs);
+ 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_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_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_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_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]);
+
+ // Create mul node
+ TensorDescriptor mul_desc = input_tensor_desc;
+ const size_t C = input_tensor_desc.shape[get_dimension_idx(mul_desc, DataLayoutDimension::CHANNEL)];
+ mul_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), 1);
+ mul_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), 1);
+ mul_desc.shape.set(get_dimension_idx(input_tensor_desc, 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_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);
+}
+} // namespace graph
+} // namespace arm_compute
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