From cac13b1cfd593889271f8e2191be2039b8d88f36 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 27 Apr 2018 19:07:19 +0100 Subject: COMPMID-1097: Port mobilenet to NHWC Change-Id: I789065bfa0d4ef133388e1904c5caf31e450f80f Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129495 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- src/graph/GraphBuilder.cpp | 22 +++++++------ src/graph/GraphManager.cpp | 2 -- src/graph/INode.cpp | 5 +++ src/graph/Utils.cpp | 39 ++++++++++++++++++++--- src/graph/backends/CL/CLDeviceBackend.cpp | 3 +- src/graph/backends/GLES/GCDeviceBackend.cpp | 3 +- src/graph/backends/NEON/NEDeviceBackend.cpp | 3 +- src/graph/detail/ExecutionHelpers.cpp | 36 ++++++++++----------- src/graph/mutators/SplitLayerSubTensorMutator.cpp | 2 +- src/graph/nodes/ActivationLayerNode.cpp | 5 --- src/graph/nodes/BatchNormalizationLayerNode.cpp | 5 --- src/graph/nodes/ConstNode.cpp | 7 +--- src/graph/nodes/ConvolutionLayerNode.cpp | 33 +++++++++---------- src/graph/nodes/DepthConcatenateLayerNode.cpp | 38 +++++++++------------- src/graph/nodes/DepthwiseConvolutionLayerNode.cpp | 31 +++++++++--------- src/graph/nodes/EltwiseLayerNode.cpp | 5 --- src/graph/nodes/FlattenLayerNode.cpp | 5 --- src/graph/nodes/FullyConnectedLayer.cpp | 38 +++++++++++----------- src/graph/nodes/InputNode.cpp | 7 +--- src/graph/nodes/NormalizationLayerNode.cpp | 5 --- src/graph/nodes/OutputNode.cpp | 5 --- src/graph/nodes/PoolingLayerNode.cpp | 33 +++++++++---------- src/graph/nodes/ReshapeLayer.cpp | 5 --- src/graph/nodes/SoftmaxLayerNode.cpp | 5 --- src/graph/nodes/SplitLayerNode.cpp | 29 +++++++++-------- src/graph/printers/DotGraphPrinter.cpp | 2 +- 26 files changed, 179 insertions(+), 194 deletions(-) (limited to 'src/graph') diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp index 4ad34e789c..56b31c7844 100644 --- a/src/graph/GraphBuilder.cpp +++ b/src/graph/GraphBuilder.cpp @@ -63,7 +63,7 @@ Status set_accessor_on_node(Graph &g, NodeID nid, bool is_output, size_t idx, IT NodeID add_const_node_with_name(Graph &g, NodeParams params, const std::string &name, TensorDescriptor desc, ITensorAccessorUPtr accessor) { params.name = params.name.empty() ? "" : params.name + name; - auto nid = GraphBuilder::add_const_node(g, params, desc, std::move(accessor)); + auto nid = GraphBuilder::add_const_node(g, params, std::move(desc), std::move(accessor)); set_node_params(g, nid, params); return nid; } @@ -165,7 +165,7 @@ NodeID GraphBuilder::add_batch_normalization_node(Graph &g, NodeParams params, N // Calculate Common Descriptor TensorDescriptor common_desc = input_tensor_desc; - common_desc.shape = TensorShape(common_desc.shape.z()); + common_desc.shape = TensorShape(get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL)); // Create mean and nodes auto mean_nid = add_const_node_with_name(g, params, "Mean", common_desc, std::move(mean_accessor)); @@ -221,8 +221,11 @@ NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPa // Create weights node TensorDescriptor w_desc = input_tensor_desc; - w_desc.shape = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z() / num_groups, depth); - + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), kernel_spatial_extend.width); + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height); + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL), + get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL) / num_groups); + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::BATCHES), depth); if(!weights_quant_info.empty()) { w_desc.quant_info = weights_quant_info; @@ -290,8 +293,10 @@ NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, // Create weights node TensorDescriptor w_desc = input_tensor_desc; - w_desc.shape = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z()); - + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::WIDTH), kernel_spatial_extend.width); + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::HEIGHT), kernel_spatial_extend.height); + w_desc.shape.set(get_dimension_idx(input_tensor_desc, DataLayoutDimension::CHANNEL), + get_dimension_size(input_tensor_desc, DataLayoutDimension::CHANNEL)); if(!quant_info.empty()) { w_desc.quant_info = quant_info; @@ -353,9 +358,8 @@ NodeID GraphBuilder::add_fully_connected_layer(Graph &g, NodeParams params, Node const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]); // Create weights node - TensorDescriptor w_desc = input_tensor_desc; - w_desc.shape = FullyConnectedLayerNode::compute_weights_shape(input_tensor_desc.shape, num_outputs); - NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); + TensorDescriptor w_desc = FullyConnectedLayerNode::compute_weights_descriptor(input_tensor_desc, num_outputs); + NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); // Create bias nodes NodeID b_nid = EmptyNodeID; diff --git a/src/graph/GraphManager.cpp b/src/graph/GraphManager.cpp index fa7dfdf8f8..aac6488311 100644 --- a/src/graph/GraphManager.cpp +++ b/src/graph/GraphManager.cpp @@ -62,8 +62,6 @@ void GraphManager::finalize_graph(Graph &graph, GraphContext &ctx, PassManager & // Apply all mutating passes pm.run_all(graph); - // TODO (geopin01): Perform a graph validation - // Perform topological sort // FIXME : Sort nodes and pass sorted indices in configure all nodes diff --git a/src/graph/INode.cpp b/src/graph/INode.cpp index c1c18e5853..cd9a46ac40 100644 --- a/src/graph/INode.cpp +++ b/src/graph/INode.cpp @@ -42,6 +42,11 @@ INode::INode() // clang-format on // *INDENT-ON* +Status INode::validate() const +{ + return Status{}; +} + void INode::set_graph(Graph *g) { ARM_COMPUTE_ERROR_ON(g == nullptr); diff --git a/src/graph/Utils.cpp b/src/graph/Utils.cpp index 8537bbfb2a..030fa2df59 100644 --- a/src/graph/Utils.cpp +++ b/src/graph/Utils.cpp @@ -89,10 +89,6 @@ PassManager create_default_pass_manager(Target target) return pm; } -/** Default setups a graph Context - * - * @param[in] ctx Context to default initialize - */ void setup_default_graph_context(GraphContext &ctx) { for(const auto &backend : backends::BackendRegistry::get().backends()) @@ -100,5 +96,40 @@ void setup_default_graph_context(GraphContext &ctx) backend.second->setup_backend_context(ctx); } } + +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!"); + return descriptor.shape[get_dimension_idx(descriptor, data_layout_dimension)]; +} + +size_t get_dimension_idx(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!"); + + /* Return the index based on the data layout + * [N C H W] + * [3 2 1 0] + * [N H W C] + */ + switch(data_layout_dimension) + { + case DataLayoutDimension::CHANNEL: + return (descriptor.layout == DataLayout::NCHW) ? 2 : 0; + break; + case DataLayoutDimension::HEIGHT: + return (descriptor.layout == DataLayout::NCHW) ? 1 : 2; + break; + case DataLayoutDimension::WIDTH: + return (descriptor.layout == DataLayout::NCHW) ? 0 : 1; + break; + case DataLayoutDimension::BATCHES: + return 3; + break; + default: + ARM_COMPUTE_ERROR("Data layout index not supported!"); + break; + } +} } // namespace graph } // namespace arm_compute \ No newline at end of file diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp index 92cb6936c3..37cbcd72d7 100644 --- a/src/graph/backends/CL/CLDeviceBackend.cpp +++ b/src/graph/backends/CL/CLDeviceBackend.cpp @@ -127,7 +127,8 @@ std::unique_ptr CLDeviceBackend::create_tensor(const Tensor &tens // Create backend tensor handle TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); - auto backend_tensor_handle = support::cpp14::make_unique(info); + info.set_data_layout(tensor_desc.layout); + auto backend_tensor_handle = support::cpp14::make_unique(info); return std::move(backend_tensor_handle); } diff --git a/src/graph/backends/GLES/GCDeviceBackend.cpp b/src/graph/backends/GLES/GCDeviceBackend.cpp index a55215f058..0185598965 100644 --- a/src/graph/backends/GLES/GCDeviceBackend.cpp +++ b/src/graph/backends/GLES/GCDeviceBackend.cpp @@ -88,7 +88,8 @@ std::unique_ptr GCDeviceBackend::create_tensor(const Tensor &tens // Create backend tensor handle TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); - auto backend_tensor_handle = support::cpp14::make_unique(info); + info.set_data_layout(tensor_desc.layout); + auto backend_tensor_handle = support::cpp14::make_unique(info); return std::move(backend_tensor_handle); } diff --git a/src/graph/backends/NEON/NEDeviceBackend.cpp b/src/graph/backends/NEON/NEDeviceBackend.cpp index 9123196540..def6c39003 100644 --- a/src/graph/backends/NEON/NEDeviceBackend.cpp +++ b/src/graph/backends/NEON/NEDeviceBackend.cpp @@ -94,7 +94,8 @@ std::unique_ptr NEDeviceBackend::create_tensor(const Tensor &tens // Create backend tensor handle TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); - auto backend_tensor_handle = support::cpp14::make_unique(info); + info.set_data_layout(tensor_desc.layout); + auto backend_tensor_handle = support::cpp14::make_unique(info); return std::move(backend_tensor_handle); } diff --git a/src/graph/detail/ExecutionHelpers.cpp b/src/graph/detail/ExecutionHelpers.cpp index 0bb47f2b33..c1304436f6 100644 --- a/src/graph/detail/ExecutionHelpers.cpp +++ b/src/graph/detail/ExecutionHelpers.cpp @@ -43,6 +43,24 @@ void default_initialize_backends() } } +void validate_all_nodes(Graph &g) +{ + auto &nodes = g.nodes(); + + // Create tasks + for(auto &node : nodes) + { + if(node != nullptr) + { + Target assigned_target = node->assigned_target(); + auto backend = backends::BackendRegistry::get().find_backend(assigned_target); + ARM_COMPUTE_ERROR_ON_MSG(!backend, "Requested backend doesn't exist!"); + Status status = backend->validate_node(*node); + ARM_COMPUTE_ERROR_ON_MSG(!bool(status), status.error_description().c_str()); + } + } +} + void configure_all_tensors(Graph &g) { auto &tensors = g.tensors(); @@ -121,24 +139,6 @@ void allocate_all_tensors(Graph &g) } } -void validate_all_nodes(Graph &g) -{ - auto &nodes = g.nodes(); - - // Create tasks - for(auto &node : nodes) - { - if(node != nullptr) - { - Target assigned_target = node->assigned_target(); - auto backend = backends::BackendRegistry::get().find_backend(assigned_target); - ARM_COMPUTE_ERROR_ON_MSG(!backend, "Requested backend doesn't exist!"); - Status status = backend->validate_node(*node); - ARM_COMPUTE_ERROR_ON_MSG(!bool(status), status.error_description().c_str()); - } - } -} - ExecutionWorkload configure_all_nodes(Graph &g, GraphContext &ctx) { ExecutionWorkload workload; diff --git a/src/graph/mutators/SplitLayerSubTensorMutator.cpp b/src/graph/mutators/SplitLayerSubTensorMutator.cpp index 179a6c35fb..2a8c029843 100644 --- a/src/graph/mutators/SplitLayerSubTensorMutator.cpp +++ b/src/graph/mutators/SplitLayerSubTensorMutator.cpp @@ -75,7 +75,7 @@ void SplitLayerSubTensorMutator::mutate(Graph &g) Tensor *output_tensor = node->output(i); const TensorShape output_shape = output_tensor->desc().shape; Coordinates coords; - std::tie(std::ignore, coords) = SplitLayerNode::compute_output_shape(input_tensor->desc().shape, num_splits, axis, i); + std::tie(std::ignore, coords) = SplitLayerNode::compute_output_descriptor(input_tensor->desc(), num_splits, axis, i); backends::IDeviceBackend *backend = backends::BackendRegistry::get().find_backend(output_tensor->desc().target); std::unique_ptr handle = backend->create_subtensor(input_tensor->handle(), output_shape, coords, extend_parent); diff --git a/src/graph/nodes/ActivationLayerNode.cpp b/src/graph/nodes/ActivationLayerNode.cpp index 9996d2ce3f..414684cf30 100644 --- a/src/graph/nodes/ActivationLayerNode.cpp +++ b/src/graph/nodes/ActivationLayerNode.cpp @@ -65,11 +65,6 @@ TensorDescriptor ActivationLayerNode::configure_output(size_t idx) const return src->desc(); } -Status ActivationLayerNode::validate() -{ - return Status{}; -} - NodeType ActivationLayerNode::type() const { return NodeType::ActivationLayer; diff --git a/src/graph/nodes/BatchNormalizationLayerNode.cpp b/src/graph/nodes/BatchNormalizationLayerNode.cpp index f7b041c828..3ae11fc24d 100644 --- a/src/graph/nodes/BatchNormalizationLayerNode.cpp +++ b/src/graph/nodes/BatchNormalizationLayerNode.cpp @@ -76,11 +76,6 @@ TensorDescriptor BatchNormalizationLayerNode::configure_output(size_t idx) const return src->desc(); } -Status BatchNormalizationLayerNode::validate() -{ - return Status{}; -} - NodeType BatchNormalizationLayerNode::type() const { return NodeType::BatchNormalizationLayer; diff --git a/src/graph/nodes/ConstNode.cpp b/src/graph/nodes/ConstNode.cpp index 631971c98f..2f3cd142af 100644 --- a/src/graph/nodes/ConstNode.cpp +++ b/src/graph/nodes/ConstNode.cpp @@ -31,7 +31,7 @@ namespace arm_compute namespace graph { ConstNode::ConstNode(TensorDescriptor desc) - : _desc(desc) + : _desc(std::move(desc)) { _outputs.resize(1, NullTensorID); } @@ -54,11 +54,6 @@ TensorDescriptor ConstNode::configure_output(size_t idx) const return _desc; } -Status ConstNode::validate() -{ - return Status{}; -} - NodeType ConstNode::type() const { return NodeType::Const; diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp index eb0c6a1c1a..eaf1f7f035 100644 --- a/src/graph/nodes/ConvolutionLayerNode.cpp +++ b/src/graph/nodes/ConvolutionLayerNode.cpp @@ -26,6 +26,7 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/graph/Graph.h" #include "arm_compute/graph/INodeVisitor.h" +#include "arm_compute/graph/Utils.h" namespace arm_compute { @@ -53,18 +54,26 @@ PadStrideInfo ConvolutionLayerNode::convolution_info() const return _info; } -TensorShape ConvolutionLayerNode::compute_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo info) +TensorDescriptor ConvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + const TensorDescriptor &weights_descriptor, + const PadStrideInfo &info) { unsigned int output_width = 0; unsigned int output_height = 0; - std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), info); - TensorShape output_shape{ input_shape }; - output_shape.set(0, output_width); - output_shape.set(1, output_height); - output_shape.set(2, weights_shape[3]); + const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH); + const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT); + const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH); + const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT); - return output_shape; + 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, DataLayoutDimension::WIDTH), output_width); + output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), output_height); + output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::CHANNEL), weights_descriptor.shape[3]); + + return output_descriptor; } bool ConvolutionLayerNode::forward_descriptors() @@ -87,10 +96,7 @@ TensorDescriptor ConvolutionLayerNode::configure_output(size_t idx) const ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr); - TensorDescriptor output_info = src->desc(); - TensorShape output_shape = compute_output_shape(src->desc().shape, weights->desc().shape, _info); - output_info.shape = output_shape; - + TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info); if(!_out_quant_info.empty()) { output_info.quant_info = _out_quant_info; @@ -99,11 +105,6 @@ TensorDescriptor ConvolutionLayerNode::configure_output(size_t idx) const return output_info; } -Status ConvolutionLayerNode::validate() -{ - return Status{}; -} - NodeType ConvolutionLayerNode::type() const { return NodeType::ConvolutionLayer; diff --git a/src/graph/nodes/DepthConcatenateLayerNode.cpp b/src/graph/nodes/DepthConcatenateLayerNode.cpp index 1c0539744f..08cccc1ff1 100644 --- a/src/graph/nodes/DepthConcatenateLayerNode.cpp +++ b/src/graph/nodes/DepthConcatenateLayerNode.cpp @@ -34,7 +34,7 @@ namespace graph DepthConcatenateLayerNode::DepthConcatenateLayerNode(unsigned int total_nodes) : _total_nodes(total_nodes), _is_enabled(true) { - _input_edges.resize(total_nodes, EmptyEdgeID); + _input_edges.resize(_total_nodes, EmptyEdgeID); _outputs.resize(1, NullTensorID); } @@ -48,28 +48,28 @@ bool DepthConcatenateLayerNode::is_enabled() const return _is_enabled; } -TensorShape DepthConcatenateLayerNode::compute_output_shape(const std::vector &input_shapes) +TensorDescriptor DepthConcatenateLayerNode::compute_output_descriptor(const std::vector &input_descriptors) { - ARM_COMPUTE_ERROR_ON(input_shapes.size() == 0); + ARM_COMPUTE_ERROR_ON(input_descriptors.size() == 0); - TensorShape output_shape = input_shapes[0]; + TensorDescriptor output_descriptor = input_descriptors[0]; size_t max_x = 0; size_t max_y = 0; size_t depth = 0; - for(const auto &shape : input_shapes) + for(const auto &input_descriptor : input_descriptors) { - max_x = std::max(shape.x(), max_x); - max_y = std::max(shape.y(), max_y); - depth += shape.z(); + max_x = std::max(input_descriptor.shape.x(), max_x); + max_y = std::max(input_descriptor.shape.y(), max_y); + depth += input_descriptor.shape.z(); } - output_shape.set(0, max_x); - output_shape.set(1, max_y); - output_shape.set(2, depth); + output_descriptor.shape.set(0, max_x); + output_descriptor.shape.set(1, max_y); + output_descriptor.shape.set(2, depth); - return output_shape; + return output_descriptor; } bool DepthConcatenateLayerNode::forward_descriptors() @@ -99,27 +99,19 @@ TensorDescriptor DepthConcatenateLayerNode::configure_output(size_t idx) const if(are_all_inputs_set) { - std::vector inputs_shapes; + std::vector inputs_descriptors; 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_shapes.push_back(t->desc().shape); + inputs_descriptors.push_back(t->desc()); } - output_info = input(0)->desc(); - TensorShape output_shape = compute_output_shape(inputs_shapes); - output_info.shape = output_shape; + output_info = compute_output_descriptor(inputs_descriptors); } return output_info; } -Status DepthConcatenateLayerNode::validate() -{ - ARM_COMPUTE_UNUSED(_total_nodes); - return Status{}; -} - NodeType DepthConcatenateLayerNode::type() const { return NodeType::DepthConcatenateLayer; diff --git a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp index 67a39029e6..1a6f8d398d 100644 --- a/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp +++ b/src/graph/nodes/DepthwiseConvolutionLayerNode.cpp @@ -26,6 +26,7 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/graph/Graph.h" #include "arm_compute/graph/INodeVisitor.h" +#include "arm_compute/graph/Utils.h" namespace arm_compute { @@ -53,17 +54,25 @@ PadStrideInfo DepthwiseConvolutionLayerNode::convolution_info() const return _info; } -TensorShape DepthwiseConvolutionLayerNode::compute_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo info) +TensorDescriptor DepthwiseConvolutionLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + const TensorDescriptor &weights_descriptor, + const PadStrideInfo &info) { unsigned int output_width = 0; unsigned int output_height = 0; - std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), info); - TensorShape output_shape{ input_shape }; - output_shape.set(0, output_width); - output_shape.set(1, output_height); + const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH); + const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT); + const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH); + const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT); - return output_shape; + 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, DataLayoutDimension::WIDTH), output_width); + output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), output_height); + + return output_descriptor; } bool DepthwiseConvolutionLayerNode::forward_descriptors() @@ -86,15 +95,7 @@ TensorDescriptor DepthwiseConvolutionLayerNode::configure_output(size_t idx) con ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr); - TensorDescriptor output_info = src->desc(); - TensorShape output_shape = compute_output_shape(src->desc().shape, weights->desc().shape, _info); - output_info.shape = output_shape; - return output_info; -} - -Status DepthwiseConvolutionLayerNode::validate() -{ - return Status{}; + return compute_output_descriptor(src->desc(), weights->desc(), _info); } NodeType DepthwiseConvolutionLayerNode::type() const diff --git a/src/graph/nodes/EltwiseLayerNode.cpp b/src/graph/nodes/EltwiseLayerNode.cpp index b794043f2f..6f1e0eecd9 100644 --- a/src/graph/nodes/EltwiseLayerNode.cpp +++ b/src/graph/nodes/EltwiseLayerNode.cpp @@ -65,11 +65,6 @@ TensorDescriptor EltwiseLayerNode::configure_output(size_t idx) const return src->desc(); } -Status EltwiseLayerNode::validate() -{ - return Status{}; -} - NodeType EltwiseLayerNode::type() const { return NodeType::EltwiseLayer; diff --git a/src/graph/nodes/FlattenLayerNode.cpp b/src/graph/nodes/FlattenLayerNode.cpp index 8b847c7056..78b45dc305 100644 --- a/src/graph/nodes/FlattenLayerNode.cpp +++ b/src/graph/nodes/FlattenLayerNode.cpp @@ -62,11 +62,6 @@ TensorDescriptor FlattenLayerNode::configure_output(size_t idx) const return output_desc; } -Status FlattenLayerNode::validate() -{ - return Status{}; -} - NodeType FlattenLayerNode::type() const { return NodeType::FlattenLayer; diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp index cbf2b35ddd..d94a7851ff 100644 --- a/src/graph/nodes/FullyConnectedLayer.cpp +++ b/src/graph/nodes/FullyConnectedLayer.cpp @@ -38,10 +38,11 @@ FullyConnectedLayerNode::FullyConnectedLayerNode(unsigned int num_outputs) _outputs.resize(1, NullTensorID); } -TensorShape FullyConnectedLayerNode::compute_weights_shape(TensorShape input_shape, unsigned int num_outputs) +TensorDescriptor FullyConnectedLayerNode::compute_weights_descriptor(const TensorDescriptor &input_descriptor, + unsigned int num_outputs) { unsigned int num_weights = 1; - unsigned int num_dimensions = input_shape.num_dimensions(); + unsigned int num_dimensions = input_descriptor.shape.num_dimensions(); // Ignore the batch dimension if there is one: if(num_dimensions == 2 || num_dimensions == 4) { @@ -49,20 +50,29 @@ TensorShape FullyConnectedLayerNode::compute_weights_shape(TensorShape input_sha } for(unsigned int i = 0; i < num_dimensions; i++) { - num_weights *= input_shape[i]; + num_weights *= input_descriptor.shape[i]; } - return TensorShape(num_weights, num_outputs); + + TensorDescriptor weights_descriptor = input_descriptor; + weights_descriptor.shape = TensorShape(num_weights, num_outputs); + + return weights_descriptor; } -TensorShape FullyConnectedLayerNode::compute_output_shape(TensorShape input_shape, unsigned int num_outputs) +TensorDescriptor FullyConnectedLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + unsigned int num_outputs) { // Note: Only 1D batch space is supported at the moment - unsigned int batches = input_shape[1]; - if(input_shape.num_dimensions() > 2) + unsigned int batches = input_descriptor.shape[1]; + if(input_descriptor.shape.num_dimensions() > 2) { - batches = input_shape[3]; + batches = input_descriptor.shape[3]; } - return TensorShape(num_outputs, batches); + + TensorDescriptor output_descriptor = input_descriptor; + output_descriptor.shape = TensorShape(num_outputs, batches); + + return output_descriptor; } bool FullyConnectedLayerNode::forward_descriptors() @@ -83,15 +93,7 @@ TensorDescriptor FullyConnectedLayerNode::configure_output(size_t idx) const const Tensor *src = input(0); ARM_COMPUTE_ERROR_ON(src == nullptr); - TensorDescriptor output_info = src->desc(); - TensorShape output_shape = compute_output_shape(src->desc().shape, _num_outputs); - output_info.shape = output_shape; - return output_info; -} - -Status FullyConnectedLayerNode::validate() -{ - return Status{}; + return compute_output_descriptor(src->desc(), _num_outputs); } NodeType FullyConnectedLayerNode::type() const diff --git a/src/graph/nodes/InputNode.cpp b/src/graph/nodes/InputNode.cpp index e912633a66..709eaae14c 100644 --- a/src/graph/nodes/InputNode.cpp +++ b/src/graph/nodes/InputNode.cpp @@ -31,7 +31,7 @@ namespace arm_compute namespace graph { InputNode::InputNode(TensorDescriptor desc) - : _desc(desc) + : _desc(std::move(desc)) { _outputs.resize(1, NullTensorID); } @@ -54,11 +54,6 @@ TensorDescriptor InputNode::configure_output(size_t idx) const return _desc; } -Status InputNode::validate() -{ - return Status{}; -} - NodeType InputNode::type() const { return NodeType::Input; diff --git a/src/graph/nodes/NormalizationLayerNode.cpp b/src/graph/nodes/NormalizationLayerNode.cpp index a9f2fbd066..a7b373860e 100644 --- a/src/graph/nodes/NormalizationLayerNode.cpp +++ b/src/graph/nodes/NormalizationLayerNode.cpp @@ -66,11 +66,6 @@ TensorDescriptor NormalizationLayerNode::configure_output(size_t idx) const return src->desc(); } -Status NormalizationLayerNode::validate() -{ - return Status{}; -} - NodeType NormalizationLayerNode::type() const { return NodeType::NormalizationLayer; diff --git a/src/graph/nodes/OutputNode.cpp b/src/graph/nodes/OutputNode.cpp index 4c63bfa20c..8aa249bc2a 100644 --- a/src/graph/nodes/OutputNode.cpp +++ b/src/graph/nodes/OutputNode.cpp @@ -48,11 +48,6 @@ TensorDescriptor OutputNode::configure_output(size_t idx) const return TensorDescriptor(); } -Status OutputNode::validate() -{ - return Status{}; -} - NodeType OutputNode::type() const { return NodeType::Output; diff --git a/src/graph/nodes/PoolingLayerNode.cpp b/src/graph/nodes/PoolingLayerNode.cpp index a7b6b3679a..26c145ae31 100644 --- a/src/graph/nodes/PoolingLayerNode.cpp +++ b/src/graph/nodes/PoolingLayerNode.cpp @@ -26,6 +26,7 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/graph/Graph.h" #include "arm_compute/graph/INodeVisitor.h" +#include "arm_compute/graph/Utils.h" namespace arm_compute { @@ -43,20 +44,24 @@ PoolingLayerInfo PoolingLayerNode::pooling_info() const return _info; } -TensorShape PoolingLayerNode::compute_output_shape(TensorShape input_shape, PoolingLayerInfo info) +TensorDescriptor PoolingLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + PoolingLayerInfo info) { - const int pool_size_x = info.is_global_pooling() ? input_shape.x() : info.pool_size().width; - const int pool_size_y = info.is_global_pooling() ? input_shape.y() : info.pool_size().height; - unsigned int pooled_width = 0; unsigned int pooled_height = 0; - std::tie(pooled_width, pooled_height) = scaled_dimensions(input_shape.x(), input_shape.y(), pool_size_x, pool_size_y, info.pad_stride_info()); - TensorShape output_shape{ input_shape }; - output_shape.set(0, pooled_width); - output_shape.set(1, pooled_height); + const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH); + const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT); + 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()); + + TensorDescriptor output_descriptor = input_descriptor; + output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::WIDTH), pooled_width); + output_descriptor.shape.set(get_dimension_idx(output_descriptor, DataLayoutDimension::HEIGHT), pooled_height); - return output_shape; + return output_descriptor; } bool PoolingLayerNode::forward_descriptors() @@ -79,15 +84,7 @@ TensorDescriptor PoolingLayerNode::configure_output(size_t idx) const const Tensor *src = input(0); ARM_COMPUTE_ERROR_ON(src == nullptr); - TensorDescriptor output_info = src->desc(); - TensorShape output_shape = compute_output_shape(src->desc().shape, _info); - output_info.shape = output_shape; - return output_info; -} - -Status PoolingLayerNode::validate() -{ - return Status{}; + return compute_output_descriptor(src->desc(), _info); } NodeType PoolingLayerNode::type() const diff --git a/src/graph/nodes/ReshapeLayer.cpp b/src/graph/nodes/ReshapeLayer.cpp index 2757f06bd3..58610e9b1c 100644 --- a/src/graph/nodes/ReshapeLayer.cpp +++ b/src/graph/nodes/ReshapeLayer.cpp @@ -63,11 +63,6 @@ TensorDescriptor ReshapeLayerNode::configure_output(size_t idx) const return output_desc; } -Status ReshapeLayerNode::validate() -{ - return Status{}; -} - NodeType ReshapeLayerNode::type() const { return NodeType::ReshapeLayer; diff --git a/src/graph/nodes/SoftmaxLayerNode.cpp b/src/graph/nodes/SoftmaxLayerNode.cpp index b6241e6654..57e556160f 100644 --- a/src/graph/nodes/SoftmaxLayerNode.cpp +++ b/src/graph/nodes/SoftmaxLayerNode.cpp @@ -69,11 +69,6 @@ TensorDescriptor SoftmaxLayerNode::configure_output(size_t idx) const return out_desc; } -Status SoftmaxLayerNode::validate() -{ - return Status{}; -} - NodeType SoftmaxLayerNode::type() const { return NodeType::SoftmaxLayer; diff --git a/src/graph/nodes/SplitLayerNode.cpp b/src/graph/nodes/SplitLayerNode.cpp index c8fb43c2a1..5d46c9dcc9 100644 --- a/src/graph/nodes/SplitLayerNode.cpp +++ b/src/graph/nodes/SplitLayerNode.cpp @@ -48,26 +48,25 @@ unsigned int SplitLayerNode::axis() const return _axis; } -std::pair SplitLayerNode::compute_output_shape(TensorShape input_shape, unsigned int num_splits, unsigned int axis, unsigned int idx) +std::pair SplitLayerNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + unsigned int num_splits, unsigned int axis, unsigned int idx) { - ARM_COMPUTE_ERROR_ON(axis >= input_shape.num_dimensions()); - ARM_COMPUTE_ERROR_ON_MSG(input_shape[axis] % num_splits, "Split should be exact"); + const unsigned int split_size = input_descriptor.shape[axis] / num_splits; - const unsigned int split_size = input_shape[axis] / num_splits; - - TensorShape output_shape = input_shape; - output_shape.set(axis, split_size); + TensorDescriptor output_descriptor = input_descriptor; + output_descriptor.shape.set(axis, split_size); Coordinates coords; coords.set(axis, idx * split_size); - return std::make_pair(output_shape, coords); + return std::make_pair(output_descriptor, coords); } bool SplitLayerNode::forward_descriptors() { if(input_id(0) != NullTensorID) { + validate(); for(unsigned int i = 0; i < _outputs.size(); ++i) { if(output_id(i) != NullTensorID) @@ -90,17 +89,19 @@ TensorDescriptor SplitLayerNode::configure_output(size_t idx) const const Tensor *src = input(0); ARM_COMPUTE_ERROR_ON(src == nullptr); - TensorShape output_shape; - - TensorDescriptor output_info = src->desc(); - std::tie(output_shape, std::ignore) = compute_output_shape(src->desc().shape, _num_splits, _axis, idx); - output_info.shape = output_shape; + TensorDescriptor output_info; + std::tie(output_info, std::ignore) = compute_output_descriptor(src->desc(), _num_splits, _axis, idx); return output_info; } -Status SplitLayerNode::validate() +Status SplitLayerNode::validate() const { + const Tensor *src = input(0); + ARM_COMPUTE_RETURN_ERROR_ON(src == nullptr); + ARM_COMPUTE_RETURN_ERROR_ON(_axis >= src->desc().shape.num_dimensions()); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->desc().shape[_axis] % _num_splits, "Split should be exact"); + return Status{}; } diff --git a/src/graph/printers/DotGraphPrinter.cpp b/src/graph/printers/DotGraphPrinter.cpp index 47b1bb56bf..61cf42356f 100644 --- a/src/graph/printers/DotGraphPrinter.cpp +++ b/src/graph/printers/DotGraphPrinter.cpp @@ -164,7 +164,7 @@ void DotGraphPrinter::print_edges(const Graph &g, std::ostream &os) 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"("])"; + os << R"([label = ")" << t->desc().shape << R"( \n )" << t->desc().data_type << R"( \n )" << t->desc().layout << R"("])"; os << ";\n"; } } -- cgit v1.2.1