Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/src/graph/DataLayerVisitor.cpp b/src/graph/DataLayerVisitor.cpp
index 073ffd4..f0fac25 100644
--- a/src/graph/DataLayerVisitor.cpp
+++ b/src/graph/DataLayerVisitor.cpp
@@ -25,8 +25,8 @@
#include "arm_compute/core/Error.h"
#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/TypePrinter.h"
namespace arm_compute
{
@@ -43,17 +43,14 @@
layer_data["data_layout"] = to_string(layout);
// Add padding info
std::ostringstream padding;
- padding << "[" << to_string(ps_info.pad_left()) << ","
- << to_string(ps_info.pad_top()) << ","
- << to_string(ps_info.pad_bottom()) << ","
- << to_string(ps_info.pad_right()) << "]";
+ padding << "[" << to_string(ps_info.pad_left()) << "," << to_string(ps_info.pad_top()) << ","
+ << to_string(ps_info.pad_bottom()) << "," << to_string(ps_info.pad_right()) << "]";
layer_data["pad"] = padding.str();
// Add stride info
std::ostringstream stride;
- stride << "[" << to_string(ps_info.stride().first) << ","
- << to_string(ps_info.stride().second) << "]";
+ stride << "[" << to_string(ps_info.stride().first) << "," << to_string(ps_info.stride().second) << "]";
layer_data["stride"] = stride.str();
@@ -68,12 +65,12 @@
// Change input names for weights / bias (if applicable)
// Assumes input(1) is weights and input(2) is bias
- if(layer_data.count("input_shape1"))
+ if (layer_data.count("input_shape1"))
{
layer_data["weights_shape"] = layer_data["input_shape1"];
layer_data.erase("input_shape1");
}
- if(layer_data.count("input_shape2"))
+ if (layer_data.count("input_shape2"))
{
layer_data["bias_shape"] = layer_data["input_shape2"];
layer_data.erase("input_shape2");
@@ -92,16 +89,17 @@
void add_generic_layer_data(DataLayerVisitor::LayerData &layer_data, T &node)
{
// Loop over each input tensor
- for(size_t tensor_no = 0; tensor_no < node.num_inputs(); ++tensor_no)
+ for (size_t tensor_no = 0; tensor_no < node.num_inputs(); ++tensor_no)
{
// Add input tensor shapes
- if(node.input(tensor_no) != nullptr)
+ if (node.input(tensor_no) != nullptr)
{
- layer_data["input_shape" + to_string(tensor_no)] = "[" + to_string(node.input(tensor_no)->desc().shape) + "]";
+ layer_data["input_shape" + to_string(tensor_no)] =
+ "[" + to_string(node.input(tensor_no)->desc().shape) + "]";
}
}
// Add output tensor shape
- if(node.output(0) != nullptr)
+ if (node.output(0) != nullptr)
{
layer_data["output_shape0"] = "[" + to_string(node.output(0)->desc().shape) + "]";
}
diff --git a/src/graph/Graph.cpp b/src/graph/Graph.cpp
index 4ce5358..3ae83f2 100644
--- a/src/graph/Graph.cpp
+++ b/src/graph/Graph.cpp
@@ -34,24 +34,24 @@
bool Graph::remove_node(NodeID nid)
{
- if(nid >= _nodes.size())
+ if (nid >= _nodes.size())
{
return false;
}
std::unique_ptr<INode> &node = _nodes[nid];
- if(node)
+ if (node)
{
// Remove input connections
- for(auto &input_eid : node->_input_edges)
+ for (auto &input_eid : node->_input_edges)
{
remove_connection(input_eid);
}
// Remove output connections
std::set<EdgeID> output_edges_copy = node->output_edges();
- for(auto &output_eid : output_edges_copy)
+ for (auto &output_eid : output_edges_copy)
{
remove_connection(output_eid);
}
@@ -71,8 +71,10 @@
arm_compute::lock_guard<arm_compute::Mutex> lock(_mtx);
// Check if node index is valid, if node exists and finally if the connection index is valid
- ARM_COMPUTE_ERROR_ON((source >= _nodes.size()) || (_nodes[source] == nullptr) || (source_idx >= _nodes[source]->num_outputs()));
- ARM_COMPUTE_ERROR_ON((sink >= _nodes.size()) || (_nodes[sink] == nullptr) || (sink_idx >= _nodes[sink]->num_inputs()));
+ ARM_COMPUTE_ERROR_ON((source >= _nodes.size()) || (_nodes[source] == nullptr) ||
+ (source_idx >= _nodes[source]->num_outputs()));
+ ARM_COMPUTE_ERROR_ON((sink >= _nodes.size()) || (_nodes[sink] == nullptr) ||
+ (sink_idx >= _nodes[sink]->num_inputs()));
// Get nodes
std::unique_ptr<INode> &source_node = _nodes[source];
@@ -80,23 +82,25 @@
// Check for duplicate connections (Check only sink node)
Edge *sink_node_edge = sink_node->input_edge(sink_idx);
- if((sink_node_edge != nullptr) && (sink_node_edge->producer_id() == source) && (sink_node_edge->producer_idx() == source_idx)
- && (sink_node_edge->consumer_id() == sink) && (sink_node_edge->consumer_idx() == sink_idx))
+ if ((sink_node_edge != nullptr) && (sink_node_edge->producer_id() == source) &&
+ (sink_node_edge->producer_idx() == source_idx) && (sink_node_edge->consumer_id() == sink) &&
+ (sink_node_edge->consumer_idx() == sink_idx))
{
return sink_node_edge->id();
}
// Check if there is already a tensor associated with output if not create one
TensorID tid = source_node->output_id(source_idx);
- if(tid == NullTensorID)
+ if (tid == NullTensorID)
{
tid = create_tensor();
}
std::unique_ptr<Tensor> &tensor = _tensors[tid];
// Create connections
- EdgeID eid = _edges.size();
- auto connection = std::make_unique<Edge>(eid, source_node.get(), source_idx, sink_node.get(), sink_idx, tensor.get());
+ EdgeID eid = _edges.size();
+ auto connection =
+ std::make_unique<Edge>(eid, source_node.get(), source_idx, sink_node.get(), sink_idx, tensor.get());
_edges.push_back(std::move(connection));
// Add connections to source and sink nodes
@@ -117,7 +121,7 @@
bool Graph::remove_connection(EdgeID eid)
{
- if(eid >= _edges.size())
+ if (eid >= _edges.size())
{
return false;
}
@@ -125,22 +129,22 @@
std::unique_ptr<Edge> &edge = _edges[eid];
// Remove node connections
- if(edge != nullptr)
+ if (edge != nullptr)
{
// Get tensor bound to the edge
- if(edge->tensor() != nullptr)
+ if (edge->tensor() != nullptr)
{
edge->tensor()->unbind_edge(eid);
}
// Remove edges from source node
- if(edge->producer() != nullptr)
+ if (edge->producer() != nullptr)
{
edge->producer()->_output_edges.erase(eid);
}
// Remove edges from sink node
- if((edge->consumer() != nullptr) && (edge->consumer_idx() < edge->consumer()->_input_edges.size()))
+ if ((edge->consumer() != nullptr) && (edge->consumer_idx() < edge->consumer()->_input_edges.size()))
{
edge->consumer()->_input_edges[edge->consumer_idx()] = EmptyEdgeID;
}
@@ -231,4 +235,4 @@
return (id >= _tensors.size()) ? nullptr : _tensors[id].get();
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp
index 7e5d313..eab91b2 100644
--- a/src/graph/GraphBuilder.cpp
+++ b/src/graph/GraphBuilder.cpp
@@ -24,10 +24,10 @@
#include "arm_compute/graph/GraphBuilder.h"
#include "arm_compute/core/utils/DataTypeUtils.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/algorithms/TopologicalSort.h"
+#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/Utils.h"
#include "support/ToolchainSupport.h"
@@ -41,7 +41,8 @@
{
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()));
+ 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)
@@ -67,7 +68,8 @@
return Status{};
}
-NodeID add_const_node_with_name(Graph &g, NodeParams params, const std::string &name, const TensorDescriptor &desc, ITensorAccessorUPtr accessor)
+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));
@@ -76,7 +78,7 @@
}
template <typename NT, typename... Args>
-NodeID create_simple_single_input_output_node(Graph &g, NodeParams ¶ms, NodeIdxPair input, Args &&... args)
+NodeID create_simple_single_input_output_node(Graph &g, NodeParams ¶ms, NodeIdxPair input, Args &&...args)
{
check_nodeidx_pair(input, g);
@@ -88,14 +90,17 @@
}
template <typename NT, typename... Args>
-NodeID create_simple_multiple_input_single_output_node(Graph &g, NodeParams ¶ms, const std::vector<NodeIdxPair> &inputs, Args &&... 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)
+ for (const auto &input : inputs)
{
check_nodeidx_pair(input, g);
g.add_connection(input.node_id, input.index, nid, i++);
@@ -106,7 +111,8 @@
}
} // namespace
-NodeID GraphBuilder::add_const_node(Graph &g, NodeParams params, const TensorDescriptor &desc, ITensorAccessorUPtr accessor)
+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);
@@ -114,7 +120,8 @@
return nid;
}
-NodeID GraphBuilder::add_input_node(Graph &g, NodeParams params, const TensorDescriptor &desc, ITensorAccessorUPtr accessor)
+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);
@@ -134,21 +141,35 @@
return nid;
}
-NodeID GraphBuilder::add_activation_node(Graph &g, NodeParams params, NodeIdxPair input, ActivationLayerInfo act_info,
+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)
+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);
+ 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)
+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);
@@ -168,14 +189,14 @@
// Create beta node
NodeID beta_nid = EmptyNodeID;
- if(has_beta)
+ 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)
+ if (has_gamma)
{
gamma_nid = add_const_node_with_name(g, params, "Gamma", common_desc, std::move(gamma_accessor));
}
@@ -185,11 +206,11 @@
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)
+ if (has_beta)
{
g.add_connection(beta_nid, 0, batch_norm_nid, 3);
}
- if(has_gamma)
+ if (has_gamma)
{
g.add_connection(gamma_nid, 0, batch_norm_nid, 4);
}
@@ -198,7 +219,8 @@
return batch_norm_nid;
}
-NodeID GraphBuilder::add_bounding_box_transform_node(Graph &g, NodeParams params, NodeIdxPair input, NodeIdxPair deltas, BoundingBoxTransformInfo info)
+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);
@@ -217,10 +239,17 @@
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,
+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)
{
@@ -241,7 +270,7 @@
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())
+ if (!weights_quant_info.empty())
{
w_desc.quant_info = weights_quant_info;
}
@@ -250,11 +279,11 @@
// Create bias nodes
NodeID b_nid = EmptyNodeID;
- if(has_bias)
+ 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))
+ if (is_data_type_quantized_asymmetric(input_tensor_desc.data_type))
{
b_desc.data_type = DataType::S32;
}
@@ -265,7 +294,7 @@
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)
+ if (has_bias)
{
g.add_connection(b_nid, 0, conv_nid, 2);
}
@@ -274,8 +303,12 @@
return conv_nid;
}
-NodeID GraphBuilder::add_deconvolution_node(Graph &g, NodeParams params, NodeIdxPair input,
- Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo deconv_info,
+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)
{
@@ -301,11 +334,11 @@
// Create bias nodes
NodeID b_nid = EmptyNodeID;
- if(has_bias)
+ 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))
+ if (is_data_type_quantized_asymmetric(input_tensor_desc.data_type))
{
b_desc.data_type = DataType::S32;
}
@@ -313,10 +346,10 @@
}
// Create convolution node and connect
- NodeID deconv_nid = g.add_node<DeconvolutionLayerNode>(descriptors::DeconvolutionLayerDescriptor{ deconv_info });
+ 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)
+ if (has_bias)
{
g.add_connection(b_nid, 0, deconv_nid, 2);
}
@@ -325,14 +358,26 @@
return deconv_nid;
}
-NodeID GraphBuilder::add_concatenate_node(Graph &g, NodeParams params, const std::vector<NodeIdxPair> &inputs, const descriptors::ConcatLayerDescriptor &concat_descriptor)
+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);
+ 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)
+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));
@@ -349,7 +394,7 @@
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())
+ if (!quant_info.empty())
{
w_desc.quant_info = quant_info;
}
@@ -358,12 +403,13 @@
// Create bias nodes
NodeID b_nid = EmptyNodeID;
- if(has_bias)
+ if (has_bias)
{
TensorDescriptor b_desc = input_tensor_desc;
- b_desc.shape = TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) * depth_multiplier);
+ b_desc.shape =
+ TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) * depth_multiplier);
- if(is_data_type_quantized_asymmetric(b_desc.data_type))
+ if (is_data_type_quantized_asymmetric(b_desc.data_type))
{
b_desc.data_type = DataType::S32;
}
@@ -375,7 +421,7 @@
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)
+ if (has_bias)
{
g.add_connection(b_nid, 0, conv_nid, 2);
}
@@ -394,7 +440,12 @@
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)
+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);
@@ -411,18 +462,24 @@
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)
+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]);
+ 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())
+ if (!anchor_quant_info.empty())
{
anchor_desc.quant_info = anchor_quant_info;
}
@@ -446,12 +503,13 @@
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)
+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 });
+ 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);
@@ -466,9 +524,15 @@
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, FastMathHint fast_math_hint)
+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,
+ FastMathHint fast_math_hint)
{
check_nodeidx_pair(input, g);
ARM_COMPUTE_ERROR_ON(num_outputs == 0);
@@ -483,7 +547,7 @@
NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs, out_quant_info, fc_info, fast_math_hint);
g.add_connection(input.node_id, input.index, fc_nid, 0);
g.add_connection(weights_nid, 0, fc_nid, 1);
- if(has_bias)
+ if (has_bias)
{
g.add_connection(bias_nid, 0, fc_nid, 2);
}
@@ -493,10 +557,16 @@
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,
+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, FastMathHint fast_math_hint)
+ const QuantizationInfo &weights_quant_info,
+ const QuantizationInfo &out_quant_info,
+ FastMathHint fast_math_hint)
{
check_nodeidx_pair(input, g);
ARM_COMPUTE_ERROR_ON(num_outputs == 0);
@@ -507,16 +577,17 @@
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);
+ 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)
+ 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))
+ if (is_data_type_quantized_asymmetric(input_tensor_desc.data_type))
{
b_desc.data_type = DataType::S32;
}
@@ -527,7 +598,7 @@
NodeID fc_nid = g.add_node<FullyConnectedLayerNode>(num_outputs, out_quant_info, fc_info, fast_math_hint);
g.add_connection(input.node_id, input.index, fc_nid, 0);
g.add_connection(w_nid, 0, fc_nid, 1);
- if(has_bias)
+ if (has_bias)
{
g.add_connection(b_nid, 0, fc_nid, 2);
}
@@ -537,7 +608,12 @@
return fc_nid;
}
-NodeID GraphBuilder::add_generate_proposals_node(Graph &g, NodeParams params, NodeIdxPair scores, NodeIdxPair deltas, NodeIdxPair anchors, GenerateProposalsInfo info)
+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);
@@ -558,13 +634,14 @@
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)
+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)
+NodeID GraphBuilder::add_normalize_planar_yuv_node(
+ Graph &g, NodeParams params, NodeIdxPair input, ITensorAccessorUPtr mean_accessor, ITensorAccessorUPtr std_accessor)
{
check_nodeidx_pair(input, g);
@@ -589,12 +666,14 @@
return norm_planar_yuv_nid;
}
-NodeID GraphBuilder::add_pad_node(Graph &g, NodeParams params, NodeIdxPair input, const PaddingList &paddings, PixelValue pad_value)
+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)
+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);
}
@@ -618,12 +697,18 @@
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)
+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)
+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);
@@ -638,12 +723,16 @@
return prior_nid;
}
-NodeID GraphBuilder::add_quantization_node(Graph &g, NodeParams params, NodeIdxPair input, const QuantizationInfo &out_quant_info)
+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)
+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);
}
@@ -658,13 +747,14 @@
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)
+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)
+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);
@@ -678,7 +768,11 @@
return nid;
}
-NodeID GraphBuilder::add_scale_layer(Graph &g, const NodeParams ¶ms, NodeIdxPair input, ITensorAccessorUPtr mul_accessor, ITensorAccessorUPtr add_accessor)
+NodeID GraphBuilder::add_scale_layer(Graph &g,
+ const NodeParams ¶ms,
+ NodeIdxPair input,
+ ITensorAccessorUPtr mul_accessor,
+ ITensorAccessorUPtr add_accessor)
{
check_nodeidx_pair(input, g);
@@ -688,22 +782,23 @@
// 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)];
+ 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 };
+ 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 };
+ 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);
+ 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;
}
@@ -713,17 +808,25 @@
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)
+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)
+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)
+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);
}
@@ -770,7 +873,8 @@
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));
+ 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);
diff --git a/src/graph/GraphContext.cpp b/src/graph/GraphContext.cpp
index 7b74c2f..10850aa 100644
--- a/src/graph/GraphContext.cpp
+++ b/src/graph/GraphContext.cpp
@@ -24,15 +24,14 @@
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph.h"
-#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/Utils.h"
namespace arm_compute
{
namespace graph
{
-GraphContext::GraphContext()
- : _config(), _memory_managers(), _weights_managers()
+GraphContext::GraphContext() : _config(), _memory_managers(), _weights_managers()
{
}
@@ -56,7 +55,7 @@
bool GraphContext::insert_memory_management_ctx(MemoryManagerContext &&memory_ctx)
{
Target target = memory_ctx.target;
- if(target == Target::UNSPECIFIED || _memory_managers.find(target) != std::end(_memory_managers))
+ if (target == Target::UNSPECIFIED || _memory_managers.find(target) != std::end(_memory_managers))
{
return false;
}
@@ -79,7 +78,7 @@
{
Target target = weights_managers.target;
- if(_weights_managers.find(target) != std::end(_weights_managers))
+ if (_weights_managers.find(target) != std::end(_weights_managers))
{
return false;
}
@@ -102,17 +101,17 @@
void GraphContext::finalize()
{
const size_t num_pools = 1;
- for(auto &mm_obj : _memory_managers)
+ for (auto &mm_obj : _memory_managers)
{
ARM_COMPUTE_ERROR_ON(!mm_obj.second.allocator);
// Finalize intra layer memory manager
- if(mm_obj.second.intra_mm != nullptr)
+ if (mm_obj.second.intra_mm != nullptr)
{
mm_obj.second.intra_mm->populate(*mm_obj.second.allocator, num_pools);
}
// Finalize cross layer memory manager
- if(mm_obj.second.cross_mm != nullptr)
+ if (mm_obj.second.cross_mm != nullptr)
{
mm_obj.second.cross_mm->populate(*mm_obj.second.allocator, num_pools);
}
diff --git a/src/graph/GraphManager.cpp b/src/graph/GraphManager.cpp
index 45b608c..58ae60d 100644
--- a/src/graph/GraphManager.cpp
+++ b/src/graph/GraphManager.cpp
@@ -23,15 +23,15 @@
*/
#include "arm_compute/graph/GraphManager.h"
+#include "arm_compute/graph/algorithms/TopologicalSort.h"
+#include "arm_compute/graph/detail/CrossLayerMemoryManagerHelpers.h"
+#include "arm_compute/graph/detail/ExecutionHelpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/PassManager.h"
#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/algorithms/TopologicalSort.h"
-#include "arm_compute/graph/detail/CrossLayerMemoryManagerHelpers.h"
-#include "arm_compute/graph/detail/ExecutionHelpers.h"
#include "src/common/utils/Log.h"
@@ -39,8 +39,7 @@
{
namespace graph
{
-GraphManager::GraphManager()
- : _workloads()
+GraphManager::GraphManager() : _workloads()
{
}
@@ -49,7 +48,7 @@
ARM_COMPUTE_LOG_INFO_WITH_FUNCNAME_ACL("Initiate graph configuration!");
// Check if graph has been registered
- if(_workloads.find(graph.id()) != std::end(_workloads))
+ if (_workloads.find(graph.id()) != std::end(_workloads))
{
ARM_COMPUTE_ERROR("Graph is already registered!");
}
@@ -62,7 +61,7 @@
// In case CLVK is selected, use the CL backend and
// update config
- if(target == Target::CLVK)
+ if (target == Target::CLVK)
{
forced_target = Target::CL;
GraphConfig config = ctx.config();
@@ -71,7 +70,7 @@
ctx.set_config(config);
}
- if(!is_target_supported(target))
+ if (!is_target_supported(target))
{
forced_target = get_default_target();
ARM_COMPUTE_LOG_GRAPH_INFO("Switching target from " << target << " to " << forced_target << std::endl);
@@ -105,7 +104,7 @@
detail::prepare_all_tasks(workload);
// Setup tensor memory (Allocate all tensors or setup transition manager)
- if(ctx.config().use_transition_memory_manager)
+ if (ctx.config().use_transition_memory_manager)
{
detail::configure_transition_manager(graph, ctx, workload);
}
@@ -130,10 +129,10 @@
auto it = _workloads.find(graph.id());
ARM_COMPUTE_ERROR_ON_MSG(it == std::end(_workloads), "Graph is not registered!");
- while(true)
+ while (true)
{
// Call input accessors
- if(!detail::call_all_input_node_accessors(it->second))
+ if (!detail::call_all_input_node_accessors(it->second))
{
return;
}
@@ -142,7 +141,7 @@
detail::call_all_tasks(it->second);
// Call output accessors
- if(!detail::call_all_output_node_accessors(it->second))
+ if (!detail::call_all_output_node_accessors(it->second))
{
return;
}
@@ -157,4 +156,4 @@
_workloads.erase(it);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/INode.cpp b/src/graph/INode.cpp
index 70fe44e..83c3ef7 100644
--- a/src/graph/INode.cpp
+++ b/src/graph/INode.cpp
@@ -75,17 +75,17 @@
void INode::set_output_tensor(TensorID tid, size_t idx)
{
- if(tid != NullTensorID && (idx < _outputs.size()) && (_graph->tensor(tid) != nullptr))
+ if (tid != NullTensorID && (idx < _outputs.size()) && (_graph->tensor(tid) != nullptr))
{
ARM_COMPUTE_ERROR_ON(_graph == nullptr);
Tensor *updated_tensor = _graph->tensor(tid);
_outputs[idx] = tid;
// Set tensor to all output edges of the node
- for(auto &output_edge_id : _output_edges)
+ for (auto &output_edge_id : _output_edges)
{
auto output_edge = _graph->edge(output_edge_id);
- if(output_edge != nullptr)
+ if (output_edge != nullptr)
{
// Unbind edge from current tensor
auto current_output_tensor = output_edge->tensor();
diff --git a/src/graph/INodeVisitor.cpp b/src/graph/INodeVisitor.cpp
index 5369f6f..90b2e33 100644
--- a/src/graph/INodeVisitor.cpp
+++ b/src/graph/INodeVisitor.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/graph/INodeVisitor.h"
+
#include "arm_compute/graph/nodes/Nodes.h"
namespace arm_compute
diff --git a/src/graph/PassManager.cpp b/src/graph/PassManager.cpp
index f7e214c..9a889e1 100644
--- a/src/graph/PassManager.cpp
+++ b/src/graph/PassManager.cpp
@@ -29,8 +29,7 @@
{
namespace graph
{
-PassManager::PassManager()
- : _passes()
+PassManager::PassManager() : _passes()
{
}
@@ -46,7 +45,7 @@
void PassManager::append(std::unique_ptr<IGraphMutator> pass, bool conditional)
{
- if(pass && conditional)
+ if (pass && conditional)
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Appending mutating pass : " << pass->name() << std::endl);
_passes.push_back(std::move(pass));
@@ -60,9 +59,9 @@
void PassManager::run_all(Graph &g)
{
- for(auto &pass : _passes)
+ for (auto &pass : _passes)
{
- if(pass)
+ if (pass)
{
ARM_COMPUTE_LOG_GRAPH_INFO("Running mutating pass : " << pass->name() << std::endl);
pass->mutate(g);
@@ -72,9 +71,9 @@
void PassManager::run_type(Graph &g, IGraphMutator::MutationType type)
{
- for(auto &pass : _passes)
+ for (auto &pass : _passes)
{
- if(pass && (pass->type() == type))
+ if (pass && (pass->type() == type))
{
ARM_COMPUTE_LOG_GRAPH_INFO("Running mutating pass : " << pass->name() << std::endl);
pass->mutate(g);
@@ -84,17 +83,17 @@
void PassManager::run_index(Graph &g, size_t index)
{
- if(index >= _passes.size())
+ if (index >= _passes.size())
{
return;
}
auto &pass = _passes.at(index);
- if(pass != nullptr)
+ if (pass != nullptr)
{
ARM_COMPUTE_LOG_GRAPH_INFO("Running mutating pass : " << pass->name() << std::endl);
pass->mutate(g);
}
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/Tensor.cpp b/src/graph/Tensor.cpp
index 3d47234..72679c4 100644
--- a/src/graph/Tensor.cpp
+++ b/src/graph/Tensor.cpp
@@ -75,20 +75,20 @@
bool Tensor::call_accessor()
{
// Early exit guard
- if(!_accessor || !_handle)
+ if (!_accessor || !_handle)
{
return false;
}
const bool access_data = _accessor->access_tensor_data();
- if(access_data)
+ if (access_data)
{
// Map tensor
_handle->map(true);
// Return in case of null backend buffer
- if(_handle->tensor().buffer() == nullptr)
+ if (_handle->tensor().buffer() == nullptr)
{
return false;
}
@@ -97,7 +97,7 @@
// Call accessor
bool retval = _accessor->access_tensor(_handle->tensor());
- if(access_data)
+ if (access_data)
{
// Unmap tensor
_handle->unmap();
diff --git a/src/graph/TypeLoader.cpp b/src/graph/TypeLoader.cpp
index 3c51289..e1248fb 100644
--- a/src/graph/TypeLoader.cpp
+++ b/src/graph/TypeLoader.cpp
@@ -31,10 +31,9 @@
{
arm_compute::DataLayout data_layout_from_name(const std::string &name)
{
- static const std::map<std::string, arm_compute::DataLayout> data_layouts =
- {
- { "nhwc", DataLayout::NHWC },
- { "nchw", DataLayout::NCHW },
+ static const std::map<std::string, arm_compute::DataLayout> data_layouts = {
+ {"nhwc", DataLayout::NHWC},
+ {"nchw", DataLayout::NCHW},
};
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
@@ -45,7 +44,7 @@
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
}
- catch(const std::out_of_range &)
+ catch (const std::out_of_range &)
{
throw std::invalid_argument(name);
}
@@ -55,11 +54,10 @@
{
Target target_from_name(const std::string &name)
{
- static const std::map<std::string, Target> targets =
- {
- { "neon", Target::NEON },
- { "cl", Target::CL },
- { "clvk", Target::CLVK },
+ static const std::map<std::string, Target> targets = {
+ {"neon", Target::NEON},
+ {"cl", Target::CL},
+ {"clvk", Target::CLVK},
};
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
@@ -70,7 +68,7 @@
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
}
- catch(const std::out_of_range &)
+ catch (const std::out_of_range &)
{
throw std::invalid_argument(name);
}
@@ -79,12 +77,11 @@
ConvolutionMethod Convolution_method_from_name(const std::string &name)
{
- static const std::map<std::string, ConvolutionMethod> methods =
- {
- { "default", ConvolutionMethod::Default },
- { "direct", ConvolutionMethod::Direct },
- { "gemm", ConvolutionMethod::GEMM },
- { "winograd", ConvolutionMethod::Winograd },
+ static const std::map<std::string, ConvolutionMethod> methods = {
+ {"default", ConvolutionMethod::Default},
+ {"direct", ConvolutionMethod::Direct},
+ {"gemm", ConvolutionMethod::GEMM},
+ {"winograd", ConvolutionMethod::Winograd},
};
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
@@ -95,7 +92,7 @@
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
}
- catch(const std::out_of_range &)
+ catch (const std::out_of_range &)
{
throw std::invalid_argument(name);
}
@@ -104,10 +101,9 @@
DepthwiseConvolutionMethod depthwise_convolution_method_from_name(const std::string &name)
{
- static const std::map<std::string, DepthwiseConvolutionMethod> methods =
- {
- { "default", DepthwiseConvolutionMethod::Default },
- { "optimized3x3", DepthwiseConvolutionMethod::Optimized3x3 },
+ static const std::map<std::string, DepthwiseConvolutionMethod> methods = {
+ {"default", DepthwiseConvolutionMethod::Default},
+ {"optimized3x3", DepthwiseConvolutionMethod::Optimized3x3},
};
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
@@ -118,7 +114,7 @@
#ifndef ARM_COMPUTE_EXCEPTIONS_DISABLED
}
- catch(const std::out_of_range &)
+ catch (const std::out_of_range &)
{
throw std::invalid_argument(name);
}
diff --git a/src/graph/Utils.cpp b/src/graph/Utils.cpp
index dcab177..452d8ec 100644
--- a/src/graph/Utils.cpp
+++ b/src/graph/Utils.cpp
@@ -23,8 +23,8 @@
*/
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/mutators/GraphMutators.h"
namespace arm_compute
@@ -33,16 +33,17 @@
{
bool is_target_supported(Target target)
{
- return backends::BackendRegistry::get().contains(target) && backends::BackendRegistry::get().find_backend(target)->is_backend_supported();
+ return backends::BackendRegistry::get().contains(target) &&
+ backends::BackendRegistry::get().find_backend(target)->is_backend_supported();
}
Target get_default_target()
{
- if(is_target_supported(Target::NEON))
+ if (is_target_supported(Target::NEON))
{
return Target::NEON;
}
- if(is_target_supported(Target::CL))
+ if (is_target_supported(Target::CL))
{
return Target::CL;
}
@@ -52,18 +53,18 @@
void force_target_to_graph(Graph &g, Target target)
{
auto &nodes = g.nodes();
- for(auto &node : nodes)
+ for (auto &node : nodes)
{
- if(node)
+ if (node)
{
node->set_assigned_target(target);
}
}
auto &tensors = g.tensors();
- for(auto &tensor : tensors)
+ for (auto &tensor : tensors)
{
- if(tensor)
+ if (tensor)
{
tensor->desc().target = target;
}
@@ -76,9 +77,9 @@
PassManager pm;
// Passes that mutate graph IR
- if(cfg.use_synthetic_type)
+ if (cfg.use_synthetic_type)
{
- switch(cfg.synthetic_type)
+ switch (cfg.synthetic_type)
{
case DataType::QASYMM8:
case DataType::QASYMM8_SIGNED:
@@ -107,9 +108,9 @@
void release_default_graph_context(GraphContext &ctx)
{
- for(const auto &backend : backends::BackendRegistry::get().backends())
+ for (const auto &backend : backends::BackendRegistry::get().backends())
{
- if(backend.second->is_backend_supported())
+ if (backend.second->is_backend_supported())
{
backend.second->release_backend_context(ctx);
}
@@ -118,9 +119,9 @@
void sync_backends()
{
- for(const auto &backend : backends::BackendRegistry::get().backends())
+ for (const auto &backend : backends::BackendRegistry::get().backends())
{
- if(backend.second->backend_allocator())
+ if (backend.second->backend_allocator())
{
backend.second->sync();
}
@@ -129,10 +130,10 @@
void setup_requested_backend_context(GraphContext &ctx, Target target)
{
- if(backends::BackendRegistry::get().contains(target))
+ if (backends::BackendRegistry::get().contains(target))
{
const auto &backend = backends::BackendRegistry::get().find_backend(target);
- if(backend->is_backend_supported())
+ if (backend->is_backend_supported())
{
backend->setup_backend_context(ctx);
}
@@ -141,20 +142,22 @@
size_t get_dimension_size(const TensorDescriptor &descriptor, const DataLayoutDimension data_layout_dimension)
{
- ARM_COMPUTE_ERROR_ON_MSG(descriptor.layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!");
+ ARM_COMPUTE_ERROR_ON_MSG(descriptor.layout == DataLayout::UNKNOWN,
+ "Cannot retrieve the dimension index for an unknown layout!");
return descriptor.shape[get_dimension_idx(descriptor.layout, data_layout_dimension)];
}
size_t get_dimension_idx(DataLayout data_layout, const DataLayoutDimension data_layout_dimension)
{
- ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN, "Cannot retrieve the dimension index for an unknown layout!");
+ ARM_COMPUTE_ERROR_ON_MSG(data_layout == DataLayout::UNKNOWN,
+ "Cannot retrieve the dimension index for an unknown layout!");
/* Return the index based on the data layout
* [N C H W]
* [3 2 1 0]
* [N H W C]
*/
- switch(data_layout_dimension)
+ switch (data_layout_dimension)
{
case DataLayoutDimension::CHANNEL:
return (data_layout == DataLayout::NCHW) ? 2 : 0;
@@ -181,13 +184,13 @@
const Graph *g = node.graph();
ARM_COMPUTE_ERROR_ON(g == nullptr);
- for(auto &output_edge_id : node.output_edges())
+ for (auto &output_edge_id : node.output_edges())
{
auto output_edge = g->edge(output_edge_id);
- if(output_edge != nullptr)
+ if (output_edge != nullptr)
{
ARM_COMPUTE_ERROR_ON(output_edge->consumer() == nullptr);
- driving_nodes.push_back({ output_edge->consumer_id(), output_edge->consumer_idx() });
+ driving_nodes.push_back({output_edge->consumer_id(), output_edge->consumer_idx()});
}
}
@@ -201,13 +204,13 @@
const Graph *g = node.graph();
ARM_COMPUTE_ERROR_ON(g == nullptr);
- for(auto &input_edge_id : node.input_edges())
+ for (auto &input_edge_id : node.input_edges())
{
auto input_edge = g->edge(input_edge_id);
- if(input_edge != nullptr)
+ if (input_edge != nullptr)
{
ARM_COMPUTE_ERROR_ON(input_edge->producer() == nullptr);
- driver_nodes.push_back({ input_edge->producer_id(), input_edge->producer_idx() });
+ driver_nodes.push_back({input_edge->producer_id(), input_edge->producer_idx()});
}
}
@@ -216,7 +219,7 @@
void configure_tensor(Tensor *tensor)
{
- if(tensor != nullptr && tensor->handle() == nullptr)
+ if (tensor != nullptr && tensor->handle() == nullptr)
{
Target target = tensor->desc().target;
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(target);
diff --git a/src/graph/Workload.cpp b/src/graph/Workload.cpp
index b9d5729..9dddad7 100644
--- a/src/graph/Workload.cpp
+++ b/src/graph/Workload.cpp
@@ -40,12 +40,12 @@
void execute_task(ExecutionTask &task)
{
- if(task.task)
+ if (task.task)
{
task.task->run();
}
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
- else if(task.node->type() == NodeType::PrintLayer)
+ else if (task.node->type() == NodeType::PrintLayer)
{
auto print_node = utils::cast::polymorphic_downcast<PrintLayerNode *>(task.node);
auto input_handle = print_node->input(0)->handle();
@@ -61,14 +61,13 @@
void ExecutionTask::prepare()
{
- if(task)
+ if (task)
{
task->prepare();
}
}
-TaskExecutor::TaskExecutor()
- : execute_function(execute_task)
+TaskExecutor::TaskExecutor() : execute_function(execute_task)
{
}
@@ -78,4 +77,4 @@
return executor;
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/algorithms/TopologicalSort.cpp b/src/graph/algorithms/TopologicalSort.cpp
index 3a69352..08e14e1 100644
--- a/src/graph/algorithms/TopologicalSort.cpp
+++ b/src/graph/algorithms/TopologicalSort.cpp
@@ -50,14 +50,14 @@
ARM_COMPUTE_ERROR_ON(graph == nullptr);
bool are_all_visited = true;
- for(const auto &input_edge_id : node->input_edges())
+ for (const auto &input_edge_id : node->input_edges())
{
- if(input_edge_id != EmptyNodeID)
+ if (input_edge_id != EmptyNodeID)
{
const Edge *input_edge = graph->edge(input_edge_id);
ARM_COMPUTE_ERROR_ON(input_edge == nullptr);
ARM_COMPUTE_ERROR_ON(input_edge->producer() == nullptr);
- if(!visited[input_edge->producer_id()])
+ if (!visited[input_edge->producer_id()])
{
are_all_visited = false;
break;
@@ -80,9 +80,9 @@
std::list<NodeID> queue;
// Push inputs and mark as visited
- for(auto &input : g.nodes(NodeType::Input))
+ for (auto &input : g.nodes(NodeType::Input))
{
- if(input != EmptyNodeID)
+ if (input != EmptyNodeID)
{
visited[input] = true;
queue.push_back(input);
@@ -90,9 +90,9 @@
}
// Push const nodes and mark as visited
- for(auto &const_node : g.nodes(NodeType::Const))
+ for (auto &const_node : g.nodes(NodeType::Const))
{
- if(const_node != EmptyNodeID)
+ if (const_node != EmptyNodeID)
{
visited[const_node] = true;
queue.push_back(const_node);
@@ -100,7 +100,7 @@
}
// Iterate over vector and edges
- while(!queue.empty())
+ while (!queue.empty())
{
// Dequeue a node from queue and process
NodeID n = queue.front();
@@ -109,11 +109,11 @@
const INode *node = g.node(n);
ARM_COMPUTE_ERROR_ON(node == nullptr);
- for(const auto &eid : node->output_edges())
+ for (const auto &eid : node->output_edges())
{
const Edge *e = g.edge(eid);
ARM_COMPUTE_ERROR_ON(e == nullptr);
- if(!visited[e->consumer_id()] && detail::all_inputs_are_visited(e->consumer(), visited))
+ if (!visited[e->consumer_id()] && detail::all_inputs_are_visited(e->consumer(), visited))
{
visited[e->consumer_id()] = true;
queue.push_back(e->consumer_id());
@@ -135,9 +135,9 @@
std::stack<NodeID> stack;
// Push inputs and mark as visited
- for(auto &input : g.nodes(NodeType::Input))
+ for (auto &input : g.nodes(NodeType::Input))
{
- if(input != EmptyNodeID)
+ if (input != EmptyNodeID)
{
visited[input] = true;
stack.push(input);
@@ -145,9 +145,9 @@
}
// Push const nodes and mark as visited
- for(auto &const_node : g.nodes(NodeType::Const))
+ for (auto &const_node : g.nodes(NodeType::Const))
{
- if(const_node != EmptyNodeID)
+ if (const_node != EmptyNodeID)
{
visited[const_node] = true;
stack.push(const_node);
@@ -155,7 +155,7 @@
}
// Iterate over vector and edges
- while(!stack.empty())
+ while (!stack.empty())
{
// Pop a node from stack and process
NodeID n = stack.top();
@@ -163,7 +163,7 @@
stack.pop();
// Mark node as visited
- if(!visited[n])
+ if (!visited[n])
{
visited[n] = true;
}
@@ -171,11 +171,11 @@
const INode *node = g.node(n);
ARM_COMPUTE_ERROR_ON(node == nullptr);
// Reverse iterate to push branches from right to left and pop on the opposite order
- for(const auto &eid : arm_compute::utils::iterable::reverse_iterate(node->output_edges()))
+ for (const auto &eid : arm_compute::utils::iterable::reverse_iterate(node->output_edges()))
{
const Edge *e = g.edge(eid);
ARM_COMPUTE_ERROR_ON(e == nullptr);
- if(!visited[e->consumer_id()] && detail::all_inputs_are_visited(e->consumer(), visited))
+ if (!visited[e->consumer_id()] && detail::all_inputs_are_visited(e->consumer(), visited))
{
stack.push(e->consumer_id());
}
diff --git a/src/graph/backends/BackendRegistry.cpp b/src/graph/backends/BackendRegistry.cpp
index 46b4f99..bb6af79 100644
--- a/src/graph/backends/BackendRegistry.cpp
+++ b/src/graph/backends/BackendRegistry.cpp
@@ -31,8 +31,7 @@
{
namespace backends
{
-BackendRegistry::BackendRegistry()
- : _registered_backends()
+BackendRegistry::BackendRegistry() : _registered_backends()
{
}
diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp
index 01e5ab1..e27a410 100644
--- a/src/graph/backends/CL/CLDeviceBackend.cpp
+++ b/src/graph/backends/CL/CLDeviceBackend.cpp
@@ -23,18 +23,17 @@
*/
#include "arm_compute/graph/backends/CL/CLDeviceBackend.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/INode.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Tensor.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/graph/backends/BackendRegistrar.h"
#include "arm_compute/graph/backends/CL/CLFunctionFactory.h"
#include "arm_compute/graph/backends/CL/CLNodeValidator.h"
#include "arm_compute/graph/backends/CL/CLSubTensorHandle.h"
#include "arm_compute/graph/backends/CL/CLTensorHandle.h"
-
-#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphContext.h"
+#include "arm_compute/graph/INode.h"
+#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/Tensor.h"
#include "arm_compute/runtime/BlobLifetimeManager.h"
#include "arm_compute/runtime/CL/CLBufferAllocator.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
@@ -64,7 +63,12 @@
static detail::BackendRegistrar<CLDeviceBackend> CLDeviceBackend_registrar(Target::CL);
CLDeviceBackend::CLDeviceBackend()
- : _context_count(0), _tuner(), _gemm_heuristics(), _allocator(nullptr), _tuner_file(), _backend_type(CLBackendType::Native)
+ : _context_count(0),
+ _tuner(),
+ _gemm_heuristics(),
+ _allocator(nullptr),
+ _tuner_file(),
+ _backend_type(CLBackendType::Native)
{
}
@@ -95,7 +99,7 @@
{
ARM_COMPUTE_UNUSED(ctx);
_context_count--;
- if(_context_count == 0) // No more context using the backend: free resources
+ if (_context_count == 0) // No more context using the backend: free resources
{
_allocator = nullptr;
}
@@ -105,7 +109,7 @@
{
// Force backend initialization
_context_count++;
- if(_context_count == 1)
+ if (_context_count == 1)
{
_backend_type = ctx.config().backend_type;
initialize_backend();
@@ -115,7 +119,7 @@
_tuner_file = ctx.config().tuner_file;
// Load tuner data if available
- if(file_exists(_tuner_file))
+ if (file_exists(_tuner_file))
{
_tuner.load_from_file(_tuner_file);
}
@@ -128,7 +132,7 @@
CLScheduler::get().gemm_heuristics()->reload_from_file(ctx.config().mlgo_file);
// Setup a management backend
- if(ctx.memory_management_ctx(Target::CL) == nullptr)
+ if (ctx.memory_management_ctx(Target::CL) == nullptr)
{
MemoryManagerContext mm_ctx;
mm_ctx.target = Target::CL;
@@ -141,7 +145,7 @@
}
// Create function level weights manager
- if(ctx.weights_management_ctx(Target::CL) == nullptr)
+ if (ctx.weights_management_ctx(Target::CL) == nullptr)
{
WeightsManagerContext wm_ctx;
wm_ctx.target = Target::CL;
@@ -174,9 +178,10 @@
return std::make_unique<CLTensorHandle>(info);
}
-std::unique_ptr<ITensorHandle> CLDeviceBackend::create_subtensor(ITensorHandle *parent, TensorShape shape, Coordinates coords, bool extend_parent)
+std::unique_ptr<ITensorHandle>
+CLDeviceBackend::create_subtensor(ITensorHandle *parent, TensorShape shape, Coordinates coords, bool extend_parent)
{
- if(parent == nullptr)
+ if (parent == nullptr)
{
return nullptr;
}
@@ -203,7 +208,7 @@
std::shared_ptr<arm_compute::IMemoryManager> CLDeviceBackend::create_memory_manager(MemoryManagerAffinity affinity)
{
- if(affinity == MemoryManagerAffinity::Offset)
+ if (affinity == MemoryManagerAffinity::Offset)
{
ARM_COMPUTE_LOG_GRAPH_WARNING("CL Backend does not support offset affinity memory management!");
return nullptr;
diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp
index 8828104..d4e1aa8 100644
--- a/src/graph/backends/CL/CLFunctionsFactory.cpp
+++ b/src/graph/backends/CL/CLFunctionsFactory.cpp
@@ -22,12 +22,12 @@
* SOFTWARE.
*/
#include "arm_compute/graph/backends/CL/CLFunctionFactory.h"
-
+#include "arm_compute/graph/backends/FunctionHelpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/backends/FunctionHelpers.h"
#include "arm_compute/runtime/CL/CLFunctions.h"
#include "arm_compute/runtime/CPP/CPPFunctions.h"
+
#include "src/core/CL/CLKernels.h"
#include "support/Cast.h"
@@ -89,20 +89,19 @@
{
public:
/* Default constructor */
- CPPWrapperFunction()
- : _tensors(), _func(nullptr)
+ CPPWrapperFunction() : _tensors(), _func(nullptr)
{
}
void run() override
{
- for(auto &tensor : _tensors)
+ for (auto &tensor : _tensors)
{
tensor->map(CLScheduler::get().queue());
}
_func->run();
- for(auto &tensor : _tensors)
+ for (auto &tensor : _tensors)
{
tensor->unmap(CLScheduler::get().queue());
}
@@ -127,7 +126,8 @@
{
// Specialized functions
template <>
-std::unique_ptr<IFunction> create_detection_output_layer<CPPDetectionOutputLayer, CLTargetInfo>(DetectionOutputLayerNode &node)
+std::unique_ptr<IFunction>
+create_detection_output_layer<CPPDetectionOutputLayer, CLTargetInfo>(DetectionOutputLayerNode &node)
{
validate_node<CLTargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */);
@@ -149,16 +149,12 @@
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << CLTargetInfo::TargetType
- << " Data Type: " << input0->info()->data_type()
- << " Input0 shape: " << input0->info()->tensor_shape()
- << " Input1 shape: " << input1->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << CLTargetInfo::TargetType
+ << " Data Type: " << input0->info()->data_type() << " Input0 shape: "
+ << input0->info()->tensor_shape() << " Input1 shape: " << input1->info()->tensor_shape()
<< " Input2 shape: " << input2->info()->tensor_shape()
<< " Output shape: " << output->info()->tensor_shape()
- << " DetectionOutputLayer info: " << detect_info
- << std::endl);
+ << " DetectionOutputLayer info: " << detect_info << std::endl);
auto wrap_function = std::make_unique<CPPWrapperFunction>();
@@ -171,7 +167,8 @@
return std::move(wrap_function);
}
template <>
-std::unique_ptr<IFunction> create_detection_post_process_layer<CPPDetectionPostProcessLayer, CLTargetInfo>(DetectionPostProcessLayerNode &node)
+std::unique_ptr<IFunction>
+create_detection_post_process_layer<CPPDetectionPostProcessLayer, CLTargetInfo>(DetectionPostProcessLayerNode &node)
{
validate_node<CLTargetInfo>(node, 3 /* expected inputs */, 4 /* expected outputs */);
@@ -199,19 +196,15 @@
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << CLTargetInfo::TargetType
- << " Data Type: " << input0->info()->data_type()
- << " Input0 shape: " << input0->info()->tensor_shape()
- << " Input1 shape: " << input1->info()->tensor_shape()
+ << node.name() << " Type: " << node.type() << " Target: " << CLTargetInfo::TargetType
+ << " Data Type: " << input0->info()->data_type() << " Input0 shape: "
+ << input0->info()->tensor_shape() << " Input1 shape: " << input1->info()->tensor_shape()
<< " Input2 shape: " << input2->info()->tensor_shape()
<< " Output0 shape: " << output0->info()->tensor_shape()
<< " Output1 shape: " << output1->info()->tensor_shape()
<< " Output2 shape: " << output2->info()->tensor_shape()
<< " Output3 shape: " << output3->info()->tensor_shape()
- << " DetectionPostProcessLayer info: " << detect_info
- << std::endl);
+ << " DetectionPostProcessLayer info: " << detect_info << std::endl);
auto wrap_function = std::make_unique<CPPWrapperFunction>();
@@ -230,92 +223,128 @@
std::unique_ptr<IFunction> CLFunctionFactory::create(INode *node, GraphContext &ctx)
{
- if(node == nullptr)
+ if (node == nullptr)
{
return nullptr;
}
NodeType type = node->type();
- switch(type)
+ switch (type)
{
case NodeType::ActivationLayer:
- return detail::create_activation_layer<CLActivationLayer, CLTargetInfo>(*polymorphic_downcast<ActivationLayerNode *>(node));
+ return detail::create_activation_layer<CLActivationLayer, CLTargetInfo>(
+ *polymorphic_downcast<ActivationLayerNode *>(node));
case NodeType::ArgMinMaxLayer:
- return detail::create_arg_min_max_layer<CLArgMinMaxLayer, CLTargetInfo>(*polymorphic_downcast<ArgMinMaxLayerNode *>(node));
+ return detail::create_arg_min_max_layer<CLArgMinMaxLayer, CLTargetInfo>(
+ *polymorphic_downcast<ArgMinMaxLayerNode *>(node));
case NodeType::BatchNormalizationLayer:
- return detail::create_batch_normalization_layer<CLBatchNormalizationLayer, CLTargetInfo>(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+ return detail::create_batch_normalization_layer<CLBatchNormalizationLayer, CLTargetInfo>(
+ *polymorphic_downcast<BatchNormalizationLayerNode *>(node));
case NodeType::BoundingBoxTransformLayer:
- return detail::create_bounding_box_transform_layer<CLBoundingBoxTransform, CLTargetInfo>(*polymorphic_downcast<BoundingBoxTransformLayerNode *>(node));
+ return detail::create_bounding_box_transform_layer<CLBoundingBoxTransform, CLTargetInfo>(
+ *polymorphic_downcast<BoundingBoxTransformLayerNode *>(node));
case NodeType::ChannelShuffleLayer:
- return detail::create_channel_shuffle_layer<CLChannelShuffleLayer, CLTargetInfo>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
+ return detail::create_channel_shuffle_layer<CLChannelShuffleLayer, CLTargetInfo>(
+ *polymorphic_downcast<ChannelShuffleLayerNode *>(node));
case NodeType::ConvolutionLayer:
- return detail::create_convolution_layer<CLConvolutionLayerFunctions, CLTargetInfo>(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+ return detail::create_convolution_layer<CLConvolutionLayerFunctions, CLTargetInfo>(
+ *polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
case NodeType::DeconvolutionLayer:
- return detail::create_deconvolution_layer<CLDeconvolutionLayer, CLTargetInfo>(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
+ return detail::create_deconvolution_layer<CLDeconvolutionLayer, CLTargetInfo>(
+ *polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
case NodeType::ConcatenateLayer:
- return detail::create_concatenate_layer<CLConcatenateLayer, CLTargetInfo>(*polymorphic_downcast<ConcatenateLayerNode *>(node));
+ return detail::create_concatenate_layer<CLConcatenateLayer, CLTargetInfo>(
+ *polymorphic_downcast<ConcatenateLayerNode *>(node));
case NodeType::DepthToSpaceLayer:
- return detail::create_depth_to_space_layer<CLDepthToSpaceLayer, CLTargetInfo>(*polymorphic_downcast<DepthToSpaceLayerNode *>(node));
+ return detail::create_depth_to_space_layer<CLDepthToSpaceLayer, CLTargetInfo>(
+ *polymorphic_downcast<DepthToSpaceLayerNode *>(node));
case NodeType::DepthwiseConvolutionLayer:
- return detail::create_depthwise_convolution_layer<CLDepthwiseConvolutionLayer, CLTargetInfo>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ return detail::create_depthwise_convolution_layer<CLDepthwiseConvolutionLayer, CLTargetInfo>(
+ *polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
case NodeType::DequantizationLayer:
- return detail::create_dequantization_layer<CLDequantizationLayer, CLTargetInfo>(*polymorphic_downcast<DequantizationLayerNode *>(node));
+ return detail::create_dequantization_layer<CLDequantizationLayer, CLTargetInfo>(
+ *polymorphic_downcast<DequantizationLayerNode *>(node));
case NodeType::DetectionOutputLayer:
- return detail::create_detection_output_layer<CPPDetectionOutputLayer, CLTargetInfo>(*polymorphic_downcast<DetectionOutputLayerNode *>(node));
+ return detail::create_detection_output_layer<CPPDetectionOutputLayer, CLTargetInfo>(
+ *polymorphic_downcast<DetectionOutputLayerNode *>(node));
case NodeType::DetectionPostProcessLayer:
- return detail::create_detection_post_process_layer<CPPDetectionPostProcessLayer, CLTargetInfo>(*polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
+ return detail::create_detection_post_process_layer<CPPDetectionPostProcessLayer, CLTargetInfo>(
+ *polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
case NodeType::EltwiseLayer:
- return detail::create_eltwise_layer<CLEltwiseFunctions, CLTargetInfo>(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ return detail::create_eltwise_layer<CLEltwiseFunctions, CLTargetInfo>(
+ *polymorphic_downcast<EltwiseLayerNode *>(node));
case NodeType::UnaryEltwiseLayer:
- return detail::create_unary_eltwise_layer<CLUnaryEltwiseFunctions, CLTargetInfo>(*polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
+ return detail::create_unary_eltwise_layer<CLUnaryEltwiseFunctions, CLTargetInfo>(
+ *polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
case NodeType::FlattenLayer:
- return detail::create_flatten_layer<CLFlattenLayer, CLTargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
+ return detail::create_flatten_layer<CLFlattenLayer, CLTargetInfo>(
+ *polymorphic_downcast<FlattenLayerNode *>(node));
case NodeType::FullyConnectedLayer:
- return detail::create_fully_connected_layer<CLFullyConnectedLayer, CLTargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+ return detail::create_fully_connected_layer<CLFullyConnectedLayer, CLTargetInfo>(
+ *polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
case NodeType::FusedConvolutionBatchNormalizationLayer:
- return detail::create_fused_convolution_batch_normalization_layer<CLFusedLayerTypes, CLTargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node), ctx);
+ return detail::create_fused_convolution_batch_normalization_layer<CLFusedLayerTypes, CLTargetInfo>(
+ *polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node), ctx);
case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer:
- return detail::create_fused_depthwise_convolution_batch_normalization_layer<CLFusedLayerTypes, CLTargetInfo>(*polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node), ctx);
+ return detail::create_fused_depthwise_convolution_batch_normalization_layer<CLFusedLayerTypes,
+ CLTargetInfo>(
+ *polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node), ctx);
case NodeType::GenerateProposalsLayer:
- return detail::create_generate_proposals_layer<CLGenerateProposalsLayer, CLTargetInfo>(*polymorphic_downcast<GenerateProposalsLayerNode *>(node), ctx);
+ return detail::create_generate_proposals_layer<CLGenerateProposalsLayer, CLTargetInfo>(
+ *polymorphic_downcast<GenerateProposalsLayerNode *>(node), ctx);
case NodeType::L2NormalizeLayer:
- return detail::create_l2_normalize_layer<CLL2NormalizeLayer, CLTargetInfo>(*polymorphic_downcast<L2NormalizeLayerNode *>(node), ctx);
+ return detail::create_l2_normalize_layer<CLL2NormalizeLayer, CLTargetInfo>(
+ *polymorphic_downcast<L2NormalizeLayerNode *>(node), ctx);
case NodeType::NormalizationLayer:
- return detail::create_normalization_layer<CLNormalizationLayer, CLTargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
+ return detail::create_normalization_layer<CLNormalizationLayer, CLTargetInfo>(
+ *polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
case NodeType::NormalizePlanarYUVLayer:
- return detail::create_normalize_planar_yuv_layer<CLNormalizePlanarYUVLayer, CLTargetInfo>(*polymorphic_downcast<NormalizePlanarYUVLayerNode *>(node));
+ return detail::create_normalize_planar_yuv_layer<CLNormalizePlanarYUVLayer, CLTargetInfo>(
+ *polymorphic_downcast<NormalizePlanarYUVLayerNode *>(node));
case NodeType::PadLayer:
return detail::create_pad_layer<CLPadLayer, CLTargetInfo>(*polymorphic_downcast<PadLayerNode *>(node));
case NodeType::PermuteLayer:
- return detail::create_permute_layer<CLPermute, CLTargetInfo>(*polymorphic_downcast<PermuteLayerNode *>(node));
+ return detail::create_permute_layer<CLPermute, CLTargetInfo>(
+ *polymorphic_downcast<PermuteLayerNode *>(node));
case NodeType::PoolingLayer:
- return detail::create_pooling_layer<CLPoolingLayer, CLTargetInfo>(*polymorphic_downcast<PoolingLayerNode *>(node));
+ return detail::create_pooling_layer<CLPoolingLayer, CLTargetInfo>(
+ *polymorphic_downcast<PoolingLayerNode *>(node));
case NodeType::PReluLayer:
- return detail::create_prelu_layer<CLPReluLayer, CLTargetInfo>(*polymorphic_downcast<PReluLayerNode *>(node));
+ return detail::create_prelu_layer<CLPReluLayer, CLTargetInfo>(
+ *polymorphic_downcast<PReluLayerNode *>(node));
case NodeType::PrintLayer:
return detail::create_print_layer<CLTargetInfo>(*polymorphic_downcast<PrintLayerNode *>(node));
case NodeType::PriorBoxLayer:
- return detail::create_priorbox_layer<CLPriorBoxLayer, CLTargetInfo>(*polymorphic_downcast<PriorBoxLayerNode *>(node));
+ return detail::create_priorbox_layer<CLPriorBoxLayer, CLTargetInfo>(
+ *polymorphic_downcast<PriorBoxLayerNode *>(node));
case NodeType::QuantizationLayer:
- return detail::create_quantization_layer<CLQuantizationLayer, CLTargetInfo>(*polymorphic_downcast<QuantizationLayerNode *>(node));
+ return detail::create_quantization_layer<CLQuantizationLayer, CLTargetInfo>(
+ *polymorphic_downcast<QuantizationLayerNode *>(node));
case NodeType::ReductionOperationLayer:
- return detail::create_reduction_operation_layer<CLReductionOperation, CLTargetInfo>(*polymorphic_downcast<ReductionLayerNode *>(node), ctx);
+ return detail::create_reduction_operation_layer<CLReductionOperation, CLTargetInfo>(
+ *polymorphic_downcast<ReductionLayerNode *>(node), ctx);
case NodeType::ReorgLayer:
- return detail::create_reorg_layer<CLReorgLayer, CLTargetInfo>(*polymorphic_downcast<ReorgLayerNode *>(node));
+ return detail::create_reorg_layer<CLReorgLayer, CLTargetInfo>(
+ *polymorphic_downcast<ReorgLayerNode *>(node));
case NodeType::ReshapeLayer:
- return detail::create_reshape_layer<CLReshapeLayer, CLTargetInfo>(*polymorphic_downcast<ReshapeLayerNode *>(node));
+ return detail::create_reshape_layer<CLReshapeLayer, CLTargetInfo>(
+ *polymorphic_downcast<ReshapeLayerNode *>(node));
case NodeType::ResizeLayer:
return detail::create_resize_layer<CLScale, CLTargetInfo>(*polymorphic_downcast<ResizeLayerNode *>(node));
case NodeType::ROIAlignLayer:
- return detail::create_roi_align_layer<CLROIAlignLayer, CLTargetInfo>(*polymorphic_downcast<ROIAlignLayerNode *>(node));
+ return detail::create_roi_align_layer<CLROIAlignLayer, CLTargetInfo>(
+ *polymorphic_downcast<ROIAlignLayerNode *>(node));
case NodeType::SliceLayer:
return detail::create_slice_layer<CLSlice, CLTargetInfo>(*polymorphic_downcast<SliceLayerNode *>(node));
case NodeType::SoftmaxLayer:
- return detail::create_softmax_layer<CLSoftmaxLayer, CLTargetInfo>(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+ return detail::create_softmax_layer<CLSoftmaxLayer, CLTargetInfo>(
+ *polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
case NodeType::StackLayer:
- return detail::create_stack_layer<CLStackLayer, CLTargetInfo>(*polymorphic_downcast<StackLayerNode *>(node));
+ return detail::create_stack_layer<CLStackLayer, CLTargetInfo>(
+ *polymorphic_downcast<StackLayerNode *>(node));
case NodeType::StridedSliceLayer:
- return detail::create_strided_slice_layer<CLStridedSlice, CLTargetInfo>(*polymorphic_downcast<StridedSliceLayerNode *>(node));
+ return detail::create_strided_slice_layer<CLStridedSlice, CLTargetInfo>(
+ *polymorphic_downcast<StridedSliceLayerNode *>(node));
default:
return nullptr;
}
diff --git a/src/graph/backends/CL/CLNodeValidator.cpp b/src/graph/backends/CL/CLNodeValidator.cpp
index 8fd8c14..510eda7 100644
--- a/src/graph/backends/CL/CLNodeValidator.cpp
+++ b/src/graph/backends/CL/CLNodeValidator.cpp
@@ -25,7 +25,6 @@
#include "arm_compute/graph/backends/ValidateHelpers.h"
#include "arm_compute/graph/nodes/Nodes.h"
-
#include "arm_compute/runtime/CL/CLFunctions.h"
#include "arm_compute/runtime/CPP/CPPFunctions.h"
@@ -57,41 +56,51 @@
Status CLNodeValidator::validate(INode *node)
{
- if(node == nullptr)
+ if (node == nullptr)
{
return Status{};
}
NodeType type = node->type();
- switch(type)
+ switch (type)
{
case NodeType::ArgMinMaxLayer:
- return detail::validate_arg_min_max_layer<CLArgMinMaxLayer>(*polymorphic_downcast<ArgMinMaxLayerNode *>(node));
+ return detail::validate_arg_min_max_layer<CLArgMinMaxLayer>(
+ *polymorphic_downcast<ArgMinMaxLayerNode *>(node));
case NodeType::BoundingBoxTransformLayer:
- return detail::validate_bounding_box_transform_layer<CLBoundingBoxTransform>(*polymorphic_downcast<BoundingBoxTransformLayerNode *>(node));
+ return detail::validate_bounding_box_transform_layer<CLBoundingBoxTransform>(
+ *polymorphic_downcast<BoundingBoxTransformLayerNode *>(node));
case NodeType::ChannelShuffleLayer:
- return detail::validate_channel_shuffle_layer<CLChannelShuffleLayer>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
+ return detail::validate_channel_shuffle_layer<CLChannelShuffleLayer>(
+ *polymorphic_downcast<ChannelShuffleLayerNode *>(node));
case NodeType::ConvolutionLayer:
- return detail::validate_convolution_layer<CLConvolutionLayer,
- CLDirectConvolutionLayer,
- CLGEMMConvolutionLayer,
- CLWinogradConvolutionLayer>(*polymorphic_downcast<ConvolutionLayerNode *>(node));
+ return detail::validate_convolution_layer<CLConvolutionLayer, CLDirectConvolutionLayer,
+ CLGEMMConvolutionLayer, CLWinogradConvolutionLayer>(
+ *polymorphic_downcast<ConvolutionLayerNode *>(node));
case NodeType::DepthToSpaceLayer:
- return detail::validate_depth_to_space_layer<CLDepthToSpaceLayer>(*polymorphic_downcast<DepthToSpaceLayerNode *>(node));
+ return detail::validate_depth_to_space_layer<CLDepthToSpaceLayer>(
+ *polymorphic_downcast<DepthToSpaceLayerNode *>(node));
case NodeType::DepthwiseConvolutionLayer:
- return detail::validate_depthwise_convolution_layer<CLDepthwiseConvolutionLayer>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ return detail::validate_depthwise_convolution_layer<CLDepthwiseConvolutionLayer>(
+ *polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
case NodeType::DequantizationLayer:
- return detail::validate_dequantization_layer<CLDequantizationLayer>(*polymorphic_downcast<DequantizationLayerNode *>(node));
+ return detail::validate_dequantization_layer<CLDequantizationLayer>(
+ *polymorphic_downcast<DequantizationLayerNode *>(node));
case NodeType::DetectionOutputLayer:
- return detail::validate_detection_output_layer<CPPDetectionOutputLayer>(*polymorphic_downcast<DetectionOutputLayerNode *>(node));
+ return detail::validate_detection_output_layer<CPPDetectionOutputLayer>(
+ *polymorphic_downcast<DetectionOutputLayerNode *>(node));
case NodeType::DetectionPostProcessLayer:
- return detail::validate_detection_post_process_layer<CPPDetectionPostProcessLayer>(*polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
+ return detail::validate_detection_post_process_layer<CPPDetectionPostProcessLayer>(
+ *polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
case NodeType::GenerateProposalsLayer:
- return detail::validate_generate_proposals_layer<CLGenerateProposalsLayer>(*polymorphic_downcast<GenerateProposalsLayerNode *>(node));
+ return detail::validate_generate_proposals_layer<CLGenerateProposalsLayer>(
+ *polymorphic_downcast<GenerateProposalsLayerNode *>(node));
case NodeType::L2NormalizeLayer:
- return detail::validate_l2_normalize_layer<CLL2NormalizeLayer>(*polymorphic_downcast<L2NormalizeLayerNode *>(node));
+ return detail::validate_l2_normalize_layer<CLL2NormalizeLayer>(
+ *polymorphic_downcast<L2NormalizeLayerNode *>(node));
case NodeType::NormalizePlanarYUVLayer:
- return detail::validate_normalize_planar_yuv_layer<CLNormalizePlanarYUVLayer>(*polymorphic_downcast<NormalizePlanarYUVLayerNode *>(node));
+ return detail::validate_normalize_planar_yuv_layer<CLNormalizePlanarYUVLayer>(
+ *polymorphic_downcast<NormalizePlanarYUVLayerNode *>(node));
case NodeType::PadLayer:
return detail::validate_pad_layer<CLPadLayer>(*polymorphic_downcast<PadLayerNode *>(node));
case NodeType::PermuteLayer:
@@ -101,9 +110,11 @@
case NodeType::PriorBoxLayer:
return detail::validate_priorbox_layer<CLPriorBoxLayer>(*polymorphic_downcast<PriorBoxLayerNode *>(node));
case NodeType::QuantizationLayer:
- return detail::validate_quantization_layer<CLQuantizationLayer>(*polymorphic_downcast<QuantizationLayerNode *>(node));
+ return detail::validate_quantization_layer<CLQuantizationLayer>(
+ *polymorphic_downcast<QuantizationLayerNode *>(node));
case NodeType::ReductionOperationLayer:
- return detail::validate_reduction_operation_layer<CLReductionOperation>(*polymorphic_downcast<ReductionLayerNode *>(node));
+ return detail::validate_reduction_operation_layer<CLReductionOperation>(
+ *polymorphic_downcast<ReductionLayerNode *>(node));
case NodeType::ReorgLayer:
return detail::validate_reorg_layer<CLReorgLayer>(*polymorphic_downcast<ReorgLayerNode *>(node));
case NodeType::ReshapeLayer:
@@ -113,11 +124,14 @@
case NodeType::SliceLayer:
return detail::validate_slice_layer<CLSlice>(*polymorphic_downcast<SliceLayerNode *>(node));
case NodeType::StridedSliceLayer:
- return detail::validate_strided_slice_layer<CLStridedSlice>(*polymorphic_downcast<StridedSliceLayerNode *>(node));
+ return detail::validate_strided_slice_layer<CLStridedSlice>(
+ *polymorphic_downcast<StridedSliceLayerNode *>(node));
case NodeType::EltwiseLayer:
- return detail::validate_eltwise_Layer<CLEltwiseLayerFunctions>(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ return detail::validate_eltwise_Layer<CLEltwiseLayerFunctions>(
+ *polymorphic_downcast<EltwiseLayerNode *>(node));
case NodeType::UnaryEltwiseLayer:
- return detail::validate_unary_eltwise_layer<CLUnaryEltwiseLayerFunctions>(*polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
+ return detail::validate_unary_eltwise_layer<CLUnaryEltwiseLayerFunctions>(
+ *polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
default:
return Status{};
}
diff --git a/src/graph/backends/CL/CLSubTensorHandle.cpp b/src/graph/backends/CL/CLSubTensorHandle.cpp
index b97d258..ccdc877 100644
--- a/src/graph/backends/CL/CLSubTensorHandle.cpp
+++ b/src/graph/backends/CL/CLSubTensorHandle.cpp
@@ -31,7 +31,10 @@
{
namespace backends
{
-CLSubTensorHandle::CLSubTensorHandle(ITensorHandle *parent_handle, const TensorShape &shape, const Coordinates &coords, bool extend_parent)
+CLSubTensorHandle::CLSubTensorHandle(ITensorHandle *parent_handle,
+ const TensorShape &shape,
+ const Coordinates &coords,
+ bool extend_parent)
: _sub_tensor(), _parent_handle(nullptr)
{
ARM_COMPUTE_ERROR_ON(!parent_handle);
@@ -98,4 +101,4 @@
}
} // namespace backends
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/backends/CL/CLTensorHandle.cpp b/src/graph/backends/CL/CLTensorHandle.cpp
index a496c2c..1b69f9d 100644
--- a/src/graph/backends/CL/CLTensorHandle.cpp
+++ b/src/graph/backends/CL/CLTensorHandle.cpp
@@ -31,8 +31,7 @@
{
namespace backends
{
-CLTensorHandle::CLTensorHandle(const ITensorInfo &info)
- : _tensor()
+CLTensorHandle::CLTensorHandle(const ITensorInfo &info) : _tensor()
{
_tensor.allocator()->init(info);
}
@@ -49,7 +48,7 @@
void CLTensorHandle::manage(IMemoryGroup *mg)
{
- if(mg != nullptr)
+ if (mg != nullptr)
{
mg->manage(&_tensor);
}
@@ -68,7 +67,7 @@
void CLTensorHandle::release_if_unused()
{
// TODO (geopin01): Release tensor only if all sub-tensors are marked as not used
- if(!_tensor.is_used())
+ if (!_tensor.is_used())
{
_tensor.allocator()->free();
}
@@ -100,4 +99,4 @@
}
} // namespace backends
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/backends/NEON/NEDeviceBackend.cpp b/src/graph/backends/NEON/NEDeviceBackend.cpp
index 1845653..fc7b309 100644
--- a/src/graph/backends/NEON/NEDeviceBackend.cpp
+++ b/src/graph/backends/NEON/NEDeviceBackend.cpp
@@ -23,18 +23,17 @@
*/
#include "arm_compute/graph/backends/NEON/NEDeviceBackend.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/GraphContext.h"
-#include "arm_compute/graph/INode.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Tensor.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/graph/backends/BackendRegistrar.h"
#include "arm_compute/graph/backends/NEON/NEFunctionFactory.h"
#include "arm_compute/graph/backends/NEON/NENodeValidator.h"
#include "arm_compute/graph/backends/NEON/NESubTensorHandle.h"
#include "arm_compute/graph/backends/NEON/NETensorHandle.h"
-
-#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/GraphContext.h"
+#include "arm_compute/graph/INode.h"
+#include "arm_compute/graph/Logger.h"
+#include "arm_compute/graph/Tensor.h"
#include "arm_compute/runtime/Allocator.h"
#include "arm_compute/runtime/BlobLifetimeManager.h"
#include "arm_compute/runtime/IWeightsManager.h"
@@ -53,8 +52,7 @@
/** Register CPU backend */
static detail::BackendRegistrar<NEDeviceBackend> NEDeviceBackend_registrar(Target::NEON);
-NEDeviceBackend::NEDeviceBackend()
- : _allocator()
+NEDeviceBackend::NEDeviceBackend() : _allocator()
{
}
@@ -72,13 +70,13 @@
void NEDeviceBackend::setup_backend_context(GraphContext &ctx)
{
// Set number of threads
- if(ctx.config().num_threads >= 0)
+ if (ctx.config().num_threads >= 0)
{
Scheduler::get().set_num_threads(ctx.config().num_threads);
}
// Create function level memory manager
- if(ctx.memory_management_ctx(Target::NEON) == nullptr)
+ if (ctx.memory_management_ctx(Target::NEON) == nullptr)
{
MemoryManagerContext mm_ctx;
mm_ctx.target = Target::NEON;
@@ -91,7 +89,7 @@
}
// Create function level weights manager
- if(ctx.weights_management_ctx(Target::NEON) == nullptr)
+ if (ctx.weights_management_ctx(Target::NEON) == nullptr)
{
WeightsManagerContext wm_ctx;
wm_ctx.target = Target::NEON;
@@ -124,9 +122,10 @@
return std::make_unique<NETensorHandle>(info);
}
-std::unique_ptr<ITensorHandle> NEDeviceBackend::create_subtensor(ITensorHandle *parent, TensorShape shape, Coordinates coords, bool extend_parent)
+std::unique_ptr<ITensorHandle>
+NEDeviceBackend::create_subtensor(ITensorHandle *parent, TensorShape shape, Coordinates coords, bool extend_parent)
{
- if(parent == nullptr)
+ if (parent == nullptr)
{
return nullptr;
}
@@ -154,7 +153,7 @@
std::shared_ptr<arm_compute::IMemoryManager> NEDeviceBackend::create_memory_manager(MemoryManagerAffinity affinity)
{
std::shared_ptr<ILifetimeManager> lifetime_mgr = nullptr;
- if(affinity == MemoryManagerAffinity::Buffer)
+ if (affinity == MemoryManagerAffinity::Buffer)
{
lifetime_mgr = std::make_shared<BlobLifetimeManager>();
}
diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp
index d7ed5f9..fe15d4c 100644
--- a/src/graph/backends/NEON/NEFunctionFactory.cpp
+++ b/src/graph/backends/NEON/NEFunctionFactory.cpp
@@ -23,13 +23,13 @@
*/
#include "arm_compute/graph/backends/NEON/NEFunctionFactory.h"
+#include "arm_compute/graph/backends/FunctionHelpers.h"
+#include "arm_compute/graph/backends/Utils.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/TypePrinter.h"
-#include "arm_compute/graph/backends/FunctionHelpers.h"
-#include "arm_compute/graph/backends/Utils.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/runtime/CPP/CPPFunctions.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
@@ -88,7 +88,8 @@
namespace detail
{
template <>
-std::unique_ptr<IFunction> create_normalization_layer<NENormalizationLayer, NETargetInfo>(NormalizationLayerNode &node, GraphContext &ctx)
+std::unique_ptr<IFunction> create_normalization_layer<NENormalizationLayer, NETargetInfo>(NormalizationLayerNode &node,
+ GraphContext &ctx)
{
validate_node<NETargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */);
@@ -105,14 +106,10 @@
// Log info
ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
- << node.name()
- << " Type: " << node.type()
- << " Target: " << NETargetInfo::TargetType
- << " Data Type: " << input->info()->data_type()
- << " Input shape: " << input->info()->tensor_shape()
- << " Output shape: " << output->info()->tensor_shape()
- << " Normalization info: " << norm_info.type()
- << std::endl);
+ << node.name() << " Type: " << node.type() << " Target: " << NETargetInfo::TargetType
+ << " Data Type: " << input->info()->data_type() << " Input shape: "
+ << input->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape()
+ << " Normalization info: " << norm_info.type() << std::endl);
return func;
}
@@ -120,84 +117,116 @@
std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &ctx)
{
- if(node == nullptr)
+ if (node == nullptr)
{
return nullptr;
}
NodeType type = node->type();
- switch(type)
+ switch (type)
{
case NodeType::ActivationLayer:
- return detail::create_activation_layer<NEActivationLayer, NETargetInfo>(*polymorphic_downcast<ActivationLayerNode *>(node));
+ return detail::create_activation_layer<NEActivationLayer, NETargetInfo>(
+ *polymorphic_downcast<ActivationLayerNode *>(node));
case NodeType::ArgMinMaxLayer:
- return detail::create_arg_min_max_layer<NEArgMinMaxLayer, NETargetInfo>(*polymorphic_downcast<ArgMinMaxLayerNode *>(node));
+ return detail::create_arg_min_max_layer<NEArgMinMaxLayer, NETargetInfo>(
+ *polymorphic_downcast<ArgMinMaxLayerNode *>(node));
case NodeType::BatchNormalizationLayer:
- return detail::create_batch_normalization_layer<NEBatchNormalizationLayer, NETargetInfo>(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
+ return detail::create_batch_normalization_layer<NEBatchNormalizationLayer, NETargetInfo>(
+ *polymorphic_downcast<BatchNormalizationLayerNode *>(node));
case NodeType::ChannelShuffleLayer:
- return detail::create_channel_shuffle_layer<NEChannelShuffleLayer, NETargetInfo>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
+ return detail::create_channel_shuffle_layer<NEChannelShuffleLayer, NETargetInfo>(
+ *polymorphic_downcast<ChannelShuffleLayerNode *>(node));
case NodeType::ConvolutionLayer:
- return detail::create_convolution_layer<NEConvolutionLayerFunctions, NETargetInfo>(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
+ return detail::create_convolution_layer<NEConvolutionLayerFunctions, NETargetInfo>(
+ *polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
case NodeType::DepthToSpaceLayer:
- return detail::create_depth_to_space_layer<NEDepthToSpaceLayer, NETargetInfo>(*polymorphic_downcast<DepthToSpaceLayerNode *>(node));
+ return detail::create_depth_to_space_layer<NEDepthToSpaceLayer, NETargetInfo>(
+ *polymorphic_downcast<DepthToSpaceLayerNode *>(node));
case NodeType::DeconvolutionLayer:
- return detail::create_deconvolution_layer<NEDeconvolutionLayer, NETargetInfo>(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
+ return detail::create_deconvolution_layer<NEDeconvolutionLayer, NETargetInfo>(
+ *polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
case NodeType::ConcatenateLayer:
- return detail::create_concatenate_layer<NEConcatenateLayer, NETargetInfo>(*polymorphic_downcast<ConcatenateLayerNode *>(node));
+ return detail::create_concatenate_layer<NEConcatenateLayer, NETargetInfo>(
+ *polymorphic_downcast<ConcatenateLayerNode *>(node));
case NodeType::DepthwiseConvolutionLayer:
- return detail::create_depthwise_convolution_layer<NEDepthwiseConvolutionLayer, NETargetInfo>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ return detail::create_depthwise_convolution_layer<NEDepthwiseConvolutionLayer, NETargetInfo>(
+ *polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
case NodeType::DequantizationLayer:
- return detail::create_dequantization_layer<NEDequantizationLayer, NETargetInfo>(*polymorphic_downcast<DequantizationLayerNode *>(node));
+ return detail::create_dequantization_layer<NEDequantizationLayer, NETargetInfo>(
+ *polymorphic_downcast<DequantizationLayerNode *>(node));
case NodeType::DetectionOutputLayer:
- return detail::create_detection_output_layer<CPPDetectionOutputLayer, NETargetInfo>(*polymorphic_downcast<DetectionOutputLayerNode *>(node));
+ return detail::create_detection_output_layer<CPPDetectionOutputLayer, NETargetInfo>(
+ *polymorphic_downcast<DetectionOutputLayerNode *>(node));
case NodeType::DetectionPostProcessLayer:
- return detail::create_detection_post_process_layer<NEDetectionPostProcessLayer, NETargetInfo>(*polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
+ return detail::create_detection_post_process_layer<NEDetectionPostProcessLayer, NETargetInfo>(
+ *polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
case NodeType::EltwiseLayer:
- return detail::create_eltwise_layer<NEEltwiseFunctions, NETargetInfo>(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ return detail::create_eltwise_layer<NEEltwiseFunctions, NETargetInfo>(
+ *polymorphic_downcast<EltwiseLayerNode *>(node));
case NodeType::UnaryEltwiseLayer:
- return detail::create_unary_eltwise_layer<NEUnaryEltwiseFunctions, NETargetInfo>(*polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
+ return detail::create_unary_eltwise_layer<NEUnaryEltwiseFunctions, NETargetInfo>(
+ *polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
case NodeType::FlattenLayer:
- return detail::create_flatten_layer<NEFlattenLayer, NETargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
+ return detail::create_flatten_layer<NEFlattenLayer, NETargetInfo>(
+ *polymorphic_downcast<FlattenLayerNode *>(node));
case NodeType::FullyConnectedLayer:
- return detail::create_fully_connected_layer<NEFullyConnectedLayer, NETargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
+ return detail::create_fully_connected_layer<NEFullyConnectedLayer, NETargetInfo>(
+ *polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
case NodeType::FusedConvolutionBatchNormalizationLayer:
- return detail::create_fused_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node), ctx);
+ return detail::create_fused_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(
+ *polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node), ctx);
case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer:
- return detail::create_fused_depthwise_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node), ctx);
+ return detail::create_fused_depthwise_convolution_batch_normalization_layer<NEFusedLayerTypes,
+ NETargetInfo>(
+ *polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node), ctx);
case NodeType::L2NormalizeLayer:
- return detail::create_l2_normalize_layer<NEL2NormalizeLayer, NETargetInfo>(*polymorphic_downcast<L2NormalizeLayerNode *>(node), ctx);
+ return detail::create_l2_normalize_layer<NEL2NormalizeLayer, NETargetInfo>(
+ *polymorphic_downcast<L2NormalizeLayerNode *>(node), ctx);
case NodeType::NormalizationLayer:
- return detail::create_normalization_layer<NENormalizationLayer, NETargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
+ return detail::create_normalization_layer<NENormalizationLayer, NETargetInfo>(
+ *polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
case NodeType::PadLayer:
return detail::create_pad_layer<NEPadLayer, NETargetInfo>(*polymorphic_downcast<PadLayerNode *>(node));
case NodeType::PermuteLayer:
- return detail::create_permute_layer<NEPermute, NETargetInfo>(*polymorphic_downcast<PermuteLayerNode *>(node));
+ return detail::create_permute_layer<NEPermute, NETargetInfo>(
+ *polymorphic_downcast<PermuteLayerNode *>(node));
case NodeType::PoolingLayer:
- return detail::create_pooling_layer<NEPoolingLayer, NETargetInfo>(*polymorphic_downcast<PoolingLayerNode *>(node));
+ return detail::create_pooling_layer<NEPoolingLayer, NETargetInfo>(
+ *polymorphic_downcast<PoolingLayerNode *>(node));
case NodeType::PReluLayer:
- return detail::create_prelu_layer<NEPReluLayer, NETargetInfo>(*polymorphic_downcast<PReluLayerNode *>(node));
+ return detail::create_prelu_layer<NEPReluLayer, NETargetInfo>(
+ *polymorphic_downcast<PReluLayerNode *>(node));
case NodeType::PrintLayer:
return detail::create_print_layer<NETargetInfo>(*polymorphic_downcast<PrintLayerNode *>(node));
case NodeType::PriorBoxLayer:
- return detail::create_priorbox_layer<NEPriorBoxLayer, NETargetInfo>(*polymorphic_downcast<PriorBoxLayerNode *>(node));
+ return detail::create_priorbox_layer<NEPriorBoxLayer, NETargetInfo>(
+ *polymorphic_downcast<PriorBoxLayerNode *>(node));
case NodeType::QuantizationLayer:
- return detail::create_quantization_layer<NEQuantizationLayer, NETargetInfo>(*polymorphic_downcast<QuantizationLayerNode *>(node));
+ return detail::create_quantization_layer<NEQuantizationLayer, NETargetInfo>(
+ *polymorphic_downcast<QuantizationLayerNode *>(node));
case NodeType::ReductionOperationLayer:
- return detail::create_reduction_operation_layer<NEReductionOperation, NETargetInfo>(*polymorphic_downcast<ReductionLayerNode *>(node), ctx);
+ return detail::create_reduction_operation_layer<NEReductionOperation, NETargetInfo>(
+ *polymorphic_downcast<ReductionLayerNode *>(node), ctx);
case NodeType::ReorgLayer:
- return detail::create_reorg_layer<NEReorgLayer, NETargetInfo>(*polymorphic_downcast<ReorgLayerNode *>(node));
+ return detail::create_reorg_layer<NEReorgLayer, NETargetInfo>(
+ *polymorphic_downcast<ReorgLayerNode *>(node));
case NodeType::ReshapeLayer:
- return detail::create_reshape_layer<NEReshapeLayer, NETargetInfo>(*polymorphic_downcast<ReshapeLayerNode *>(node));
+ return detail::create_reshape_layer<NEReshapeLayer, NETargetInfo>(
+ *polymorphic_downcast<ReshapeLayerNode *>(node));
case NodeType::ResizeLayer:
return detail::create_resize_layer<NEScale, NETargetInfo>(*polymorphic_downcast<ResizeLayerNode *>(node));
case NodeType::SliceLayer:
return detail::create_slice_layer<NESlice, NETargetInfo>(*polymorphic_downcast<SliceLayerNode *>(node));
case NodeType::SoftmaxLayer:
- return detail::create_softmax_layer<NESoftmaxLayer, NETargetInfo>(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
+ return detail::create_softmax_layer<NESoftmaxLayer, NETargetInfo>(
+ *polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
case NodeType::StackLayer:
- return detail::create_stack_layer<NEStackLayer, NETargetInfo>(*polymorphic_downcast<StackLayerNode *>(node));
+ return detail::create_stack_layer<NEStackLayer, NETargetInfo>(
+ *polymorphic_downcast<StackLayerNode *>(node));
case NodeType::StridedSliceLayer:
- return detail::create_strided_slice_layer<NEStridedSlice, NETargetInfo>(*polymorphic_downcast<StridedSliceLayerNode *>(node));
+ return detail::create_strided_slice_layer<NEStridedSlice, NETargetInfo>(
+ *polymorphic_downcast<StridedSliceLayerNode *>(node));
default:
return nullptr;
}
diff --git a/src/graph/backends/NEON/NENodeValidator.cpp b/src/graph/backends/NEON/NENodeValidator.cpp
index a485e5d..a97806f 100644
--- a/src/graph/backends/NEON/NENodeValidator.cpp
+++ b/src/graph/backends/NEON/NENodeValidator.cpp
@@ -25,9 +25,9 @@
#include "arm_compute/graph/backends/ValidateHelpers.h"
#include "arm_compute/graph/nodes/Nodes.h"
-
#include "arm_compute/runtime/CPP/CPPFunctions.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
+
#include "support/Cast.h"
using namespace arm_compute::utils::cast;
@@ -56,41 +56,51 @@
Status NENodeValidator::validate(INode *node)
{
- if(node == nullptr)
+ if (node == nullptr)
{
return Status{};
}
NodeType type = node->type();
- switch(type)
+ switch (type)
{
case NodeType::ArgMinMaxLayer:
- return detail::validate_arg_min_max_layer<NEArgMinMaxLayer>(*polymorphic_downcast<ArgMinMaxLayerNode *>(node));
+ return detail::validate_arg_min_max_layer<NEArgMinMaxLayer>(
+ *polymorphic_downcast<ArgMinMaxLayerNode *>(node));
case NodeType::BoundingBoxTransformLayer:
- return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, "Unsupported operation : BoundingBoxTransformLayer");
+ return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unsupported operation : BoundingBoxTransformLayer");
case NodeType::ChannelShuffleLayer:
- return detail::validate_channel_shuffle_layer<NEChannelShuffleLayer>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
+ return detail::validate_channel_shuffle_layer<NEChannelShuffleLayer>(
+ *polymorphic_downcast<ChannelShuffleLayerNode *>(node));
case NodeType::ConvolutionLayer:
- return detail::validate_convolution_layer<NEConvolutionLayer,
- NEDirectConvolutionLayer,
- NEGEMMConvolutionLayer,
- NEWinogradConvolutionLayer>(*polymorphic_downcast<ConvolutionLayerNode *>(node));
+ return detail::validate_convolution_layer<NEConvolutionLayer, NEDirectConvolutionLayer,
+ NEGEMMConvolutionLayer, NEWinogradConvolutionLayer>(
+ *polymorphic_downcast<ConvolutionLayerNode *>(node));
case NodeType::DepthToSpaceLayer:
- return detail::validate_depth_to_space_layer<NEDepthToSpaceLayer>(*polymorphic_downcast<DepthToSpaceLayerNode *>(node));
+ return detail::validate_depth_to_space_layer<NEDepthToSpaceLayer>(
+ *polymorphic_downcast<DepthToSpaceLayerNode *>(node));
case NodeType::DepthwiseConvolutionLayer:
- return detail::validate_depthwise_convolution_layer<NEDepthwiseConvolutionLayer>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
+ return detail::validate_depthwise_convolution_layer<NEDepthwiseConvolutionLayer>(
+ *polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
case NodeType::DequantizationLayer:
- return detail::validate_dequantization_layer<NEDequantizationLayer>(*polymorphic_downcast<DequantizationLayerNode *>(node));
+ return detail::validate_dequantization_layer<NEDequantizationLayer>(
+ *polymorphic_downcast<DequantizationLayerNode *>(node));
case NodeType::DetectionOutputLayer:
- return detail::validate_detection_output_layer<CPPDetectionOutputLayer>(*polymorphic_downcast<DetectionOutputLayerNode *>(node));
+ return detail::validate_detection_output_layer<CPPDetectionOutputLayer>(
+ *polymorphic_downcast<DetectionOutputLayerNode *>(node));
case NodeType::DetectionPostProcessLayer:
- return detail::validate_detection_post_process_layer<NEDetectionPostProcessLayer>(*polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
+ return detail::validate_detection_post_process_layer<NEDetectionPostProcessLayer>(
+ *polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
case NodeType::GenerateProposalsLayer:
- return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, "Unsupported operation : GenerateProposalsLayer");
+ return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unsupported operation : GenerateProposalsLayer");
case NodeType::L2NormalizeLayer:
- return detail::validate_l2_normalize_layer<NEL2NormalizeLayer>(*polymorphic_downcast<L2NormalizeLayerNode *>(node));
+ return detail::validate_l2_normalize_layer<NEL2NormalizeLayer>(
+ *polymorphic_downcast<L2NormalizeLayerNode *>(node));
case NodeType::NormalizePlanarYUVLayer:
- return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, "Unsupported operation : NormalizePlanarYUVLayer");
+ return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unsupported operation : NormalizePlanarYUVLayer");
case NodeType::PadLayer:
return detail::validate_pad_layer<NEPadLayer>(*polymorphic_downcast<PadLayerNode *>(node));
case NodeType::PermuteLayer:
@@ -100,23 +110,29 @@
case NodeType::PriorBoxLayer:
return detail::validate_priorbox_layer<NEPriorBoxLayer>(*polymorphic_downcast<PriorBoxLayerNode *>(node));
case NodeType::QuantizationLayer:
- return detail::validate_quantization_layer<NEQuantizationLayer>(*polymorphic_downcast<QuantizationLayerNode *>(node));
+ return detail::validate_quantization_layer<NEQuantizationLayer>(
+ *polymorphic_downcast<QuantizationLayerNode *>(node));
case NodeType::ReductionOperationLayer:
- return detail::validate_reduction_operation_layer<NEReductionOperation>(*polymorphic_downcast<ReductionLayerNode *>(node));
+ return detail::validate_reduction_operation_layer<NEReductionOperation>(
+ *polymorphic_downcast<ReductionLayerNode *>(node));
case NodeType::ReorgLayer:
return detail::validate_reorg_layer<NEReorgLayer>(*polymorphic_downcast<ReorgLayerNode *>(node));
case NodeType::ReshapeLayer:
return detail::validate_reshape_layer<NEReshapeLayer>(*polymorphic_downcast<ReshapeLayerNode *>(node));
case NodeType::ROIAlignLayer:
- return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR, "Unsupported operation : ROIAlignLayer");
+ return ARM_COMPUTE_CREATE_ERROR(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unsupported operation : ROIAlignLayer");
case NodeType::SliceLayer:
return detail::validate_slice_layer<NESlice>(*polymorphic_downcast<SliceLayerNode *>(node));
case NodeType::StridedSliceLayer:
- return detail::validate_strided_slice_layer<NEStridedSlice>(*polymorphic_downcast<StridedSliceLayerNode *>(node));
+ return detail::validate_strided_slice_layer<NEStridedSlice>(
+ *polymorphic_downcast<StridedSliceLayerNode *>(node));
case NodeType::EltwiseLayer:
- return detail::validate_eltwise_Layer<NEEltwiseLayerFunctions>(*polymorphic_downcast<EltwiseLayerNode *>(node));
+ return detail::validate_eltwise_Layer<NEEltwiseLayerFunctions>(
+ *polymorphic_downcast<EltwiseLayerNode *>(node));
case NodeType::UnaryEltwiseLayer:
- return detail::validate_unary_eltwise_layer<NEUnaryEltwiseLayerFunctions>(*polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
+ return detail::validate_unary_eltwise_layer<NEUnaryEltwiseLayerFunctions>(
+ *polymorphic_downcast<UnaryEltwiseLayerNode *>(node));
default:
return Status{};
}
diff --git a/src/graph/backends/NEON/NESubTensorHandle.cpp b/src/graph/backends/NEON/NESubTensorHandle.cpp
index 36f29d0..8964a00 100644
--- a/src/graph/backends/NEON/NESubTensorHandle.cpp
+++ b/src/graph/backends/NEON/NESubTensorHandle.cpp
@@ -29,7 +29,10 @@
{
namespace backends
{
-NESubTensorHandle::NESubTensorHandle(ITensorHandle *parent_handle, const TensorShape &shape, const Coordinates &coords, bool extend_parent)
+NESubTensorHandle::NESubTensorHandle(ITensorHandle *parent_handle,
+ const TensorShape &shape,
+ const Coordinates &coords,
+ bool extend_parent)
: _sub_tensor(), _parent_handle(nullptr)
{
ARM_COMPUTE_ERROR_ON(!parent_handle);
@@ -95,4 +98,4 @@
}
} // namespace backends
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/backends/NEON/NETensorHandle.cpp b/src/graph/backends/NEON/NETensorHandle.cpp
index 4393156..dabf670 100644
--- a/src/graph/backends/NEON/NETensorHandle.cpp
+++ b/src/graph/backends/NEON/NETensorHandle.cpp
@@ -24,6 +24,7 @@
#include "arm_compute/graph/backends/NEON/NETensorHandle.h"
#include "arm_compute/runtime/MemoryGroup.h"
+
#include "support/Cast.h"
namespace arm_compute
@@ -32,8 +33,7 @@
{
namespace backends
{
-NETensorHandle::NETensorHandle(const ITensorInfo &info)
- : _tensor()
+NETensorHandle::NETensorHandle(const ITensorInfo &info) : _tensor()
{
_tensor.allocator()->init(info);
}
@@ -50,7 +50,7 @@
void NETensorHandle::manage(IMemoryGroup *mg)
{
- if(mg != nullptr)
+ if (mg != nullptr)
{
mg->manage(&_tensor);
}
@@ -68,7 +68,7 @@
void NETensorHandle::release_if_unused()
{
// TODO (geopin01): Release tensor only if all sub-tensors are marked as not used
- if(!_tensor.is_used())
+ if (!_tensor.is_used())
{
_tensor.allocator()->free();
}
@@ -100,4 +100,4 @@
}
} // namespace backends
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/detail/CrossLayerMemoryManagerHelpers.cpp b/src/graph/detail/CrossLayerMemoryManagerHelpers.cpp
index b45f453..1e813dc 100644
--- a/src/graph/detail/CrossLayerMemoryManagerHelpers.cpp
+++ b/src/graph/detail/CrossLayerMemoryManagerHelpers.cpp
@@ -23,6 +23,8 @@
*/
#include "arm_compute/graph/detail/CrossLayerMemoryManagerHelpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/GraphManager.h"
@@ -30,9 +32,7 @@
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/Types.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/BackendRegistry.h"
-#include "arm_compute/core/ITensor.h"
#include "support/Cast.h"
#include <algorithm>
@@ -78,28 +78,28 @@
*/
std::set<ITensorHandle *> get_const_handles(const Graph &g)
{
- std::set<NodeType> const_node_types = { NodeType::Input, NodeType::Output, NodeType::Const };
+ std::set<NodeType> const_node_types = {NodeType::Input, NodeType::Output, NodeType::Const};
std::set<ITensorHandle *> const_tensors;
auto &nodes = g.nodes();
- for(auto &node : nodes)
+ for (auto &node : nodes)
{
// If its a const node:
- if(node != nullptr && const_node_types.find(node->type()) != std::end(const_node_types))
+ if (node != nullptr && const_node_types.find(node->type()) != std::end(const_node_types))
{
// TODO (geopin01) : Create IO iterator wrappers
// Add all its inputs / outputs to the list of constant handles
- for(unsigned int i = 0; i < node->num_inputs(); ++i)
+ for (unsigned int i = 0; i < node->num_inputs(); ++i)
{
- if(node->input(i) != nullptr)
+ if (node->input(i) != nullptr)
{
const_tensors.insert(node->input(i)->handle()->parent_handle());
}
}
- for(unsigned int i = 0; i < node->num_outputs(); ++i)
+ for (unsigned int i = 0; i < node->num_outputs(); ++i)
{
- if(node->output(i) != nullptr)
+ if (node->output(i) != nullptr)
{
const_tensors.insert(node->output(i)->handle()->parent_handle());
}
@@ -118,9 +118,8 @@
*
* @return List of transition handles
*/
-TaskHandles get_transition_handles(GraphContext &ctx,
- ExecutionTask &task,
- const std::set<ITensorHandle *> &const_tensors)
+TaskHandles
+get_transition_handles(GraphContext &ctx, ExecutionTask &task, const std::set<ITensorHandle *> &const_tensors)
{
ARM_COMPUTE_ERROR_ON(task.node == nullptr || (task.task == nullptr && !is_utility_node(task.node)));
INode &node = *task.node;
@@ -128,28 +127,30 @@
TaskHandles transition_handles;
// Add input handles
- for(unsigned int i = 0; i < node.input_edges().size(); ++i)
+ for (unsigned int i = 0; i < node.input_edges().size(); ++i)
{
Edge *input_edge = node.input_edge(i);
// If this input is the output of another node
- if(input_edge != nullptr && input_edge->tensor() != nullptr && const_tensors.find(input_edge->tensor()->handle()->parent_handle()) == std::end(const_tensors))
+ if (input_edge != nullptr && input_edge->tensor() != nullptr &&
+ const_tensors.find(input_edge->tensor()->handle()->parent_handle()) == std::end(const_tensors))
{
// Then add it to the list of transition buffers
ITensorHandle *tensor_handle = input_edge->tensor()->handle()->parent_handle();
- IMemoryGroup *mm_group = get_memory_group_from_handle(ctx, tensor_handle);
+ IMemoryGroup *mm_group = get_memory_group_from_handle(ctx, tensor_handle);
transition_handles.input_handles.emplace_back(std::make_pair(tensor_handle, mm_group));
}
}
// Add output handles
- for(unsigned int i = 0; i < node.num_outputs(); ++i)
+ for (unsigned int i = 0; i < node.num_outputs(); ++i)
{
Tensor *output_tensor = node.output(i);
// If this output is used as an input for another node
- if(output_tensor != nullptr && const_tensors.find(output_tensor->handle()->parent_handle()) == std::end(const_tensors))
+ if (output_tensor != nullptr &&
+ const_tensors.find(output_tensor->handle()->parent_handle()) == std::end(const_tensors))
{
ITensorHandle *tensor_handle = output_tensor->handle()->parent_handle();
- IMemoryGroup *mm_group = get_memory_group_from_handle(ctx, tensor_handle);
+ IMemoryGroup *mm_group = get_memory_group_from_handle(ctx, tensor_handle);
transition_handles.output_handles.emplace_back(std::make_pair(tensor_handle, mm_group));
}
}
@@ -164,11 +165,11 @@
*/
void count_input_handles_per_target(const TaskHandles &task_handles, TargetHandleCounter &handle_counter)
{
- for(const auto &handle : task_handles.input_handles)
+ for (const auto &handle : task_handles.input_handles)
{
ITensorHandle *key = handle.first;
HandleCounter &target_counter = handle_counter[key->target()];
- if(target_counter.find(key) == std::end(target_counter))
+ if (target_counter.find(key) == std::end(target_counter))
{
target_counter.emplace(std::make_pair(key, 1));
}
@@ -192,12 +193,12 @@
// Acquires the given handles and sets them as in flight if they aren't already
auto acquire = [&](std::vector<std::pair<ITensorHandle *, IMemoryGroup *>> &handles)
{
- for(auto &handle : handles)
+ for (auto &handle : handles)
{
ITensorHandle *parent_handle = handle.first;
ARM_COMPUTE_ERROR_ON(parent_handle == nullptr);
// If the tensor is not already in flight:
- if(tensors_in_flight.find(parent_handle) == std::end(tensors_in_flight))
+ if (tensors_in_flight.find(parent_handle) == std::end(tensors_in_flight))
{
ARM_COMPUTE_ERROR_ON(hc.find(parent_handle) == std::end(hc));
// Then add it to the list of in flight tensors
@@ -208,20 +209,20 @@
}
};
- for(auto &task_handle : tasks_handles)
+ for (auto &task_handle : tasks_handles)
{
// Marking all the input and output tensors of the task as in flight
acquire(task_handle.input_handles);
acquire(task_handle.output_handles);
// Releasing the input tensors
- for(auto &input_handle : task_handle.input_handles)
+ for (auto &input_handle : task_handle.input_handles)
{
ITensorHandle *ihandle = input_handle.first;
ARM_COMPUTE_ERROR_ON(ihandle == nullptr);
ARM_COMPUTE_ERROR_ON(tensors_in_flight.find(ihandle) == std::end(tensors_in_flight));
--tensors_in_flight[ihandle];
- if(tensors_in_flight[ihandle] <= 0)
+ if (tensors_in_flight[ihandle] <= 0)
{
// Remove tensor for tensors in flight
tensors_in_flight.erase(ihandle);
@@ -242,7 +243,7 @@
TargetHandleCounter target_handle_count;
// Count handles
- for(auto &task : workload.tasks)
+ for (auto &task : workload.tasks)
{
// Populates IO handles
tasks_handles.push_back(get_transition_handles(ctx, task, const_tensors));
@@ -252,12 +253,12 @@
}
// Setup memory managers
- for(auto &hc : target_handle_count)
+ for (auto &hc : target_handle_count)
{
MemoryManagerContext *mm_ctx = ctx.memory_management_ctx(hc.first);
- if(mm_ctx != nullptr)
+ if (mm_ctx != nullptr)
{
- if(mm_ctx->cross_mm != nullptr && mm_ctx->cross_group != nullptr)
+ if (mm_ctx->cross_mm != nullptr && mm_ctx->cross_group != nullptr)
{
// Manage and allocate tensors
configure_handle_lifetime(tasks_handles, hc.second);
diff --git a/src/graph/detail/ExecutionHelpers.cpp b/src/graph/detail/ExecutionHelpers.cpp
index ac800df..870d24a 100644
--- a/src/graph/detail/ExecutionHelpers.cpp
+++ b/src/graph/detail/ExecutionHelpers.cpp
@@ -23,12 +23,12 @@
*/
#include "arm_compute/graph/detail/ExecutionHelpers.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/GraphManager.h"
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/BackendRegistry.h"
namespace arm_compute
{
@@ -41,9 +41,9 @@
auto &nodes = g.nodes();
// Create tasks
- for(auto &node : nodes)
+ for (auto &node : nodes)
{
- if(node != nullptr)
+ if (node != nullptr)
{
Target assigned_target = node->assigned_target();
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(assigned_target);
@@ -57,9 +57,9 @@
{
auto &tensors = g.tensors();
- for(auto &tensor : tensors)
+ for (auto &tensor : tensors)
{
- if(tensor && tensor->handle() == nullptr)
+ if (tensor && tensor->handle() == nullptr)
{
Target target = tensor->desc().target;
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(target);
@@ -72,10 +72,10 @@
void allocate_all_input_tensors(INode &node)
{
- for(unsigned int i = 0; i < node.num_inputs(); ++i)
+ for (unsigned int i = 0; i < node.num_inputs(); ++i)
{
Tensor *tensor = node.input(i);
- if(tensor != nullptr && !tensor->bound_edges().empty())
+ if (tensor != nullptr && !tensor->bound_edges().empty())
{
ARM_COMPUTE_ERROR_ON_MSG(!tensor->handle(), "Tensor handle is not configured!");
tensor->handle()->allocate();
@@ -85,10 +85,10 @@
void allocate_all_output_tensors(INode &node)
{
- for(unsigned int i = 0; i < node.num_outputs(); ++i)
+ for (unsigned int i = 0; i < node.num_outputs(); ++i)
{
Tensor *tensor = node.output(i);
- if(tensor != nullptr && !tensor->bound_edges().empty())
+ if (tensor != nullptr && !tensor->bound_edges().empty())
{
ARM_COMPUTE_ERROR_ON_MSG(!tensor->handle(), "Tensor handle is not configured!");
tensor->handle()->allocate();
@@ -98,11 +98,11 @@
void allocate_const_tensors(Graph &g)
{
- for(auto &node : g.nodes())
+ for (auto &node : g.nodes())
{
- if(node != nullptr)
+ if (node != nullptr)
{
- switch(node->type())
+ switch (node->type())
{
case NodeType::Const:
case NodeType::Input:
@@ -121,9 +121,10 @@
{
auto &tensors = g.tensors();
- for(auto &tensor : tensors)
+ for (auto &tensor : tensors)
{
- if(tensor && !tensor->bound_edges().empty() && tensor->handle() != nullptr && tensor->handle()->tensor().info()->is_resizable() && tensor->handle()->tensor().is_used())
+ if (tensor && !tensor->bound_edges().empty() && tensor->handle() != nullptr &&
+ tensor->handle()->tensor().info()->is_resizable() && tensor->handle()->tensor().is_used())
{
tensor->handle()->allocate();
}
@@ -140,15 +141,15 @@
workload.tasks.reserve(node_order.size());
// Create tasks
- for(auto &node_id : node_order)
+ for (auto &node_id : node_order)
{
auto node = g.node(node_id);
- if(node != nullptr)
+ if (node != nullptr)
{
Target assigned_target = node->assigned_target();
- backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(assigned_target);
+ backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(assigned_target);
std::unique_ptr<IFunction> func = backend.configure_node(*node, ctx);
- if(func != nullptr || is_utility_node(node))
+ if (func != nullptr || is_utility_node(node))
{
workload.tasks.emplace_back(ExecutionTask(std::move(func), node));
}
@@ -156,14 +157,14 @@
}
// Add inputs and outputs
- for(auto &node : g.nodes())
+ for (auto &node : g.nodes())
{
- if(node != nullptr && node->type() == NodeType::Input)
+ if (node != nullptr && node->type() == NodeType::Input)
{
workload.inputs.push_back(node->output(0));
}
- if(node != nullptr && node->type() == NodeType::Output)
+ if (node != nullptr && node->type() == NodeType::Output)
{
workload.outputs.push_back(node->input(0));
continue;
@@ -175,9 +176,9 @@
void release_unused_tensors(Graph &g)
{
- for(auto &tensor : g.tensors())
+ for (auto &tensor : g.tensors())
{
- if(tensor != nullptr && tensor->handle() != nullptr)
+ if (tensor != nullptr && tensor->handle() != nullptr)
{
tensor->handle()->release_if_unused();
}
@@ -194,11 +195,11 @@
{
auto &nodes = g.nodes();
- for(auto &node : nodes)
+ for (auto &node : nodes)
{
- if(node != nullptr && node->type() == NodeType::Const && node->num_outputs())
+ if (node != nullptr && node->type() == NodeType::Const && node->num_outputs())
{
- if(!node->output(0)->bound_edges().empty())
+ if (!node->output(0)->bound_edges().empty())
{
call_tensor_accessor(node->output(0));
}
@@ -209,18 +210,19 @@
bool call_all_input_node_accessors(ExecutionWorkload &workload)
{
bool is_valid = true;
- std::for_each(std::begin(workload.inputs), std::end(workload.inputs), [&](Tensor * input_tensor)
- {
- bool valid_input = (input_tensor != nullptr) && input_tensor->call_accessor();
- is_valid = is_valid && valid_input;
- });
+ std::for_each(std::begin(workload.inputs), std::end(workload.inputs),
+ [&](Tensor *input_tensor)
+ {
+ bool valid_input = (input_tensor != nullptr) && input_tensor->call_accessor();
+ is_valid = is_valid && valid_input;
+ });
return is_valid;
}
void prepare_all_tasks(ExecutionWorkload &workload)
{
ARM_COMPUTE_ERROR_ON(workload.graph == nullptr);
- for(auto &task : workload.tasks)
+ for (auto &task : workload.tasks)
{
task.prepare();
release_unused_tensors(*workload.graph);
@@ -232,24 +234,24 @@
ARM_COMPUTE_ERROR_ON(workload.ctx == nullptr);
// Acquire memory for the transition buffers
- for(auto &mm_ctx : workload.ctx->memory_managers())
+ for (auto &mm_ctx : workload.ctx->memory_managers())
{
- if(mm_ctx.second.cross_group != nullptr)
+ if (mm_ctx.second.cross_group != nullptr)
{
mm_ctx.second.cross_group->acquire();
}
}
// Execute tasks
- for(auto &task : workload.tasks)
+ for (auto &task : workload.tasks)
{
task();
}
// Release memory for the transition buffers
- for(auto &mm_ctx : workload.ctx->memory_managers())
+ for (auto &mm_ctx : workload.ctx->memory_managers())
{
- if(mm_ctx.second.cross_group != nullptr)
+ if (mm_ctx.second.cross_group != nullptr)
{
mm_ctx.second.cross_group->release();
}
@@ -259,11 +261,12 @@
bool call_all_output_node_accessors(ExecutionWorkload &workload)
{
bool is_valid = true;
- std::for_each(std::begin(workload.outputs), std::end(workload.outputs), [&](Tensor * output_tensor)
- {
- bool valid_output = (output_tensor != nullptr) && output_tensor->call_accessor();
- is_valid = is_valid && valid_output;
- });
+ std::for_each(std::begin(workload.outputs), std::end(workload.outputs),
+ [&](Tensor *output_tensor)
+ {
+ bool valid_output = (output_tensor != nullptr) && output_tensor->call_accessor();
+ is_valid = is_valid && valid_output;
+ });
sync_backends();
diff --git a/src/graph/frontend/Stream.cpp b/src/graph/frontend/Stream.cpp
index 44c8400..383a6dc 100644
--- a/src/graph/frontend/Stream.cpp
+++ b/src/graph/frontend/Stream.cpp
@@ -23,8 +23,8 @@
*/
#include "arm_compute/graph/frontend/Stream.h"
-#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/frontend/ILayer.h"
+#include "arm_compute/graph/Utils.h"
namespace arm_compute
{
@@ -32,8 +32,7 @@
{
namespace frontend
{
-Stream::Stream(size_t id, std::string name)
- : _ctx(), _manager(), _g(id, std::move(name))
+Stream::Stream(size_t id, std::string name) : _ctx(), _manager(), _g(id, std::move(name))
{
}
diff --git a/src/graph/frontend/SubStream.cpp b/src/graph/frontend/SubStream.cpp
index 4b42207..8596aaa 100644
--- a/src/graph/frontend/SubStream.cpp
+++ b/src/graph/frontend/SubStream.cpp
@@ -23,8 +23,8 @@
*/
#include "arm_compute/graph/frontend/SubStream.h"
-#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/frontend/ILayer.h"
+#include "arm_compute/graph/Graph.h"
namespace arm_compute
{
@@ -32,8 +32,7 @@
{
namespace frontend
{
-SubStream::SubStream(IStream &s)
- : _s(s)
+SubStream::SubStream(IStream &s) : _s(s)
{
_hints = s.hints();
_tail_node = s.tail_node();
diff --git a/src/graph/mutators/DepthConcatSubTensorMutator.cpp b/src/graph/mutators/DepthConcatSubTensorMutator.cpp
index 963b948..1b7ee3c 100644
--- a/src/graph/mutators/DepthConcatSubTensorMutator.cpp
+++ b/src/graph/mutators/DepthConcatSubTensorMutator.cpp
@@ -23,12 +23,12 @@
*/
#include "arm_compute/graph/mutators/DepthConcatSubTensorMutator.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/algorithms/TopologicalSort.h"
#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/nodes/ConcatenateLayerNode.h"
+#include "arm_compute/graph/Utils.h"
#include "support/Cast.h"
#include "support/Iterable.h"
@@ -50,7 +50,7 @@
void DepthConcatSubTensorMutator::mutate(Graph &g)
{
// Early exit if no Concatenation layers exist in graph
- if(g.nodes(NodeType::ConcatenateLayer).empty())
+ if (g.nodes(NodeType::ConcatenateLayer).empty())
{
return;
}
@@ -59,43 +59,48 @@
std::vector<NodeID> topological_sorted_node_ids = dfs(g);
// Should be in reverse order of execution
- for(auto &node_id : arm_compute::utils::iterable::reverse_iterate(topological_sorted_node_ids))
+ for (auto &node_id : arm_compute::utils::iterable::reverse_iterate(topological_sorted_node_ids))
{
INode *node = g.node(node_id);
- if(node != nullptr && node->type() == NodeType::ConcatenateLayer && node->output(0) != nullptr)
+ if (node != nullptr && node->type() == NodeType::ConcatenateLayer && node->output(0) != nullptr)
{
// Get output tensor
auto output_tensor = node->output(0);
// Check concatenation axis (Sub-tensor optimization is supported for concatenation axis >=2)
auto *concat_node = arm_compute::utils::cast::polymorphic_downcast<ConcatenateLayerNode *>(node);
- if(output_tensor == nullptr || get_dimension_idx(output_tensor->desc().layout, concat_node->concatenation_axis()) < 2)
+ if (output_tensor == nullptr ||
+ get_dimension_idx(output_tensor->desc().layout, concat_node->concatenation_axis()) < 2)
{
continue;
}
// Check that all tensor have the same target, valid inputs and same quantization info
- bool is_valid = std::all_of(node->input_edges().cbegin(), node->input_edges().cend(),
- [&](const EdgeID & eid)
- {
- return (g.edge(eid) != nullptr) && (g.edge(eid)->tensor() != nullptr) && (g.edge(eid)->tensor()->desc().target == output_tensor->desc().target)
- && (g.edge(eid)->tensor()->desc().quant_info == output_tensor->desc().quant_info);
- });
+ bool is_valid =
+ std::all_of(node->input_edges().cbegin(), node->input_edges().cend(),
+ [&](const EdgeID &eid)
+ {
+ return (g.edge(eid) != nullptr) && (g.edge(eid)->tensor() != nullptr) &&
+ (g.edge(eid)->tensor()->desc().target == output_tensor->desc().target) &&
+ (g.edge(eid)->tensor()->desc().quant_info == output_tensor->desc().quant_info);
+ });
// Create subtensors
- if(is_valid && is_target_supported(output_tensor->desc().target))
+ if (is_valid && is_target_supported(output_tensor->desc().target))
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Using sub-tensors for the node with ID : "
<< node->id() << " and name : " << node->name() << std::endl);
// Create sub-tensor handles
unsigned depth = 0;
- for(unsigned int i = 0; i < node->input_edges().size(); ++i)
+ for (unsigned int i = 0; i < node->input_edges().size(); ++i)
{
auto input_tensor = node->input(i);
const auto input_shape = input_tensor->desc().shape;
- backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(input_tensor->desc().target);
- std::unique_ptr<ITensorHandle> handle = backend.create_subtensor(output_tensor->handle(), input_shape, Coordinates(0, 0, depth), false);
+ backends::IDeviceBackend &backend =
+ backends::BackendRegistry::get().get_backend(input_tensor->desc().target);
+ std::unique_ptr<ITensorHandle> handle =
+ backend.create_subtensor(output_tensor->handle(), input_shape, Coordinates(0, 0, depth), false);
input_tensor->set_handle(std::move(handle));
depth += input_shape.z();
diff --git a/src/graph/mutators/GroupedConvolutionMutator.cpp b/src/graph/mutators/GroupedConvolutionMutator.cpp
index b7c551c..31efba6 100644
--- a/src/graph/mutators/GroupedConvolutionMutator.cpp
+++ b/src/graph/mutators/GroupedConvolutionMutator.cpp
@@ -23,15 +23,14 @@
*/
#include "arm_compute/graph/mutators/GroupedConvolutionMutator.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphBuilder.h"
#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/Utils.h"
#include "support/Cast.h"
-
#include "support/StringSupport.h"
#include <set>
@@ -42,43 +41,51 @@
{
namespace
{
-NodeID create_grouped_convolution(Graph &g, const NodeParams ¶ms, NodeIdxPair input, NodeID weights, NodeID bias,
- PadStrideInfo conv_info, ConvolutionMethod method, ActivationLayerInfo fused_act, FastMathHint fast_math_hint, unsigned int num_groups)
+NodeID create_grouped_convolution(Graph &g,
+ const NodeParams ¶ms,
+ NodeIdxPair input,
+ NodeID weights,
+ NodeID bias,
+ PadStrideInfo conv_info,
+ ConvolutionMethod method,
+ ActivationLayerInfo fused_act,
+ FastMathHint fast_math_hint,
+ unsigned int num_groups)
{
bool has_bias = (bias != EmptyNodeID);
// Split input
const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
- const unsigned int input_idx = get_dimension_idx(input_tensor_desc.layout, DataLayoutDimension::CHANNEL);
- NodeID input_split = GraphBuilder::add_split_node(g, params, input, num_groups, input_idx);
+ const unsigned int input_idx = get_dimension_idx(input_tensor_desc.layout, DataLayoutDimension::CHANNEL);
+ NodeID input_split = GraphBuilder::add_split_node(g, params, input, num_groups, input_idx);
// Split weights
const TensorDescriptor weights_tensor_desc = get_tensor_descriptor(g, g.node(weights)->outputs()[0]);
- const unsigned int batch_idx = get_dimension_idx(weights_tensor_desc.layout, DataLayoutDimension::BATCHES);
- NodeID weights_split = GraphBuilder::add_split_node(g, params, { weights, 0 }, num_groups, batch_idx);
+ const unsigned int batch_idx = get_dimension_idx(weights_tensor_desc.layout, DataLayoutDimension::BATCHES);
+ NodeID weights_split = GraphBuilder::add_split_node(g, params, {weights, 0}, num_groups, batch_idx);
// Split bias
NodeID bias_split = EmptyNodeID;
- if(has_bias)
+ if (has_bias)
{
// Split bias
- bias_split = GraphBuilder::add_split_node(g, params, { bias, 0 }, num_groups, 0);
+ 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)
+ for (unsigned int i = 0; i < num_groups; ++i)
{
NodeParams group_params = params;
NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, 1, 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)
+ if (has_bias)
{
g.add_connection(bias_split, i, conv_nid, 2);
}
// Add group name
- if(!group_params.name.empty())
+ if (!group_params.name.empty())
{
group_params.name.append("_g" + arm_compute::support::cpp11::to_string(i));
}
@@ -92,7 +99,7 @@
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node);
conv_node->set_fused_activation(fused_act);
- convolution_outputs.push_back({ conv_nid, 0 });
+ convolution_outputs.push_back({conv_nid, 0});
}
// Depth concatenate output
@@ -113,7 +120,7 @@
void GroupedConvolutionMutator::mutate(Graph &g)
{
// Early exit if no Convolution layers exist in graph
- if(g.nodes(NodeType::ConvolutionLayer).empty())
+ if (g.nodes(NodeType::ConvolutionLayer).empty())
{
return;
}
@@ -122,17 +129,18 @@
size_t total_nodes = g.nodes().size();
// Iterate over convolution nodes
- for(unsigned int i = 0; i < total_nodes; ++i)
+ for (unsigned int i = 0; i < total_nodes; ++i)
{
INode *node = g.node(i);
- if(node != nullptr && node->type() == NodeType::ConvolutionLayer && arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node)->num_groups() != 1)
+ if (node != nullptr && node->type() == NodeType::ConvolutionLayer &&
+ arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node)->num_groups() != 1)
{
// Validate node
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(node->assigned_target());
Status status = backend.validate_node(*node);
// If grouped convolution is not supported
- if(!bool(status))
+ if (!bool(status))
{
// Down-cast node
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node);
@@ -151,7 +159,8 @@
ARM_COMPUTE_ERROR_ON(conv_node->input_edge(0) == nullptr || conv_node->input_edge(1) == nullptr);
const NodeID input_id = conv_node->input_edge(0)->producer()->id();
const NodeID weights_id = conv_node->input_edge(1)->producer()->id();
- const NodeID bias_id = (conv_node->input_edge(2) != nullptr) ? conv_node->input_edge(2)->producer()->id() : EmptyNodeID;
+ const NodeID bias_id =
+ (conv_node->input_edge(2) != nullptr) ? conv_node->input_edge(2)->producer()->id() : EmptyNodeID;
// Get driving nodes
std::vector<NodeIdxPair> driving_nodes = get_driving_nodes(*node);
@@ -164,14 +173,15 @@
NodeID latest_nid = g.nodes().size();
// Create grouped convolution node
- NodeID grouped_conv_id = create_grouped_convolution(g, params, { input_id, 0 }, weights_id, bias_id,
- conv_info, conv_method, fused_act_info, fast_math_hint, num_groups);
+ NodeID grouped_conv_id =
+ create_grouped_convolution(g, params, {input_id, 0}, weights_id, bias_id, conv_info, conv_method,
+ fused_act_info, fast_math_hint, num_groups);
// Remove convolution node
g.remove_node(node->id());
// Update batch normalization node outputs
- for(auto &driving_node : driving_nodes)
+ for (auto &driving_node : driving_nodes)
{
g.add_connection(grouped_conv_id, 0, driving_node.node_id, driving_node.index);
}
@@ -180,17 +190,16 @@
g.node(grouped_conv_id)->output(0)->set_accessor(std::move(node_accessor));
// Configure new tensors and nodes
- std::for_each(g.tensors().begin() + latest_tid, g.tensors().end(), [](std::unique_ptr<Tensor> &t)
- {
- configure_tensor(t.get());
- });
- std::for_each(g.nodes().begin() + latest_nid, g.nodes().end(), [&assigned_target](std::unique_ptr<INode> &n)
- {
- if(n != nullptr)
- {
- n->set_assigned_target(assigned_target);
- }
- });
+ std::for_each(g.tensors().begin() + latest_tid, g.tensors().end(),
+ [](std::unique_ptr<Tensor> &t) { configure_tensor(t.get()); });
+ std::for_each(g.nodes().begin() + latest_nid, g.nodes().end(),
+ [&assigned_target](std::unique_ptr<INode> &n)
+ {
+ if (n != nullptr)
+ {
+ n->set_assigned_target(assigned_target);
+ }
+ });
}
}
}
diff --git a/src/graph/mutators/InPlaceOperationMutator.cpp b/src/graph/mutators/InPlaceOperationMutator.cpp
index d3ea940..a51dcc4 100644
--- a/src/graph/mutators/InPlaceOperationMutator.cpp
+++ b/src/graph/mutators/InPlaceOperationMutator.cpp
@@ -29,6 +29,7 @@
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/nodes/DepthwiseConvolutionLayerNode.h"
#include "arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h"
+
#include "support/Cast.h"
using namespace arm_compute::utils::cast;
@@ -48,7 +49,7 @@
const auto input_tensor = input_edge->tensor();
const auto input_edge_id = input_edge->id();
- if(parent_node == nullptr)
+ if (parent_node == nullptr)
{
return false;
}
@@ -57,24 +58,23 @@
// If the output is connected to only one edge, then computations can
// be done in-place.
- if(output_edges.size() == 1)
+ if (output_edges.size() == 1)
{
return true;
}
- return std::all_of(output_edges.begin(),
- output_edges.end(),
- [&](const EdgeID & edge_id)
- {
- // Skip check on current input edge
- if(edge_id == input_edge_id)
- {
- return true;
- }
+ return std::all_of(output_edges.begin(), output_edges.end(),
+ [&](const EdgeID &edge_id)
+ {
+ // Skip check on current input edge
+ if (edge_id == input_edge_id)
+ {
+ return true;
+ }
- auto edge = g.edge(edge_id);
- return edge->tensor() != input_tensor;
- });
+ auto edge = g.edge(edge_id);
+ return edge->tensor() != input_tensor;
+ });
}
// If do in-place calculation, then need to use the new output and inherit original output's accessor
@@ -109,12 +109,14 @@
// Extract PadStrideInfo and depth multiplier
PadStrideInfo conv_info{};
unsigned int depth_multiplier{};
- if(node->type() == NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer)
+ if (node->type() == NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer)
{
- conv_info = polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node.get())->convolution_info();
- depth_multiplier = polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node.get())->depth_multiplier();
+ conv_info =
+ polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node.get())->convolution_info();
+ depth_multiplier =
+ polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node.get())->depth_multiplier();
}
- else if(node->type() == NodeType::DepthwiseConvolutionLayer)
+ else if (node->type() == NodeType::DepthwiseConvolutionLayer)
{
conv_info = polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node.get())->convolution_info();
depth_multiplier = polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node.get())->depth_multiplier();
@@ -126,7 +128,8 @@
const auto out_shape = current_output_tensor->desc().shape;
const auto qinfo_out = current_output_tensor->desc().quant_info;
- bool input_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, input_shape, 0) && (qinfo_input == qinfo_out) && (input_tensor->accessor() == nullptr);
+ bool input_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, input_shape, 0) &&
+ (qinfo_input == qinfo_out) && (input_tensor->accessor() == nullptr);
// Specify conditions with which input can be in-placed
input_can_in_place &= weight_layout == input_tensor->desc().layout && weight_layout == DataLayout::NHWC;
@@ -141,13 +144,14 @@
input_can_in_place &= !conv_info.has_padding();
// NOTE: Dilation should also be (1, 1). However currently dilation is not supported in the depthwise conv node
- if(input_can_in_place)
+ if (input_can_in_place)
{
set_new_output_and_inherit_accessor(node, current_output_tensor, input_tensor);
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to the input tensor or the quantization info are different.\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to the input tensor "
+ "or the quantization info are different.\n");
}
}
@@ -170,7 +174,7 @@
const TensorShape out_shape = TensorShape::broadcast_shape(shape0, shape1);
// Inputs are not broadcast compatible
- if(out_shape.total_size() == 0)
+ if (out_shape.total_size() == 0)
{
return;
}
@@ -181,22 +185,27 @@
const auto qinfo_out = current_output_tensor->desc().quant_info;
// Can do in place, if the input has same shape as output, has same quntisation info as output, has same data type as output and input doesn't have accessor.
- bool input0_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, shape0, 0) && (qinfo0 == qinfo_out)
- && (input0_tensor->desc().data_type == current_output_tensor->desc().data_type) && (input0_tensor->accessor() == nullptr);
- bool input1_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, shape1, 0) && (qinfo1 == qinfo_out)
- && (input1_tensor->desc().data_type == current_output_tensor->desc().data_type) && (input1_tensor->accessor() == nullptr);
+ bool input0_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, shape0, 0) &&
+ (qinfo0 == qinfo_out) &&
+ (input0_tensor->desc().data_type == current_output_tensor->desc().data_type) &&
+ (input0_tensor->accessor() == nullptr);
+ bool input1_can_in_place = !arm_compute::detail::have_different_dimensions(out_shape, shape1, 0) &&
+ (qinfo1 == qinfo_out) &&
+ (input1_tensor->desc().data_type == current_output_tensor->desc().data_type) &&
+ (input1_tensor->accessor() == nullptr);
- if(input0_can_in_place)
+ if (input0_can_in_place)
{
set_new_output_and_inherit_accessor(node, current_output_tensor, input0_tensor);
}
- else if(input1_can_in_place)
+ else if (input1_can_in_place)
{
set_new_output_and_inherit_accessor(node, current_output_tensor, input1_tensor);
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to the input tensor or the quantization info are different.\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to the input tensor "
+ "or the quantization info are different.\n");
}
}
} // namespace
@@ -213,33 +222,31 @@
void InPlaceOperationMutator::mutate(Graph &g)
{
- std::set<NodeType> in_place_nodes =
- {
- NodeType::ActivationLayer,
- NodeType::BatchNormalizationLayer,
- NodeType::EltwiseLayer,
- NodeType::UnaryEltwiseLayer,
- NodeType::DepthwiseConvolutionLayer,
- NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer,
- NodeType::PrintLayer
- };
+ std::set<NodeType> in_place_nodes = {NodeType::ActivationLayer,
+ NodeType::BatchNormalizationLayer,
+ NodeType::EltwiseLayer,
+ NodeType::UnaryEltwiseLayer,
+ NodeType::DepthwiseConvolutionLayer,
+ NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer,
+ NodeType::PrintLayer};
// Not interested in the order of nodes
- for(auto &node : g.nodes())
+ for (auto &node : g.nodes())
{
- if(node && in_place_nodes.find(node->type()) != std::end(in_place_nodes))
+ if (node && in_place_nodes.find(node->type()) != std::end(in_place_nodes))
{
// Get input edge
Edge *input_edge = node->input_edge(0);
// Check if parent has a single output if yes then force in place calculation else not
- if((input_edge != nullptr) && output_edges_are_separate_tensors(g, input_edge))
+ if ((input_edge != nullptr) && output_edges_are_separate_tensors(g, input_edge))
{
- if(node->type() == NodeType::EltwiseLayer)
+ if (node->type() == NodeType::EltwiseLayer)
{
try_in_place_elementwise(node);
}
- else if(node->type() == NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer || node->type() == NodeType::DepthwiseConvolutionLayer)
+ else if (node->type() == NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer ||
+ node->type() == NodeType::DepthwiseConvolutionLayer)
{
try_in_place_depthwiseconv(node);
}
@@ -252,9 +259,11 @@
ARM_COMPUTE_ERROR_ON(current_output_tensor == nullptr || new_output_tensor == nullptr);
// Prevent in-place operation if there is an accessor bound to the in-place tensor or quantization info are different
- if(new_output_tensor->accessor() != nullptr || current_output_tensor->desc().quant_info != new_output_tensor->desc().quant_info)
+ if (new_output_tensor->accessor() != nullptr ||
+ current_output_tensor->desc().quant_info != new_output_tensor->desc().quant_info)
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to the input tensor or the quantization info are different.\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented in-place operation as there is an accessor bound to "
+ "the input tensor or the quantization info are different.\n");
}
else
{
diff --git a/src/graph/mutators/MutatorUtils.cpp b/src/graph/mutators/MutatorUtils.cpp
index c8f38f3..f47240e 100644
--- a/src/graph/mutators/MutatorUtils.cpp
+++ b/src/graph/mutators/MutatorUtils.cpp
@@ -29,14 +29,14 @@
{
bool is_padding_in_height_or_width(const DataLayout &layout, const PaddingList &padding_list)
{
- if(layout == DataLayout::NCHW || layout == DataLayout::NHWC)
+ if (layout == DataLayout::NCHW || layout == DataLayout::NHWC)
{
const unsigned int height_index = get_dimension_idx(layout, DataLayoutDimension::HEIGHT);
const unsigned int width_index = get_dimension_idx(layout, DataLayoutDimension::WIDTH);
- for(unsigned int i = 0; i < padding_list.size(); ++i)
+ for (unsigned int i = 0; i < padding_list.size(); ++i)
{
- if(i != height_index && i != width_index && padding_list[i] != PaddingInfo(0, 0))
+ if (i != height_index && i != width_index && padding_list[i] != PaddingInfo(0, 0))
{
// if the index is not either height or width, don't fuse
return false;
@@ -49,4 +49,4 @@
return false;
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/mutators/NodeExecutionMethodMutator.cpp b/src/graph/mutators/NodeExecutionMethodMutator.cpp
index 09a3cf5..588befe 100644
--- a/src/graph/mutators/NodeExecutionMethodMutator.cpp
+++ b/src/graph/mutators/NodeExecutionMethodMutator.cpp
@@ -23,11 +23,11 @@
*/
#include "arm_compute/graph/mutators/NodeExecutionMethodMutator.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/Utils.h"
#include "support/Cast.h"
@@ -49,17 +49,17 @@
void set_default_on_invalid_method(Graph &g, NodeType node_type, Setter &&setter)
{
const std::vector<NodeID> &node_ids = g.nodes(node_type);
- for(auto &node_id : node_ids)
+ for (auto &node_id : node_ids)
{
INode *node = g.node(node_id);
- if(node != nullptr)
+ if (node != nullptr)
{
// Validate node
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(node->assigned_target());
Status status = backend.validate_node(*node);
// Set default execution method in case of failure
- if(!bool(status))
+ if (!bool(status))
{
setter(node);
}
@@ -81,22 +81,26 @@
void NodeExecutionMethodMutator::mutate(Graph &g)
{
// Convolution Layer
- set_default_on_invalid_method(g, NodeType::ConvolutionLayer, [](INode * n)
- {
- ARM_COMPUTE_LOG_GRAPH_INFO("Switched ConvolutionLayer method of node with ID : "
- << n->id() << " and Name: " << n->name() << std::endl);
- auto *casted_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(n);
- casted_node->set_convolution_method(ConvolutionMethod::Default);
- });
+ set_default_on_invalid_method(g, NodeType::ConvolutionLayer,
+ [](INode *n)
+ {
+ ARM_COMPUTE_LOG_GRAPH_INFO("Switched ConvolutionLayer method of node with ID : "
+ << n->id() << " and Name: " << n->name() << std::endl);
+ auto *casted_node =
+ arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(n);
+ casted_node->set_convolution_method(ConvolutionMethod::Default);
+ });
// Depthwise Convolution Layer
- set_default_on_invalid_method(g, NodeType::DepthwiseConvolutionLayer, [](INode * n)
- {
- ARM_COMPUTE_LOG_GRAPH_INFO("Switched Depthwise ConvolutionLayer method of node with ID : "
- << n->id() << " and Name: " << n->name() << std::endl);
- auto *casted_node = arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode *>(n);
- casted_node->set_depthwise_convolution_method(DepthwiseConvolutionMethod::Default);
- });
+ set_default_on_invalid_method(
+ g, NodeType::DepthwiseConvolutionLayer,
+ [](INode *n)
+ {
+ ARM_COMPUTE_LOG_GRAPH_INFO("Switched Depthwise ConvolutionLayer method of node with ID : "
+ << n->id() << " and Name: " << n->name() << std::endl);
+ auto *casted_node = arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode *>(n);
+ casted_node->set_depthwise_convolution_method(DepthwiseConvolutionMethod::Default);
+ });
}
} // namespace graph
} // namespace arm_compute
diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp
index 38284b9..998a4a0 100644
--- a/src/graph/mutators/NodeFusionMutator.cpp
+++ b/src/graph/mutators/NodeFusionMutator.cpp
@@ -24,15 +24,14 @@
#include "arm_compute/graph/mutators/NodeFusionMutator.h"
#include "arm_compute/core/utils/DataTypeUtils.h"
+#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/GraphBuilder.h"
#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Utils.h"
-#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h"
#include "arm_compute/graph/nodes/Nodes.h"
+#include "arm_compute/graph/Utils.h"
#include "src/graph/mutators/MutatorUtils.h"
-
#include "support/Cast.h"
#include <list>
@@ -46,7 +45,7 @@
{
void transfer_driving_nodes_and_remove_old_node(Graph &g, INode *new_node, INode *old_node, bool add_output_tensor)
{
- if(new_node == nullptr || old_node == nullptr)
+ if (new_node == nullptr || old_node == nullptr)
{
return;
}
@@ -55,7 +54,7 @@
std::vector<NodeIdxPair> last_driving_nodes = get_driving_nodes(*old_node);
// Extract last fusable node accessor if any
- if(old_node->output(0) == nullptr)
+ if (old_node->output(0) == nullptr)
{
return;
}
@@ -65,10 +64,10 @@
g.remove_node(old_node->id());
// Update fused node outputs
- for(auto &driving_node : last_driving_nodes)
+ for (auto &driving_node : last_driving_nodes)
{
g.add_connection(new_node->id(), 0, driving_node.node_id, driving_node.index);
- if(add_output_tensor)
+ if (add_output_tensor)
{
configure_tensor(new_node->output(0));
}
@@ -83,19 +82,21 @@
ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(output_edge->producer());
- auto *bn_node = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
+ auto *bn_node =
+ arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
// Not fusing if number of groups is greater than 1
- if(conv_node->num_groups() > 1)
+ if (conv_node->num_groups() > 1)
{
return;
}
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : " << output_edge->producer_id()
- << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing convolution node with ID : "
+ << output_edge->producer_id() << " with BatchNormalization Layer node with ID : "
+ << output_edge->consumer_id() << std::endl);
// Prevent fusion if fused node has an output accessor
- if(conv_node->output(0)->accessor() == nullptr)
+ if (conv_node->output(0)->accessor() == nullptr)
{
const Target assigned_target = conv_node->assigned_target();
@@ -115,9 +116,10 @@
const auto epsilon = bn_node->epsilon();
// Create the fused node
- const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info);
+ const NodeID fused_id = g.add_node<FusedConvolutionBatchNormalizationNode>(
+ epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info);
- if(conv_node->input_edge(2) != nullptr)
+ if (conv_node->input_edge(2) != nullptr)
{
auto conv_bias_id = conv_node->input_edge(2)->producer_id();
g.add_connection(conv_bias_id, 0, fused_id, 2);
@@ -129,13 +131,13 @@
g.add_connection(bn_mean_id, 0, fused_id, 3);
g.add_connection(bn_var_id, 0, fused_id, 4);
- if(bn_node->input_edge(3) != nullptr)
+ if (bn_node->input_edge(3) != nullptr)
{
const auto bn_beta_id = bn_node->input_edge(3)->producer_id();
g.add_connection(bn_beta_id, 0, fused_id, 5);
}
- if(bn_node->input_edge(4) != nullptr)
+ if (bn_node->input_edge(4) != nullptr)
{
const auto bn_gamma_id = bn_node->input_edge(4)->producer_id();
g.add_connection(bn_gamma_id, 0, fused_id, 6);
@@ -147,14 +149,15 @@
transfer_driving_nodes_and_remove_old_node(g, fused_node, bn_node, true);
fused_node->set_assigned_target(assigned_target);
- fused_node->set_common_node_parameters(NodeParams{ conv_node->name() + "+" + bn_node_name, assigned_target });
+ fused_node->set_common_node_parameters(NodeParams{conv_node->name() + "+" + bn_node_name, assigned_target});
// Remove convolution node
g.remove_node(conv_node->id());
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Prevented fusion of convolution with batch normalization due to the presence of an output accessor\n");
}
}
@@ -162,14 +165,17 @@
{
ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
- auto *depth_conv_node = arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode *>(output_edge->producer());
- auto *bn_node = arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
+ auto *depth_conv_node =
+ arm_compute::utils::cast::polymorphic_downcast<DepthwiseConvolutionLayerNode *>(output_edge->producer());
+ auto *bn_node =
+ arm_compute::utils::cast::polymorphic_downcast<BatchNormalizationLayerNode *>(output_edge->consumer());
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing depthwise convolution node with ID : " << output_edge->producer_id()
- << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl);
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing depthwise convolution node with ID : "
+ << output_edge->producer_id() << " with BatchNormalization Layer node with ID : "
+ << output_edge->consumer_id() << std::endl);
// Prevent fusion if fused node has an output accessor
- if(depth_conv_node->output(0)->accessor() == nullptr)
+ if (depth_conv_node->output(0)->accessor() == nullptr)
{
const Target assigned_target = depth_conv_node->assigned_target();
@@ -189,9 +195,10 @@
const auto epsilon = bn_node->epsilon();
// Create the fused node
- const NodeID fused_id = g.add_node<FusedDepthwiseConvolutionBatchNormalizationNode>(epsilon, conv_info, depth_multiplier, depth_conv_method, act_info);
+ const NodeID fused_id = g.add_node<FusedDepthwiseConvolutionBatchNormalizationNode>(
+ epsilon, conv_info, depth_multiplier, depth_conv_method, act_info);
- if(depth_conv_node->input_edge(2) != nullptr)
+ if (depth_conv_node->input_edge(2) != nullptr)
{
const auto conv_bias_id = depth_conv_node->input_edge(2)->producer_id();
g.add_connection(conv_bias_id, 0, fused_id, 2);
@@ -211,19 +218,23 @@
transfer_driving_nodes_and_remove_old_node(g, fused_node, bn_node, true);
fused_node->set_assigned_target(assigned_target);
- fused_node->set_common_node_parameters(NodeParams{ depth_conv_node->name() + "+" + bn_node_name, assigned_target });
+ fused_node->set_common_node_parameters(
+ NodeParams{depth_conv_node->name() + "+" + bn_node_name, assigned_target});
// Remove convolution node
g.remove_node(depth_conv_node->id());
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of depthwise convolution with batch normalization due to the presence of an output accessor\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of depthwise convolution with batch normalization due to the "
+ "presence of an output accessor\n");
}
}
template <typename N>
-void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set<Activation> &supported_fused_activations)
+void fuse_node_with_activation(Graph &g,
+ const Edge *output_edge,
+ const std::set<Activation> &supported_fused_activations)
{
ARM_COMPUTE_ERROR_ON(output_edge == nullptr);
@@ -233,22 +244,23 @@
ARM_COMPUTE_ERROR_ON(act_node->output(0) == nullptr || n_node->output(0) == nullptr);
// Check if activation is supported for fusion
- if(supported_fused_activations.count(act_node->activation_info().activation()) == 0)
+ if (supported_fused_activations.count(act_node->activation_info().activation()) == 0)
{
return;
}
// EltwiseLayerNode can only be fused when dataype is float
- if(n_node->type() == NodeType::EltwiseLayer && !is_data_type_float(n_node->output(0)->desc().data_type))
+ if (n_node->type() == NodeType::EltwiseLayer && !is_data_type_float(n_node->output(0)->desc().data_type))
{
return;
}
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing node with ID : " << output_edge->producer_id()
- << " with Activation Layer node with ID : " << output_edge->consumer_id() << std::endl);
+ << " with Activation Layer node with ID : "
+ << output_edge->consumer_id() << std::endl);
// Prevent fusion if fused node has an output accessor
- if(n_node->output(0)->accessor() == nullptr)
+ if (n_node->output(0)->accessor() == nullptr)
{
// Set activation info to fused node
n_node->set_fused_activation(act_node->activation_info());
@@ -257,7 +269,8 @@
}
else
{
- ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of node with activation due to the presence of an output accessor\n");
+ ARM_COMPUTE_LOG_GRAPH_VERBOSE(
+ "Prevented fusion of node with activation due to the presence of an output accessor\n");
}
}
@@ -268,8 +281,8 @@
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<N *>(output_edge->consumer());
const Edge *input_edge = pad_node->input_edge(0);
- if(input_edge != nullptr && input_edge->tensor() != nullptr && pad_node->output(0)->accessor() == nullptr
- && pad_node->pad_value().get<float>() == 0.0)
+ if (input_edge != nullptr && input_edge->tensor() != nullptr && pad_node->output(0)->accessor() == nullptr &&
+ pad_node->pad_value().get<float>() == 0.0)
{
const DataLayout layout = input_edge->tensor()->desc().layout;
const PaddingList padding_list = pad_node->padding();
@@ -280,18 +293,14 @@
const PaddingInfo pad_w = width_index < padding_list.size() ? padding_list[width_index] : PaddingInfo(0, 0);
const PaddingInfo pad_h = height_index < padding_list.size() ? padding_list[height_index] : PaddingInfo(0, 0);
- if(is_padding_in_height_or_width(layout, padding_list))
+ if (is_padding_in_height_or_width(layout, padding_list))
{
// Add paddings to the convolution node
const PadStrideInfo conv_info = conv_node->convolution_info();
- const PadStrideInfo new_conv_info(
- conv_info.stride().first,
- conv_info.stride().second,
- conv_info.pad_left() + pad_w.first,
- conv_info.pad_right() + pad_w.second,
- conv_info.pad_top() + pad_h.first,
- conv_info.pad_bottom() + pad_h.second,
- conv_info.round());
+ const PadStrideInfo new_conv_info(conv_info.stride().first, conv_info.stride().second,
+ conv_info.pad_left() + pad_w.first, conv_info.pad_right() + pad_w.second,
+ conv_info.pad_top() + pad_h.first, conv_info.pad_bottom() + pad_h.second,
+ conv_info.round());
conv_node->set_convolution_info(new_conv_info);
// Update drivers of the convolution node
@@ -299,7 +308,7 @@
g.remove_node(pad_node->id());
// Update fused node inputs
- for(auto &driver_node : pad_driver_nodes)
+ for (auto &driver_node : pad_driver_nodes)
{
g.add_connection(driver_node.node_id, driver_node.index, conv_node->id(), 0);
}
@@ -308,22 +317,23 @@
}
template <typename N1, typename N2, typename F, typename... Args>
-void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
+void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&...optional_arguments)
{
// Note that fused nodes may be added to the end of the node list.
// Instead of only looping over the original list of nodes, we loop over the current node list which could be growing.
// This is intentional as it probes the newly added fused nodes for further fusing opportunities.
- for(unsigned int i = 0; i < g.nodes().size(); ++i)
+ for (unsigned int i = 0; i < g.nodes().size(); ++i)
{
auto node = g.node(i);
// Check if the node is of type N1 and not a branching node
- if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
+ if (node && node->type() == N1::node_type && node->output_edges().size() == 1)
{
const auto output_edge_id = *node->output_edges().begin();
const auto output_edge = g.edge(output_edge_id);
// Check if following node is a type N2 node
- if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
+ if ((output_edge != nullptr) && (output_edge->consumer() != nullptr) &&
+ (output_edge->consumer()->type() == N2::node_type) && prec(*output_edge->producer()))
{
fuse_fcn(g, output_edge, optional_arguments...);
}
@@ -332,22 +342,22 @@
}
template <typename N1, typename F, typename... Args>
-void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&... optional_arguments)
+void fuse_layer(Graph &g, std::function<bool(INode &)> const &prec, const F fuse_fcn, Args &&...optional_arguments)
{
// Note that fused nodes may be added to the end of the node list.
// Instead of only looping over the original list of nodes, we loop over the current node list which could be growing.
// This is intentional as it probes the newly added fused nodes for further fusing opportunities.
- for(unsigned int i = 0; i < g.nodes().size(); ++i)
+ for (unsigned int i = 0; i < g.nodes().size(); ++i)
{
auto node = g.node(i);
// Check if the node is of type N1 and not a branching node
- if(node && node->type() == N1::node_type && node->output_edges().size() == 1)
+ if (node && node->type() == N1::node_type && node->output_edges().size() == 1)
{
const auto output_edge_id = *node->output_edges().begin();
const auto output_edge = g.edge(output_edge_id);
// Check if it's the correct target
- if((output_edge != nullptr) && (output_edge->consumer() != nullptr) && prec(*output_edge->producer()))
+ if ((output_edge != nullptr) && (output_edge->consumer() != nullptr) && prec(*output_edge->producer()))
{
fuse_fcn(g, output_edge, i, optional_arguments...);
}
@@ -369,30 +379,24 @@
void NodeFusionMutator::mutate(Graph &g)
{
// Supported activations when fusing
- const std::set<Activation> supported_fused_activations = { Activation::ABS, Activation::BOUNDED_RELU, Activation::ELU,
- Activation::HARD_SWISH, Activation::IDENTITY, Activation::LEAKY_RELU,
- Activation::LINEAR, Activation::LOGISTIC, Activation::LU_BOUNDED_RELU,
- Activation::RELU, Activation::SOFT_RELU, Activation::SQRT,
- Activation::SQUARE, Activation::TANH
- };
+ const std::set<Activation> supported_fused_activations = {
+ Activation::ABS, Activation::BOUNDED_RELU, Activation::ELU,
+ Activation::HARD_SWISH, Activation::IDENTITY, Activation::LEAKY_RELU,
+ Activation::LINEAR, Activation::LOGISTIC, Activation::LU_BOUNDED_RELU,
+ Activation::RELU, Activation::SOFT_RELU, Activation::SQRT,
+ Activation::SQUARE, Activation::TANH};
// Preconditions
- auto empty_prec = [](INode &)
- {
- return true;
- };
- auto cl_target_prec = [](INode & n)
- {
- return n.assigned_target() == Target::CL;
- };
- auto qs8_prec = [&g](INode & n)
+ auto empty_prec = [](INode &) { return true; };
+ auto cl_target_prec = [](INode &n) { return n.assigned_target() == Target::CL; };
+ auto qs8_prec = [&g](INode &n)
{
ARM_COMPUTE_ERROR_ON(n.output(0) == nullptr);
const auto output_edge_id = *n.output_edges().begin();
const auto output_edge = g.edge(output_edge_id);
// To perform fusion the two nodes must have same output quantization information
- const bool same_qinfo = n.output(0)->desc().quant_info == output_edge->producer()->output(0)->desc().quant_info;
+ const bool same_qinfo = n.output(0)->desc().quant_info == output_edge->producer()->output(0)->desc().quant_info;
const bool output_qasymm8 = n.output(0)->desc().data_type == DataType::QASYMM8;
return (output_qasymm8 && same_qinfo) || !output_qasymm8;
@@ -400,16 +404,25 @@
// Fusion mutations
- detail::fuse_layer<PadLayerNode, ConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<ConvolutionLayerNode>);
- detail::fuse_layer<PadLayerNode, DepthwiseConvolutionLayerNode>(g, empty_prec, detail::fuse_pad_with_convolution<DepthwiseConvolutionLayerNode>);
- detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
- detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
- detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
- detail::fuse_layer<FullyConnectedLayerNode, ActivationLayerNode>(g, empty_prec, detail::fuse_node_with_activation<FullyConnectedLayerNode>, supported_fused_activations);
- detail::fuse_layer<EltwiseLayerNode, ActivationLayerNode>(g, cl_target_prec, detail::fuse_node_with_activation<EltwiseLayerNode>, supported_fused_activations);
+ detail::fuse_layer<PadLayerNode, ConvolutionLayerNode>(g, empty_prec,
+ detail::fuse_pad_with_convolution<ConvolutionLayerNode>);
+ detail::fuse_layer<PadLayerNode, DepthwiseConvolutionLayerNode>(
+ g, empty_prec, detail::fuse_pad_with_convolution<DepthwiseConvolutionLayerNode>);
+ detail::fuse_layer<BatchNormalizationLayerNode, ActivationLayerNode>(
+ g, empty_prec, detail::fuse_node_with_activation<BatchNormalizationLayerNode>, supported_fused_activations);
+ detail::fuse_layer<ConvolutionLayerNode, ActivationLayerNode>(
+ g, empty_prec, detail::fuse_node_with_activation<ConvolutionLayerNode>, supported_fused_activations);
+ detail::fuse_layer<DepthwiseConvolutionLayerNode, ActivationLayerNode>(
+ g, qs8_prec, detail::fuse_node_with_activation<DepthwiseConvolutionLayerNode>, supported_fused_activations);
+ detail::fuse_layer<FullyConnectedLayerNode, ActivationLayerNode>(
+ g, empty_prec, detail::fuse_node_with_activation<FullyConnectedLayerNode>, supported_fused_activations);
+ detail::fuse_layer<EltwiseLayerNode, ActivationLayerNode>(
+ g, cl_target_prec, detail::fuse_node_with_activation<EltwiseLayerNode>, supported_fused_activations);
// The fusion of BatchNormalizationLayer must occur after the fusion of ActivationLayer. Because FusedConvolutionBatchNormalizationNode assumes the BatchNormalization is already fused with activation, if any
- detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_convolution_with_batch_normalization);
- detail::fuse_layer<DepthwiseConvolutionLayerNode, BatchNormalizationLayerNode>(g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization);
+ detail::fuse_layer<ConvolutionLayerNode, BatchNormalizationLayerNode>(
+ g, empty_prec, detail::fuse_convolution_with_batch_normalization);
+ detail::fuse_layer<DepthwiseConvolutionLayerNode, BatchNormalizationLayerNode>(
+ g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization);
}
} // namespace graph
} // namespace arm_compute
diff --git a/src/graph/mutators/SplitLayerSubTensorMutator.cpp b/src/graph/mutators/SplitLayerSubTensorMutator.cpp
index 2c28a1a..533f894 100644
--- a/src/graph/mutators/SplitLayerSubTensorMutator.cpp
+++ b/src/graph/mutators/SplitLayerSubTensorMutator.cpp
@@ -23,12 +23,12 @@
*/
#include "arm_compute/graph/mutators/SplitLayerSubTensorMutator.h"
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Logger.h"
-#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/algorithms/TopologicalSort.h"
#include "arm_compute/graph/backends/BackendRegistry.h"
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/nodes/SplitLayerNode.h"
+#include "arm_compute/graph/Utils.h"
#include "support/Cast.h"
#include "support/Iterable.h"
@@ -50,7 +50,7 @@
void SplitLayerSubTensorMutator::mutate(Graph &g)
{
// Early exit if no Split layers exist in graph
- if(g.nodes(NodeType::SplitLayer).empty())
+ if (g.nodes(NodeType::SplitLayer).empty())
{
return;
}
@@ -59,23 +59,23 @@
std::vector<NodeID> topological_sorted_node_ids = dfs(g);
// Should be in reverse order of execution
- for(auto &node_id : arm_compute::utils::iterable::reverse_iterate(topological_sorted_node_ids))
+ for (auto &node_id : arm_compute::utils::iterable::reverse_iterate(topological_sorted_node_ids))
{
INode *node = g.node(node_id);
- if(node != nullptr && node->type() == NodeType::SplitLayer && node->input(0) != nullptr)
+ if (node != nullptr && node->type() == NodeType::SplitLayer && node->input(0) != nullptr)
{
// Get output tensor
Tensor *input_tensor = node->input(0);
// Check that all tensor have the same target and are valid
bool is_valid = std::all_of(node->outputs().cbegin(), node->outputs().cend(),
- [&](const TensorID & tid)
- {
- return (g.tensor(tid) != nullptr) && (g.tensor(tid)->desc().target == input_tensor->desc().target);
- });
+ [&](const TensorID &tid) {
+ return (g.tensor(tid) != nullptr) &&
+ (g.tensor(tid)->desc().target == input_tensor->desc().target);
+ });
// Create subtensors
- if(is_valid && is_target_supported(input_tensor->desc().target))
+ if (is_valid && is_target_supported(input_tensor->desc().target))
{
ARM_COMPUTE_LOG_GRAPH_VERBOSE("Using sub-tensors for the node with ID : "
<< node->id() << " and name : " << node->name() << std::endl);
@@ -87,15 +87,18 @@
const bool extend_parent = (axis < 2);
// Create sub-tensor handles
- for(unsigned int i = 0; i < node->outputs().size(); ++i)
+ for (unsigned int i = 0; i < node->outputs().size(); ++i)
{
Tensor *output_tensor = node->output(i);
const TensorShape output_shape = output_tensor->desc().shape;
Coordinates coords;
- std::tie(std::ignore, coords) = split_node->compute_output_descriptor(input_tensor->desc(), num_splits, axis, i);
+ std::tie(std::ignore, coords) =
+ split_node->compute_output_descriptor(input_tensor->desc(), num_splits, axis, i);
- backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(output_tensor->desc().target);
- std::unique_ptr<ITensorHandle> handle = backend.create_subtensor(input_tensor->handle(), output_shape, coords, extend_parent);
+ backends::IDeviceBackend &backend =
+ backends::BackendRegistry::get().get_backend(output_tensor->desc().target);
+ std::unique_ptr<ITensorHandle> handle =
+ backend.create_subtensor(input_tensor->handle(), output_shape, coords, extend_parent);
output_tensor->set_handle(std::move(handle));
}
}
diff --git a/src/graph/mutators/SyntheticDataTypeMutator.cpp b/src/graph/mutators/SyntheticDataTypeMutator.cpp
index 74d040b..3dc2480 100644
--- a/src/graph/mutators/SyntheticDataTypeMutator.cpp
+++ b/src/graph/mutators/SyntheticDataTypeMutator.cpp
@@ -26,8 +26,8 @@
#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 "arm_compute/graph/Utils.h"
#include "support/Cast.h"
@@ -62,14 +62,12 @@
*/
bool is_mutation_supported(Graph &g)
{
- const std::set<NodeType> unsupported_node_types = { NodeType::DetectionOutputLayer,
- NodeType::NormalizationLayer,
- NodeType::PriorBoxLayer
- };
+ const std::set<NodeType> unsupported_node_types = {NodeType::DetectionOutputLayer, NodeType::NormalizationLayer,
+ NodeType::PriorBoxLayer};
- for(const auto &utype : unsupported_node_types)
+ for (const auto &utype : unsupported_node_types)
{
- if(!g.nodes(utype).empty())
+ if (!g.nodes(utype).empty())
{
return false;
}
@@ -83,12 +81,12 @@
*/
void remove_optimized_nodes(Graph &g)
{
- const std::set<NodeType> optimized_node_types = { NodeType::BatchNormalizationLayer };
+ const std::set<NodeType> optimized_node_types = {NodeType::BatchNormalizationLayer};
- for(const auto &opt_type : optimized_node_types)
+ 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)
+ for (const auto &node_id : opt_nodes_ids)
{
INode *node = g.node(node_id);
@@ -108,7 +106,7 @@
g.remove_node(node->id());
// Update connections
- for(auto &driving_node : driving_nodes)
+ for (auto &driving_node : driving_nodes)
{
g.add_connection(producer->id(), producer_edge_id, driving_node.node_id, driving_node.index);
}
@@ -123,11 +121,11 @@
void convert_tensors(Graph &g, DataType data_type)
{
auto &tensors = g.tensors();
- for(auto &tensor : tensors)
+ for (auto &tensor : tensors)
{
- if(tensor != nullptr)
+ if (tensor != nullptr)
{
- switch(data_type)
+ switch (data_type)
{
case DataType::QASYMM8:
case DataType::QASYMM8_SIGNED:
@@ -156,7 +154,7 @@
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)
+ 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);
@@ -174,41 +172,41 @@
*/
void convert_special_tensors(Graph &g)
{
- auto softmax_func = [](INode * node, Tensor * tensor)
+ auto softmax_func = [](INode *node, Tensor *tensor)
{
ARM_COMPUTE_UNUSED(node);
- if(tensor->desc().data_type == DataType::QASYMM8)
+ if (tensor->desc().data_type == DataType::QASYMM8)
{
tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, 0);
}
- else if(tensor->desc().data_type == DataType::QASYMM8_SIGNED)
+ else if (tensor->desc().data_type == DataType::QASYMM8_SIGNED)
{
tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, -128);
}
return true;
};
- auto act_func = [](INode * node, Tensor * tensor)
+ auto act_func = [](INode *node, Tensor *tensor)
{
auto *act_node = arm_compute::utils::cast::polymorphic_downcast<ActivationLayerNode *>(node);
- if(tensor->desc().data_type == DataType::QASYMM8)
+ if (tensor->desc().data_type == DataType::QASYMM8)
{
- if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::TANH)
+ 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)
+ else if (act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::LOGISTIC)
{
tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, 0);
}
}
- else if(tensor->desc().data_type == DataType::QASYMM8_SIGNED)
+ else if (tensor->desc().data_type == DataType::QASYMM8_SIGNED)
{
- if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::TANH)
+ if (act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::TANH)
{
tensor->desc().quant_info = QuantizationInfo(1.f / 128.f, 0);
}
- else if(act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::LOGISTIC)
+ else if (act_node->activation_info().activation() == ActivationLayerInfo::ActivationFunction::LOGISTIC)
{
tensor->desc().quant_info = QuantizationInfo(1.f / 256.f, -128);
}
@@ -228,22 +226,19 @@
*/
void handle_nodes_with_bias(Graph &g)
{
- const std::set<NodeType> special_node_types = { NodeType::ConvolutionLayer,
- NodeType::DeconvolutionLayer,
- NodeType::DepthwiseConvolutionLayer,
- NodeType::FullyConnectedLayer
- };
+ const std::set<NodeType> special_node_types = {NodeType::ConvolutionLayer, NodeType::DeconvolutionLayer,
+ NodeType::DepthwiseConvolutionLayer, NodeType::FullyConnectedLayer};
- for(const auto &spc_type : special_node_types)
+ 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)
+ for (const auto &node_id : scp_nodes_ids)
{
INode *node = g.node(node_id);
- if(node != nullptr)
+ if (node != nullptr)
{
Tensor *tensor = node->input(2);
- if(tensor != nullptr)
+ if (tensor != nullptr)
{
tensor->desc().data_type = DataType::S32;
}
@@ -253,8 +248,8 @@
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 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));
@@ -266,8 +261,7 @@
}
} // namespace
-SyntheticDataTypeMutator::SyntheticDataTypeMutator(DataType mutate_type)
- : _mutate_type{ mutate_type }
+SyntheticDataTypeMutator::SyntheticDataTypeMutator(DataType mutate_type) : _mutate_type{mutate_type}
{
}
@@ -283,7 +277,7 @@
void SyntheticDataTypeMutator::mutate(Graph &g)
{
- if(is_mutation_supported(g))
+ if (is_mutation_supported(g))
{
// Remove nodes that get optimized out (e.g. BatchNorm)
remove_optimized_nodes(g);
diff --git a/src/graph/nodes/ActivationLayerNode.cpp b/src/graph/nodes/ActivationLayerNode.cpp
index cf65d83..1773afc 100644
--- a/src/graph/nodes/ActivationLayerNode.cpp
+++ b/src/graph/nodes/ActivationLayerNode.cpp
@@ -44,7 +44,7 @@
bool ActivationLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -63,7 +63,7 @@
ARM_COMPUTE_ERROR_ON(src == nullptr);
TensorDescriptor output_info = src->desc();
- if(!_out_quant_info.empty())
+ if (!_out_quant_info.empty())
{
output_info.quant_info = _out_quant_info;
}
diff --git a/src/graph/nodes/ArgMinMaxLayerNode.cpp b/src/graph/nodes/ArgMinMaxLayerNode.cpp
index 63163b9..5adebc9 100644
--- a/src/graph/nodes/ArgMinMaxLayerNode.cpp
+++ b/src/graph/nodes/ArgMinMaxLayerNode.cpp
@@ -23,16 +23,18 @@
*/
#include "arm_compute/graph/nodes/ArgMinMaxLayerNode.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
namespace arm_compute
{
namespace graph
{
-ArgMinMaxLayerNode::ArgMinMaxLayerNode(ReductionOperation op, unsigned int axis, DataType out_data_type, QuantizationInfo out_quant_info)
+ArgMinMaxLayerNode::ArgMinMaxLayerNode(ReductionOperation op,
+ unsigned int axis,
+ DataType out_data_type,
+ QuantizationInfo out_quant_info)
: _op(op), _axis(axis), _out_data_type(out_data_type), _out_quant_info(std::move(out_quant_info))
{
_input_edges.resize(1, EmptyEdgeID);
@@ -56,7 +58,7 @@
bool ArgMinMaxLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -75,17 +77,18 @@
ARM_COMPUTE_ERROR_ON(src == nullptr);
TensorDescriptor output_info = src->desc();
- if(!_out_quant_info.empty())
+ if (!_out_quant_info.empty())
{
output_info.quant_info = _out_quant_info;
}
- if(_out_data_type != DataType::UNKNOWN)
+ if (_out_data_type != DataType::UNKNOWN)
{
output_info.data_type = _out_data_type;
}
- TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(output_info.shape, _axis, false);
+ TensorShape output_shape =
+ arm_compute::misc::shape_calculator::compute_reduced_shape(output_info.shape, _axis, false);
output_info.set_shape(output_shape);
return output_info;
diff --git a/src/graph/nodes/BatchNormalizationLayerNode.cpp b/src/graph/nodes/BatchNormalizationLayerNode.cpp
index ceca0e2..c317123 100644
--- a/src/graph/nodes/BatchNormalizationLayerNode.cpp
+++ b/src/graph/nodes/BatchNormalizationLayerNode.cpp
@@ -55,7 +55,7 @@
bool BatchNormalizationLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -86,4 +86,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/BoundingBoxTransformLayerNode.cpp b/src/graph/nodes/BoundingBoxTransformLayerNode.cpp
index f3f4f91..8e52174 100644
--- a/src/graph/nodes/BoundingBoxTransformLayerNode.cpp
+++ b/src/graph/nodes/BoundingBoxTransformLayerNode.cpp
@@ -23,17 +23,15 @@
*/
#include "arm_compute/graph/nodes/BoundingBoxTransformLayerNode.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/Helpers.h"
-
namespace arm_compute
{
namespace graph
{
-BoundingBoxTransformLayerNode::BoundingBoxTransformLayerNode(BoundingBoxTransformInfo &info)
- : _bbox_info(info)
+BoundingBoxTransformLayerNode::BoundingBoxTransformLayerNode(BoundingBoxTransformInfo &info) : _bbox_info(info)
{
_input_edges.resize(2, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -46,7 +44,7 @@
bool BoundingBoxTransformLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/ChannelShuffleLayerNode.cpp b/src/graph/nodes/ChannelShuffleLayerNode.cpp
index 5102e4b..3cb9e23 100644
--- a/src/graph/nodes/ChannelShuffleLayerNode.cpp
+++ b/src/graph/nodes/ChannelShuffleLayerNode.cpp
@@ -30,8 +30,7 @@
{
namespace graph
{
-ChannelShuffleLayerNode::ChannelShuffleLayerNode(unsigned int num_groups)
- : _num_groups(num_groups)
+ChannelShuffleLayerNode::ChannelShuffleLayerNode(unsigned int num_groups) : _num_groups(num_groups)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -44,7 +43,7 @@
bool ChannelShuffleLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -75,4 +74,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/ConcatenateLayerNode.cpp b/src/graph/nodes/ConcatenateLayerNode.cpp
index 3f3c70f..8e5393a 100644
--- a/src/graph/nodes/ConcatenateLayerNode.cpp
+++ b/src/graph/nodes/ConcatenateLayerNode.cpp
@@ -24,17 +24,17 @@
#include "arm_compute/graph/nodes/ConcatenateLayerNode.h"
#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
namespace arm_compute
{
namespace graph
{
-ConcatenateLayerNode::ConcatenateLayerNode(unsigned int total_nodes, descriptors::ConcatLayerDescriptor concat_descriptor)
+ConcatenateLayerNode::ConcatenateLayerNode(unsigned int total_nodes,
+ descriptors::ConcatLayerDescriptor concat_descriptor)
: _total_nodes(total_nodes), _concat_descriptor(std::move(concat_descriptor)), _is_enabled(true)
{
_input_edges.resize(_total_nodes, EmptyEdgeID);
@@ -73,7 +73,7 @@
// Extract shapes
std::vector<const TensorShape *> shapes;
shapes.reserve(input_descriptors.size());
- for(auto &input_descriptor : input_descriptors)
+ for (auto &input_descriptor : input_descriptors)
{
shapes.emplace_back(&input_descriptor.shape);
}
@@ -85,7 +85,7 @@
bool ConcatenateLayerNode::forward_descriptors()
{
- if(_outputs[0] != NullTensorID)
+ if (_outputs[0] != NullTensorID)
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -101,24 +101,22 @@
ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
// Check if all input tensors are set
- bool are_all_inputs_set = std::all_of(std::begin(_input_edges), std::end(_input_edges), [](const EdgeID & eid)
- {
- return eid != EmptyEdgeID;
- });
+ bool are_all_inputs_set = std::all_of(std::begin(_input_edges), std::end(_input_edges),
+ [](const EdgeID &eid) { return eid != EmptyEdgeID; });
TensorDescriptor output_info = {};
- if(are_all_inputs_set)
+ if (are_all_inputs_set)
{
std::vector<TensorDescriptor> inputs_descriptors;
- for(unsigned int i = 0; i < _input_edges.size(); ++i)
+ for (unsigned int i = 0; i < _input_edges.size(); ++i)
{
const Tensor *t = _graph->tensor(input_id(i));
ARM_COMPUTE_ERROR_ON(t == nullptr);
inputs_descriptors.push_back(t->desc());
}
output_info = compute_output_descriptor(inputs_descriptors, _concat_descriptor.axis);
- if(!_concat_descriptor.output_qinfo.empty())
+ if (!_concat_descriptor.output_qinfo.empty())
{
output_info.quant_info = _concat_descriptor.output_qinfo;
}
diff --git a/src/graph/nodes/ConstNode.cpp b/src/graph/nodes/ConstNode.cpp
index eb96d63..6e8fbff 100644
--- a/src/graph/nodes/ConstNode.cpp
+++ b/src/graph/nodes/ConstNode.cpp
@@ -30,15 +30,14 @@
{
namespace graph
{
-ConstNode::ConstNode(TensorDescriptor desc)
- : _desc(std::move(desc))
+ConstNode::ConstNode(TensorDescriptor desc) : _desc(std::move(desc))
{
_outputs.resize(1, NullTensorID);
}
bool ConstNode::forward_descriptors()
{
- if(output_id(0) != NullTensorID)
+ if (output_id(0) != NullTensorID)
{
Tensor *t = output(0);
ARM_COMPUTE_ERROR_ON(t == nullptr);
diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp
index ee9dde9..f0263fc 100644
--- a/src/graph/nodes/ConvolutionLayerNode.cpp
+++ b/src/graph/nodes/ConvolutionLayerNode.cpp
@@ -37,7 +37,12 @@
ConvolutionMethod method,
FastMathHint fast_math_hint,
QuantizationInfo out_quant_info)
- : _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(std::move(out_quant_info)), _fused_activation()
+ : _info(std::move(info)),
+ _num_groups(num_groups),
+ _method(method),
+ _fast_math_hint(fast_math_hint),
+ _out_quant_info(std::move(out_quant_info)),
+ _fused_activation()
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -100,20 +105,22 @@
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
- std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+ std::tie(output_width, output_height) =
+ scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
- output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+ output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL),
+ weights_descriptor.shape[3]);
return output_descriptor;
}
bool ConvolutionLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -132,7 +139,7 @@
ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info);
- if(!_out_quant_info.empty())
+ if (!_out_quant_info.empty())
{
output_info.quant_info = _out_quant_info;
}
diff --git a/src/graph/nodes/DeconvolutionLayerNode.cpp b/src/graph/nodes/DeconvolutionLayerNode.cpp
index 3542d5a..2058ab2 100644
--- a/src/graph/nodes/DeconvolutionLayerNode.cpp
+++ b/src/graph/nodes/DeconvolutionLayerNode.cpp
@@ -56,20 +56,22 @@
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
- std::tie(output_width, output_height) = deconvolution_output_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+ std::tie(output_width, output_height) =
+ deconvolution_output_dimensions(input_width, input_height, kernel_width, kernel_height, info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
- output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+ output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL),
+ weights_descriptor.shape[3]);
return output_descriptor;
}
bool DeconvolutionLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -89,7 +91,7 @@
TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), descriptor.info);
- if(!descriptor.out_quant_info.empty())
+ if (!descriptor.out_quant_info.empty())
{
output_info.set_quantization_info(descriptor.out_quant_info);
}
diff --git a/src/graph/nodes/DepthToSpaceLayerNode.cpp b/src/graph/nodes/DepthToSpaceLayerNode.cpp
index b70ac56..0b914a0 100644
--- a/src/graph/nodes/DepthToSpaceLayerNode.cpp
+++ b/src/graph/nodes/DepthToSpaceLayerNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-DepthToSpaceLayerNode::DepthToSpaceLayerNode(int block_shape)
- : _block_shape(block_shape)
+DepthToSpaceLayerNode::DepthToSpaceLayerNode(int block_shape) : _block_shape(block_shape)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -44,7 +43,8 @@
return _block_shape;
}
-TensorDescriptor DepthToSpaceLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, int block_shape)
+TensorDescriptor DepthToSpaceLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+ int block_shape)
{
using namespace arm_compute::helpers::tensor_transform;
@@ -53,14 +53,15 @@
// Set descriptor shape
TensorDescriptor output_descriptor = input_descriptor;
- output_descriptor.shape = misc::shape_calculator::compute_depth_to_space_shape(input_shape, data_layout, block_shape);
+ output_descriptor.shape =
+ misc::shape_calculator::compute_depth_to_space_shape(input_shape, data_layout, block_shape);
return output_descriptor;
}
bool DepthToSpaceLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
index 7de2016..92d7266 100644
--- a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
+++ b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp
@@ -32,9 +32,15 @@
{
namespace graph
{
-DepthwiseConvolutionLayerNode::DepthwiseConvolutionLayerNode(PadStrideInfo info, int depth_multiplier, DepthwiseConvolutionMethod method,
- QuantizationInfo out_quant_info)
- : _info(std::move(info)), _depth_multiplier(depth_multiplier), _method(method), _out_quant_info(std::move(out_quant_info)), _fused_activation()
+DepthwiseConvolutionLayerNode::DepthwiseConvolutionLayerNode(PadStrideInfo info,
+ int depth_multiplier,
+ DepthwiseConvolutionMethod method,
+ QuantizationInfo out_quant_info)
+ : _info(std::move(info)),
+ _depth_multiplier(depth_multiplier),
+ _method(method),
+ _out_quant_info(std::move(out_quant_info)),
+ _fused_activation()
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -89,20 +95,22 @@
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
- std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+ std::tie(output_width, output_height) =
+ scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
- output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier);
+ output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL),
+ input_channels * depth_multiplier);
return output_descriptor;
}
bool DepthwiseConvolutionLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -121,7 +129,7 @@
ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr);
TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info, _depth_multiplier);
- if(!_out_quant_info.empty())
+ if (!_out_quant_info.empty())
{
output_info.quant_info = _out_quant_info;
}
@@ -139,4 +147,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/DequantizationLayerNode.cpp b/src/graph/nodes/DequantizationLayerNode.cpp
index 14c4752..3ea0008 100644
--- a/src/graph/nodes/DequantizationLayerNode.cpp
+++ b/src/graph/nodes/DequantizationLayerNode.cpp
@@ -40,7 +40,7 @@
bool DequantizationLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -74,4 +74,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/DetectionOutputLayerNode.cpp b/src/graph/nodes/DetectionOutputLayerNode.cpp
index fc6f531..65ddd2f 100644
--- a/src/graph/nodes/DetectionOutputLayerNode.cpp
+++ b/src/graph/nodes/DetectionOutputLayerNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-DetectionOutputLayerNode::DetectionOutputLayerNode(DetectionOutputLayerInfo detection_info)
- : _info(detection_info)
+DetectionOutputLayerNode::DetectionOutputLayerNode(DetectionOutputLayerInfo detection_info) : _info(detection_info)
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -47,7 +46,8 @@
TensorDescriptor DetectionOutputLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
const DetectionOutputLayerInfo &info)
{
- const unsigned int max_size = info.keep_top_k() * ((input_descriptor.shape.num_dimensions() > 1) ? input_descriptor.shape[1] : 1);
+ const unsigned int max_size =
+ info.keep_top_k() * ((input_descriptor.shape.num_dimensions() > 1) ? input_descriptor.shape[1] : 1);
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(0, detection_size);
@@ -58,7 +58,8 @@
bool DetectionOutputLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) &&
+ (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/DetectionPostProcessLayerNode.cpp b/src/graph/nodes/DetectionPostProcessLayerNode.cpp
index 2c5005a..af3fc03 100644
--- a/src/graph/nodes/DetectionPostProcessLayerNode.cpp
+++ b/src/graph/nodes/DetectionPostProcessLayerNode.cpp
@@ -46,10 +46,11 @@
bool DetectionPostProcessLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) && (output_id(0) != NullTensorID) && (output_id(1) != NullTensorID)
- && (output_id(2) != NullTensorID) && (output_id(3) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) &&
+ (output_id(0) != NullTensorID) && (output_id(1) != NullTensorID) && (output_id(2) != NullTensorID) &&
+ (output_id(3) != NullTensorID))
{
- for(unsigned int i = 0; i < 4; ++i)
+ for (unsigned int i = 0; i < 4; ++i)
{
Tensor *dst = output(i);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -68,7 +69,7 @@
TensorDescriptor output_desc;
const unsigned int num_detected_box = _info.max_detections() * _info.max_classes_per_detection();
- switch(idx)
+ switch (idx)
{
case 0:
// Configure boxes output
@@ -101,4 +102,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/DummyNode.cpp b/src/graph/nodes/DummyNode.cpp
index 6fa9fba..b5f37bd 100644
--- a/src/graph/nodes/DummyNode.cpp
+++ b/src/graph/nodes/DummyNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-DummyNode::DummyNode(TensorShape shape)
- : _shape(shape)
+DummyNode::DummyNode(TensorShape shape) : _shape(shape)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -41,7 +40,7 @@
bool DummyNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -75,4 +74,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/EltwiseLayerNode.cpp b/src/graph/nodes/EltwiseLayerNode.cpp
index 4426e95..3f7a08e 100644
--- a/src/graph/nodes/EltwiseLayerNode.cpp
+++ b/src/graph/nodes/EltwiseLayerNode.cpp
@@ -31,8 +31,7 @@
{
namespace graph
{
-EltwiseLayerNode::EltwiseLayerNode(const descriptors::EltwiseLayerDescriptor &descriptor)
- : descriptor(descriptor)
+EltwiseLayerNode::EltwiseLayerNode(const descriptors::EltwiseLayerDescriptor &descriptor) : descriptor(descriptor)
{
_input_edges.resize(2, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -70,7 +69,7 @@
bool EltwiseLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -97,7 +96,7 @@
output_info.set_shape(out_shape);
- if(!descriptor.out_quant_info.empty())
+ if (!descriptor.out_quant_info.empty())
{
output_info.set_quantization_info(descriptor.out_quant_info);
}
@@ -134,7 +133,7 @@
bool UnaryEltwiseLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -153,7 +152,7 @@
auto output_info = src->desc();
- if(!descriptor.out_quant_info.empty())
+ if (!descriptor.out_quant_info.empty())
{
output_info.set_quantization_info(descriptor.out_quant_info);
}
diff --git a/src/graph/nodes/FlattenLayerNode.cpp b/src/graph/nodes/FlattenLayerNode.cpp
index 48519a1..952df2f 100644
--- a/src/graph/nodes/FlattenLayerNode.cpp
+++ b/src/graph/nodes/FlattenLayerNode.cpp
@@ -38,7 +38,7 @@
bool FlattenLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -72,4 +72,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp
index 6278227..1eed69d 100644
--- a/src/graph/nodes/FullyConnectedLayer.cpp
+++ b/src/graph/nodes/FullyConnectedLayer.cpp
@@ -21,18 +21,23 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "arm_compute/graph/nodes/FullyConnectedLayerNode.h"
-
#include "arm_compute/core/Utils.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/nodes/FullyConnectedLayerNode.h"
namespace arm_compute
{
namespace graph
{
-FullyConnectedLayerNode::FullyConnectedLayerNode(unsigned int num_outputs, QuantizationInfo out_quant_info, FullyConnectedLayerInfo fc_info, FastMathHint fast_math_hint)
- : _num_outputs(num_outputs), _out_quant_info(std::move(out_quant_info)), _info(fc_info), _fast_math_hint(fast_math_hint)
+FullyConnectedLayerNode::FullyConnectedLayerNode(unsigned int num_outputs,
+ QuantizationInfo out_quant_info,
+ FullyConnectedLayerInfo fc_info,
+ FastMathHint fast_math_hint)
+ : _num_outputs(num_outputs),
+ _out_quant_info(std::move(out_quant_info)),
+ _info(fc_info),
+ _fast_math_hint(fast_math_hint)
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -60,11 +65,11 @@
unsigned int num_weights = 1;
unsigned int num_dimensions = input_descriptor.shape.num_dimensions();
// Ignore the batch dimension if there is one:
- if(num_dimensions == 2 || num_dimensions == 4)
+ if (num_dimensions == 2 || num_dimensions == 4)
{
num_dimensions--;
}
- for(unsigned int i = 0; i < num_dimensions; i++)
+ for (unsigned int i = 0; i < num_dimensions; i++)
{
num_weights *= input_descriptor.shape[i];
}
@@ -73,13 +78,13 @@
weights_descriptor.shape = TensorShape(num_weights, num_outputs);
// If weights are tranposed, use tranposed shape
- if(!fc_info.transpose_weights)
+ if (!fc_info.transpose_weights)
{
weights_descriptor.shape = TensorShape(num_outputs, num_weights);
}
// Set quantization info if present
- if(!weights_quant_info.empty())
+ if (!weights_quant_info.empty())
{
weights_descriptor.quant_info = weights_quant_info;
}
@@ -93,7 +98,7 @@
{
// Note: Only 1D batch space is supported at the moment
unsigned int batches = input_descriptor.shape[1];
- if(input_descriptor.shape.num_dimensions() > 2)
+ if (input_descriptor.shape.num_dimensions() > 2)
{
batches = input_descriptor.shape[3];
}
@@ -103,7 +108,7 @@
output_descriptor.shape = TensorShape(num_outputs, batches);
// Set quantization info if present
- if(!out_quant_info.empty())
+ if (!out_quant_info.empty())
{
output_descriptor.quant_info = out_quant_info;
}
@@ -118,7 +123,7 @@
bool FullyConnectedLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -147,4 +152,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
index de995eb..9d37e84 100644
--- a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
+++ b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp
@@ -32,12 +32,18 @@
{
namespace graph
{
-FusedConvolutionBatchNormalizationNode::FusedConvolutionBatchNormalizationNode(float epsilon, PadStrideInfo info,
- unsigned int num_groups,
- ConvolutionMethod method,
- FastMathHint fast_math_hint,
+FusedConvolutionBatchNormalizationNode::FusedConvolutionBatchNormalizationNode(float epsilon,
+ PadStrideInfo info,
+ unsigned int num_groups,
+ ConvolutionMethod method,
+ FastMathHint fast_math_hint,
ActivationLayerInfo fused_activation)
- : _epsilon(epsilon), _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _fused_activation(fused_activation)
+ : _epsilon(epsilon),
+ _info(std::move(info)),
+ _num_groups(num_groups),
+ _method(method),
+ _fast_math_hint(fast_math_hint),
+ _fused_activation(fused_activation)
{
_input_edges.resize(7, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -88,9 +94,8 @@
_fused_activation = fused_activation;
}
-TensorDescriptor FusedConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
- const TensorDescriptor &weights_descriptor,
- const PadStrideInfo &info)
+TensorDescriptor FusedConvolutionBatchNormalizationNode::compute_output_descriptor(
+ const TensorDescriptor &input_descriptor, const TensorDescriptor &weights_descriptor, const PadStrideInfo &info)
{
unsigned int output_width = 0;
unsigned int output_height = 0;
@@ -100,20 +105,22 @@
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
- std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+ std::tie(output_width, output_height) =
+ scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), output_width);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), output_height);
- output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]);
+ output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL),
+ weights_descriptor.shape[3]);
return output_descriptor;
}
bool FusedConvolutionBatchNormalizationNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp
index c022450..c51641d 100644
--- a/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp
+++ b/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp
@@ -32,18 +32,24 @@
{
namespace graph
{
-FusedDepthwiseConvolutionBatchNormalizationNode::FusedDepthwiseConvolutionBatchNormalizationNode(float epsilon,
- PadStrideInfo info,
- unsigned int depth_multiplier,
- DepthwiseConvolutionMethod method,
- ActivationLayerInfo fused_activation)
- : _epsilon(epsilon), _info(std::move(info)), _depth_multiplier(depth_multiplier), _method(method), _fused_activation(fused_activation)
+FusedDepthwiseConvolutionBatchNormalizationNode::FusedDepthwiseConvolutionBatchNormalizationNode(
+ float epsilon,
+ PadStrideInfo info,
+ unsigned int depth_multiplier,
+ DepthwiseConvolutionMethod method,
+ ActivationLayerInfo fused_activation)
+ : _epsilon(epsilon),
+ _info(std::move(info)),
+ _depth_multiplier(depth_multiplier),
+ _method(method),
+ _fused_activation(fused_activation)
{
_input_edges.resize(7, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
}
-void FusedDepthwiseConvolutionBatchNormalizationNode::set_depthwise_convolution_method(DepthwiseConvolutionMethod method)
+void FusedDepthwiseConvolutionBatchNormalizationNode::set_depthwise_convolution_method(
+ DepthwiseConvolutionMethod method)
{
_method = method;
}
@@ -78,10 +84,11 @@
_fused_activation = fused_activation;
}
-TensorDescriptor FusedDepthwiseConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
- const TensorDescriptor &weights_descriptor,
- const PadStrideInfo &info,
- int depth_multiplier)
+TensorDescriptor
+FusedDepthwiseConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+ const TensorDescriptor &weights_descriptor,
+ const PadStrideInfo &info,
+ int depth_multiplier)
{
unsigned int output_width = 0;
unsigned int output_height = 0;
@@ -92,19 +99,22 @@
const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH);
const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT);
- std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
+ std::tie(output_width, output_height) =
+ scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info);
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::WIDTH), output_width);
- output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::HEIGHT), output_height);
- output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier);
+ output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::HEIGHT),
+ output_height);
+ output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::CHANNEL),
+ input_channels * depth_multiplier);
return output_descriptor;
}
bool FusedDepthwiseConvolutionBatchNormalizationNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/GenerateProposalsLayerNode.cpp b/src/graph/nodes/GenerateProposalsLayerNode.cpp
index 9f36862..1671a47 100644
--- a/src/graph/nodes/GenerateProposalsLayerNode.cpp
+++ b/src/graph/nodes/GenerateProposalsLayerNode.cpp
@@ -23,17 +23,15 @@
*/
#include "arm_compute/graph/nodes/GenerateProposalsLayerNode.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/Helpers.h"
-
namespace arm_compute
{
namespace graph
{
-GenerateProposalsLayerNode::GenerateProposalsLayerNode(GenerateProposalsInfo &info)
- : _info(info)
+GenerateProposalsLayerNode::GenerateProposalsLayerNode(GenerateProposalsInfo &info) : _info(info)
{
_input_edges.resize(3, EmptyEdgeID);
_outputs.resize(3, NullTensorID);
@@ -46,10 +44,10 @@
bool GenerateProposalsLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) && (output_id(0) != NullTensorID) && (output_id(1) != NullTensorID)
- && (output_id(2) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (input_id(2) != NullTensorID) &&
+ (output_id(0) != NullTensorID) && (output_id(1) != NullTensorID) && (output_id(2) != NullTensorID))
{
- for(unsigned int i = 0; i < 3; ++i)
+ for (unsigned int i = 0; i < 3; ++i)
{
Tensor *dst = output(i);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -68,7 +66,7 @@
ARM_COMPUTE_ERROR_ON(src == nullptr);
TensorDescriptor output_desc = src->desc();
- switch(idx)
+ switch (idx)
{
case 0:
// Configure proposals output
diff --git a/src/graph/nodes/InputNode.cpp b/src/graph/nodes/InputNode.cpp
index 072281f..7408bc2 100644
--- a/src/graph/nodes/InputNode.cpp
+++ b/src/graph/nodes/InputNode.cpp
@@ -30,15 +30,14 @@
{
namespace graph
{
-InputNode::InputNode(TensorDescriptor desc)
- : _desc(std::move(desc))
+InputNode::InputNode(TensorDescriptor desc) : _desc(std::move(desc))
{
_outputs.resize(1, NullTensorID);
}
bool InputNode::forward_descriptors()
{
- if(output_id(0) != NullTensorID)
+ if (output_id(0) != NullTensorID)
{
Tensor *t = output(0);
ARM_COMPUTE_ERROR_ON(t == nullptr);
diff --git a/src/graph/nodes/L2NormalizeLayerNode.cpp b/src/graph/nodes/L2NormalizeLayerNode.cpp
index 0c35a33..1a57cf0 100644
--- a/src/graph/nodes/L2NormalizeLayerNode.cpp
+++ b/src/graph/nodes/L2NormalizeLayerNode.cpp
@@ -30,18 +30,15 @@
{
namespace graph
{
-L2NormalizeLayerNode::L2NormalizeLayerNode()
- : L2NormalizeLayerNode(0, 1e-12f)
+L2NormalizeLayerNode::L2NormalizeLayerNode() : L2NormalizeLayerNode(0, 1e-12f)
{
}
-L2NormalizeLayerNode::L2NormalizeLayerNode(int axis)
- : L2NormalizeLayerNode(axis, 1e-12f)
+L2NormalizeLayerNode::L2NormalizeLayerNode(int axis) : L2NormalizeLayerNode(axis, 1e-12f)
{
}
-L2NormalizeLayerNode::L2NormalizeLayerNode(int axis, float epsilon)
- : _axis(axis), _epsilon(epsilon)
+L2NormalizeLayerNode::L2NormalizeLayerNode(int axis, float epsilon) : _axis(axis), _epsilon(epsilon)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -49,7 +46,7 @@
bool L2NormalizeLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -92,4 +89,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/NormalizationLayerNode.cpp b/src/graph/nodes/NormalizationLayerNode.cpp
index eaa1bcf..b18bb7d 100644
--- a/src/graph/nodes/NormalizationLayerNode.cpp
+++ b/src/graph/nodes/NormalizationLayerNode.cpp
@@ -31,8 +31,7 @@
{
namespace graph
{
-NormalizationLayerNode::NormalizationLayerNode(NormalizationLayerInfo norm_info)
- : _info(norm_info)
+NormalizationLayerNode::NormalizationLayerNode(NormalizationLayerInfo norm_info) : _info(norm_info)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -45,7 +44,7 @@
bool NormalizationLayerNode::forward_descriptors()
{
- if(input_id(0) != NullTensorID && (output_id(0) != NullTensorID))
+ if (input_id(0) != NullTensorID && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -76,4 +75,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/NormalizePlanarYUVLayerNode.cpp b/src/graph/nodes/NormalizePlanarYUVLayerNode.cpp
index 113d0a5..cac9660 100644
--- a/src/graph/nodes/NormalizePlanarYUVLayerNode.cpp
+++ b/src/graph/nodes/NormalizePlanarYUVLayerNode.cpp
@@ -39,7 +39,7 @@
bool NormalizePlanarYUVLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/PReluLayerNode.cpp b/src/graph/nodes/PReluLayerNode.cpp
index 378c18e..2b50fe9 100644
--- a/src/graph/nodes/PReluLayerNode.cpp
+++ b/src/graph/nodes/PReluLayerNode.cpp
@@ -38,7 +38,7 @@
bool PReluLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/PadLayerNode.cpp b/src/graph/nodes/PadLayerNode.cpp
index 6424370..336e7de 100644
--- a/src/graph/nodes/PadLayerNode.cpp
+++ b/src/graph/nodes/PadLayerNode.cpp
@@ -23,17 +23,15 @@
*/
#include "arm_compute/graph/nodes/PadLayerNode.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/Helpers.h"
-
namespace arm_compute
{
namespace graph
{
-PadLayerNode::PadLayerNode(const PaddingList &padding, PixelValue pad_value)
- : _padding(padding), _pad_value(pad_value)
+PadLayerNode::PadLayerNode(const PaddingList &padding, PixelValue pad_value) : _padding(padding), _pad_value(pad_value)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -51,7 +49,7 @@
bool PadLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -71,7 +69,7 @@
TensorDescriptor output_desc = src->desc();
const TensorShape input_shape = src->desc().shape;
- for(size_t dim = 0; dim < _padding.size(); ++dim)
+ for (size_t dim = 0; dim < _padding.size(); ++dim)
{
output_desc.shape.set(dim, _padding[dim].first + input_shape[dim] + _padding[dim].second);
}
diff --git a/src/graph/nodes/PermuteLayerNode.cpp b/src/graph/nodes/PermuteLayerNode.cpp
index b311ee1..db53722 100644
--- a/src/graph/nodes/PermuteLayerNode.cpp
+++ b/src/graph/nodes/PermuteLayerNode.cpp
@@ -23,17 +23,15 @@
*/
#include "arm_compute/graph/nodes/PermuteLayerNode.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/Helpers.h"
-
namespace arm_compute
{
namespace graph
{
-PermuteLayerNode::PermuteLayerNode(PermutationVector perm, DataLayout layout)
- : _perm(perm), _layout(layout)
+PermuteLayerNode::PermuteLayerNode(PermutationVector perm, DataLayout layout) : _perm(perm), _layout(layout)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -46,7 +44,7 @@
bool PermuteLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -66,7 +64,7 @@
TensorDescriptor output_desc = src->desc();
permute(output_desc.shape, _perm);
- if(_layout != DataLayout::UNKNOWN)
+ if (_layout != DataLayout::UNKNOWN)
{
output_desc.layout = _layout;
}
@@ -84,4 +82,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/PoolingLayerNode.cpp b/src/graph/nodes/PoolingLayerNode.cpp
index 4ecf924..ac954ac 100644
--- a/src/graph/nodes/PoolingLayerNode.cpp
+++ b/src/graph/nodes/PoolingLayerNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-PoolingLayerNode::PoolingLayerNode(PoolingLayerInfo pool_info)
- : _info(std::move(pool_info))
+PoolingLayerNode::PoolingLayerNode(PoolingLayerInfo pool_info) : _info(std::move(pool_info))
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -55,7 +54,8 @@
const unsigned int pool_size_x = info.is_global_pooling ? input_width : info.pool_size.width;
const unsigned int pool_size_y = info.is_global_pooling ? input_height : info.pool_size.height;
- std::tie(pooled_width, pooled_height) = scaled_dimensions(input_width, input_height, pool_size_x, pool_size_y, info.pad_stride_info);
+ std::tie(pooled_width, pooled_height) =
+ scaled_dimensions(input_width, input_height, pool_size_x, pool_size_y, info.pad_stride_info);
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
@@ -67,7 +67,7 @@
bool PoolingLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -98,4 +98,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/PrintLayerNode.cpp b/src/graph/nodes/PrintLayerNode.cpp
index da408d8..82a3400 100644
--- a/src/graph/nodes/PrintLayerNode.cpp
+++ b/src/graph/nodes/PrintLayerNode.cpp
@@ -32,7 +32,9 @@
{
namespace graph
{
-PrintLayerNode::PrintLayerNode(std::ostream &stream, const IOFormatInfo &format_info, const std::function<ITensor *(ITensor *)> transform)
+PrintLayerNode::PrintLayerNode(std::ostream &stream,
+ const IOFormatInfo &format_info,
+ const std::function<ITensor *(ITensor *)> transform)
: _stream(stream), _format_info(format_info), _transform(transform)
{
_input_edges.resize(1, EmptyEdgeID);
@@ -56,7 +58,7 @@
bool PrintLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -88,4 +90,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/PriorBoxLayerNode.cpp b/src/graph/nodes/PriorBoxLayerNode.cpp
index f017ead..5ffb173 100644
--- a/src/graph/nodes/PriorBoxLayerNode.cpp
+++ b/src/graph/nodes/PriorBoxLayerNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-PriorBoxLayerNode::PriorBoxLayerNode(PriorBoxLayerInfo prior_info)
- : _info(std::move(prior_info))
+PriorBoxLayerNode::PriorBoxLayerNode(PriorBoxLayerInfo prior_info) : _info(std::move(prior_info))
{
_input_edges.resize(2, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -44,7 +43,7 @@
return _info;
}
-TensorDescriptor PriorBoxLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
+TensorDescriptor PriorBoxLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
const PriorBoxLayerInfo &info)
{
const unsigned int layer_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH);
@@ -61,7 +60,7 @@
bool PriorBoxLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/QuantizationLayerNode.cpp b/src/graph/nodes/QuantizationLayerNode.cpp
index 4906808..0dd2da9 100644
--- a/src/graph/nodes/QuantizationLayerNode.cpp
+++ b/src/graph/nodes/QuantizationLayerNode.cpp
@@ -47,7 +47,7 @@
bool QuantizationLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/ROIAlignLayerNode.cpp b/src/graph/nodes/ROIAlignLayerNode.cpp
index 6289181..5909335 100644
--- a/src/graph/nodes/ROIAlignLayerNode.cpp
+++ b/src/graph/nodes/ROIAlignLayerNode.cpp
@@ -24,17 +24,15 @@
#include "arm_compute/graph/nodes/ROIAlignLayerNode.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
-#include "arm_compute/core/Helpers.h"
-
namespace arm_compute
{
namespace graph
{
-ROIAlignLayerNode::ROIAlignLayerNode(ROIPoolingLayerInfo &pool_info)
- : _pool_info(pool_info)
+ROIAlignLayerNode::ROIAlignLayerNode(ROIPoolingLayerInfo &pool_info) : _pool_info(pool_info)
{
_input_edges.resize(2, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -47,7 +45,7 @@
bool ROIAlignLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -92,4 +90,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/ReductionLayerNode.cpp b/src/graph/nodes/ReductionLayerNode.cpp
index 0e93039..965c1ba 100644
--- a/src/graph/nodes/ReductionLayerNode.cpp
+++ b/src/graph/nodes/ReductionLayerNode.cpp
@@ -56,7 +56,7 @@
bool ReductionLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -74,8 +74,9 @@
const Tensor *src = input(0);
ARM_COMPUTE_ERROR_ON(src == nullptr);
- TensorDescriptor output_info = src->desc();
- TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(output_info.shape, _axis, _keep_dims);
+ TensorDescriptor output_info = src->desc();
+ TensorShape output_shape =
+ arm_compute::misc::shape_calculator::compute_reduced_shape(output_info.shape, _axis, _keep_dims);
output_info.set_shape(output_shape);
return output_info;
@@ -91,4 +92,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/ReorgLayerNode.cpp b/src/graph/nodes/ReorgLayerNode.cpp
index e693e4b..251a4ea 100644
--- a/src/graph/nodes/ReorgLayerNode.cpp
+++ b/src/graph/nodes/ReorgLayerNode.cpp
@@ -31,8 +31,7 @@
{
namespace graph
{
-ReorgLayerNode::ReorgLayerNode(int stride)
- : _stride(stride)
+ReorgLayerNode::ReorgLayerNode(int stride) : _stride(stride)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -51,20 +50,22 @@
ARM_COMPUTE_ERROR_ON(stride <= 0);
ARM_COMPUTE_ERROR_ON_MSG((input_width % stride != 0), "The width of the input tensor must be a multiple of stride");
- ARM_COMPUTE_ERROR_ON_MSG((input_height % stride != 0), "The height of the input tensor must be a multiple of stride");
+ ARM_COMPUTE_ERROR_ON_MSG((input_height % stride != 0),
+ "The height of the input tensor must be a multiple of stride");
const DataLayout data_layout = input_descriptor.layout;
TensorDescriptor output_descriptor = input_descriptor;
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::WIDTH), input_width / stride);
output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::HEIGHT), input_height / stride);
- output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL), input_channel * stride * stride);
+ output_descriptor.shape.set(get_dimension_idx(data_layout, DataLayoutDimension::CHANNEL),
+ input_channel * stride * stride);
return output_descriptor;
}
bool ReorgLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -95,4 +96,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/ReshapeLayer.cpp b/src/graph/nodes/ReshapeLayer.cpp
index a6354d0..ce6bf9b 100644
--- a/src/graph/nodes/ReshapeLayer.cpp
+++ b/src/graph/nodes/ReshapeLayer.cpp
@@ -21,17 +21,15 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "arm_compute/graph/nodes/ReshapeLayerNode.h"
-
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
+#include "arm_compute/graph/nodes/ReshapeLayerNode.h"
namespace arm_compute
{
namespace graph
{
-ReshapeLayerNode::ReshapeLayerNode(TensorShape shape)
- : _shape(shape)
+ReshapeLayerNode::ReshapeLayerNode(TensorShape shape) : _shape(shape)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -39,7 +37,7 @@
bool ReshapeLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -73,4 +71,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/ResizeLayerNode.cpp b/src/graph/nodes/ResizeLayerNode.cpp
index 2a94bf6..292b2c6 100644
--- a/src/graph/nodes/ResizeLayerNode.cpp
+++ b/src/graph/nodes/ResizeLayerNode.cpp
@@ -50,7 +50,7 @@
bool ResizeLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -88,4 +88,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/SliceLayerNode.cpp b/src/graph/nodes/SliceLayerNode.cpp
index b7655b9..eb877d9 100644
--- a/src/graph/nodes/SliceLayerNode.cpp
+++ b/src/graph/nodes/SliceLayerNode.cpp
@@ -32,8 +32,7 @@
{
namespace graph
{
-SliceLayerNode::SliceLayerNode(const Coordinates &starts, const Coordinates &ends)
- : _starts(starts), _ends(ends)
+SliceLayerNode::SliceLayerNode(const Coordinates &starts, const Coordinates &ends) : _starts(starts), _ends(ends)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -50,19 +49,20 @@
}
TensorDescriptor SliceLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
- const Coordinates &starts, const Coordinates &ends)
+ const Coordinates &starts,
+ const Coordinates &ends)
{
using namespace arm_compute::helpers::tensor_transform;
TensorDescriptor output_desc = input_descriptor;
- output_desc.shape = arm_compute::misc::shape_calculator::compute_slice_shape(input_descriptor.shape, starts, ends);
+ output_desc.shape = arm_compute::misc::shape_calculator::compute_slice_shape(input_descriptor.shape, starts, ends);
return output_desc;
}
bool SliceLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/nodes/SoftmaxLayerNode.cpp b/src/graph/nodes/SoftmaxLayerNode.cpp
index 0311669..4beac81 100644
--- a/src/graph/nodes/SoftmaxLayerNode.cpp
+++ b/src/graph/nodes/SoftmaxLayerNode.cpp
@@ -31,8 +31,7 @@
{
namespace graph
{
-SoftmaxLayerNode::SoftmaxLayerNode(float beta)
- : _beta(beta)
+SoftmaxLayerNode::SoftmaxLayerNode(float beta) : _beta(beta)
{
_input_edges.resize(1, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -45,7 +44,7 @@
bool SoftmaxLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -79,4 +78,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/SplitLayerNode.cpp b/src/graph/nodes/SplitLayerNode.cpp
index 31931c3..dfb6624 100644
--- a/src/graph/nodes/SplitLayerNode.cpp
+++ b/src/graph/nodes/SplitLayerNode.cpp
@@ -49,8 +49,8 @@
return _axis;
}
-std::pair<TensorDescriptor, Coordinates> SplitLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor,
- unsigned int num_splits, int axis, unsigned int idx)
+std::pair<TensorDescriptor, Coordinates> SplitLayerNode::compute_output_descriptor(
+ const TensorDescriptor &input_descriptor, unsigned int num_splits, int axis, unsigned int idx)
{
// Handle negative axis, negative index is used to specify axis from the end (e.g. -1 for the last axis).
int num_dimension = static_cast<int32_t>(input_descriptor.shape.num_dimensions());
@@ -58,7 +58,7 @@
Coordinates coords;
TensorDescriptor output_descriptor = input_descriptor;
int split_size = input_descriptor.shape[tmp_axis] / num_splits;
- if(_size_splits.empty())
+ if (_size_splits.empty())
{
output_descriptor.shape.set(tmp_axis, split_size);
coords.set(tmp_axis, idx * split_size);
@@ -66,15 +66,15 @@
else
{
int split_size = _size_splits[idx];
- if(split_size == -1)
+ if (split_size == -1)
{
split_size = input_descriptor.shape[tmp_axis];
- for(unsigned int i = 0; i < _size_splits.size() - 1; ++i)
+ for (unsigned int i = 0; i < _size_splits.size() - 1; ++i)
split_size -= _size_splits[i];
}
output_descriptor.shape.set(tmp_axis, split_size);
int coord_value = 0;
- for(unsigned int i = 0; i < idx; ++i)
+ for (unsigned int i = 0; i < idx; ++i)
coord_value += _size_splits[i];
coords.set(tmp_axis, coord_value);
}
@@ -84,12 +84,12 @@
bool SplitLayerNode::forward_descriptors()
{
- if(input_id(0) != NullTensorID)
+ if (input_id(0) != NullTensorID)
{
validate();
- for(unsigned int i = 0; i < _outputs.size(); ++i)
+ for (unsigned int i = 0; i < _outputs.size(); ++i)
{
- if(output_id(i) != NullTensorID)
+ if (output_id(i) != NullTensorID)
{
Tensor *dst_i = output(i);
ARM_COMPUTE_ERROR_ON(dst_i == nullptr);
@@ -117,10 +117,10 @@
int tmp_axis = wrap_around(_axis, num_dimension);
int split_size = (_size_splits.empty()) ? (input_descriptor.shape[tmp_axis] / _num_splits) : _size_splits[idx];
- if(split_size == -1)
+ if (split_size == -1)
{
split_size = input_descriptor.shape[tmp_axis];
- for(unsigned int i = 0; i < _size_splits.size() - 1; ++i)
+ for (unsigned int i = 0; i < _size_splits.size() - 1; ++i)
split_size -= _size_splits[i];
}
output_descriptor.shape.set(tmp_axis, split_size);
@@ -138,7 +138,7 @@
// Handle negative axis, negative index is used to specify axis from the end (e.g. -1 for the last axis).
int tmp_axis = wrap_around(_axis, num_dimension);
- if(_size_splits.empty())
+ if (_size_splits.empty())
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->desc().shape[tmp_axis] % _num_splits, "Split should be exact");
}
@@ -156,4 +156,4 @@
v.visit(*this);
}
} // namespace graph
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/graph/nodes/StackLayerNode.cpp b/src/graph/nodes/StackLayerNode.cpp
index f292b33..031d8fc 100644
--- a/src/graph/nodes/StackLayerNode.cpp
+++ b/src/graph/nodes/StackLayerNode.cpp
@@ -25,18 +25,16 @@
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/INodeVisitor.h"
#include "arm_compute/graph/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-
namespace arm_compute
{
namespace graph
{
-StackLayerNode::StackLayerNode(unsigned int total_nodes, int axis)
- : _total_nodes(total_nodes), _axis(axis)
+StackLayerNode::StackLayerNode(unsigned int total_nodes, int axis) : _total_nodes(total_nodes), _axis(axis)
{
_input_edges.resize(_total_nodes, EmptyEdgeID);
_outputs.resize(1, NullTensorID);
@@ -64,7 +62,7 @@
bool StackLayerNode::forward_descriptors()
{
- if(_outputs[0] != NullTensorID)
+ if (_outputs[0] != NullTensorID)
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
@@ -80,17 +78,15 @@
ARM_COMPUTE_ERROR_ON(idx >= _outputs.size());
// Check if all input tensors are set
- bool are_all_inputs_set = std::all_of(std::begin(_input_edges), std::end(_input_edges), [](const EdgeID & eid)
- {
- return eid != EmptyEdgeID;
- });
+ bool are_all_inputs_set = std::all_of(std::begin(_input_edges), std::end(_input_edges),
+ [](const EdgeID &eid) { return eid != EmptyEdgeID; });
TensorDescriptor output_info = {};
- if(are_all_inputs_set)
+ if (are_all_inputs_set)
{
std::vector<TensorDescriptor> inputs_descriptors;
- for(unsigned int i = 0; i < _input_edges.size(); ++i)
+ for (unsigned int i = 0; i < _input_edges.size(); ++i)
{
const Tensor *t = _graph->tensor(input_id(i));
ARM_COMPUTE_ERROR_ON(t == nullptr);
diff --git a/src/graph/nodes/StridedSliceLayerNode.cpp b/src/graph/nodes/StridedSliceLayerNode.cpp
index 6a1a724..fc9f722 100644
--- a/src/graph/nodes/StridedSliceLayerNode.cpp
+++ b/src/graph/nodes/StridedSliceLayerNode.cpp
@@ -79,7 +79,7 @@
bool StridedSliceLayerNode::forward_descriptors()
{
- if((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
+ if ((input_id(0) != NullTensorID) && (output_id(0) != NullTensorID))
{
Tensor *dst = output(0);
ARM_COMPUTE_ERROR_ON(dst == nullptr);
diff --git a/src/graph/printers/DotGraphPrinter.cpp b/src/graph/printers/DotGraphPrinter.cpp
index 9c7c424..5587ed2 100644
--- a/src/graph/printers/DotGraphPrinter.cpp
+++ b/src/graph/printers/DotGraphPrinter.cpp
@@ -25,9 +25,9 @@
#include "arm_compute/core/Error.h"
#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/nodes/Nodes.h"
#include "arm_compute/graph/Tensor.h"
#include "arm_compute/graph/TypePrinter.h"
-#include "arm_compute/graph/nodes/Nodes.h"
namespace arm_compute
{
@@ -152,9 +152,9 @@
void DotGraphPrinter::print_nodes(const Graph &g, std::ostream &os)
{
- for(const auto &n : g.nodes())
+ for (const auto &n : g.nodes())
{
- if(n)
+ if (n)
{
// Output node id
std::string node_id = std::string("n") + support::cpp11::to_string(n->id());
@@ -166,7 +166,8 @@
std::string name = n->name().empty() ? node_id : n->name();
auto node_description = _dot_node_visitor.info();
- os << R"([label = ")" << name << R"( \n )" << n->assigned_target() << R"( \n )" << node_description << R"("])";
+ os << R"([label = ")" << name << R"( \n )" << n->assigned_target() << R"( \n )" << node_description
+ << R"("])";
os << ";\n";
}
}
@@ -174,16 +175,17 @@
void DotGraphPrinter::print_edges(const Graph &g, std::ostream &os)
{
- for(const auto &e : g.edges())
+ for (const auto &e : g.edges())
{
- if(e)
+ if (e)
{
std::string source_node_id = std::string("n") + support::cpp11::to_string(e->producer_id());
std::string sink_node_id = std::string("n") + support::cpp11::to_string(e->consumer_id());
os << source_node_id << " -> " << sink_node_id << " ";
const Tensor *t = e->tensor();
ARM_COMPUTE_ERROR_ON(t == nullptr);
- os << R"([label = ")" << t->desc().shape << R"( \n )" << t->desc().data_type << R"( \n )" << t->desc().layout << R"("])";
+ os << R"([label = ")" << t->desc().shape << R"( \n )" << t->desc().data_type << R"( \n )"
+ << t->desc().layout << R"("])";
os << ";\n";
}
}