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-rw-r--r--src/armnnCaffeParser/CaffeParser.cpp2271
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diff --git a/src/armnnCaffeParser/CaffeParser.cpp b/src/armnnCaffeParser/CaffeParser.cpp
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--- a/src/armnnCaffeParser/CaffeParser.cpp
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@@ -1,2271 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include "CaffeParser.hpp"
-#include "RecordByRecordCaffeParser.hpp"
-
-#include "armnnCaffeParser/Version.hpp"
-
-#include "armnn/Descriptors.hpp"
-#include "armnn/INetwork.hpp"
-#include "armnn/Utils.hpp"
-#include "armnn/Exceptions.hpp"
-
-#include "GraphTopologicalSort.hpp"
-#include "VerificationHelpers.hpp"
-
-#include <armnn/utility/Assert.hpp>
-#include <armnn/utility/NumericCast.hpp>
-
-#include <fmt/format.h>
-
-// Caffe
-#include "caffe/proto/caffe.pb.h"
-
-// ProtoBuf
-#include <google/protobuf/io/coded_stream.h>
-#include <google/protobuf/io/zero_copy_stream.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
-#include <google/protobuf/text_format.h>
-#include <google/protobuf/stubs/common.h>
-#include <google/protobuf/stubs/once.h>
-#include <google/protobuf/io/coded_stream.h>
-#include <google/protobuf/descriptor.h>
-#include <google/protobuf/generated_message_reflection.h>
-#include <google/protobuf/reflection_ops.h>
-#include <google/protobuf/wire_format.h>
-
-#include <cmath>
-#include <iostream>
-#include <sstream>
-#include <queue>
-#include <fcntl.h>
-
-/// Caffe networks are loaded from protobuf files (binary or text) using the protobuf library and the generated
-/// code from caffe.pb.h. This gives us a caffe::NetParameter which is an in-memory version of the file.
-/// This contains a flat list of Caffe 'layers' (e.g. convolution, pooling etc.).
-/// Each layer has inputs (called "bottoms") and outputs (called "tops"). Data flows from bottom to top.
-/// The bottoms of a layer refer to the tops of other layers, not their names.
-/// The names of layers seem to be arbitrary (you could rename a layer and the network wouldn't
-/// need any other changes).
-///
-/// Some layers (e.g. Relu) can be configured so that their top and bottom are both the same. This is called an
-/// "in-place" layer and is a Caffe runtime feature used to reduce memory usage by modifying tensors in-place.
-/// This isn't relevant to the parser and so we preprocess these layers to convert them to regular layers, to result
-/// in a consistent graph structure.
-
-namespace armnnCaffeParser
-{
-
-using namespace armnn;
-using namespace caffe;
-using namespace std;
-using namespace google::protobuf::io;
-
-ICaffeParser::ICaffeParser() : pCaffeParserImpl(new RecordByRecordCaffeParser()) {}
-
-ICaffeParser::~ICaffeParser() = default;
-
-ICaffeParser* ICaffeParser::CreateRaw()
-{
- return new ICaffeParser();
-}
-
-ICaffeParserPtr ICaffeParser::Create()
-{
- return ICaffeParserPtr(CreateRaw(), &ICaffeParser::Destroy);
-}
-
-void ICaffeParser::Destroy(ICaffeParser* parser)
-{
- delete parser;
-}
-
-armnn::INetworkPtr ICaffeParser::CreateNetworkFromTextFile(
- const char* graphFile,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- return pCaffeParserImpl->CreateNetworkFromTextFile(graphFile, inputShapes, requestedOutputs);
-}
-
-armnn::INetworkPtr ICaffeParser::CreateNetworkFromBinaryFile(
- const char* graphFile,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- return pCaffeParserImpl->CreateNetworkFromBinaryFile(graphFile, inputShapes,requestedOutputs);
-}
-
-armnn::INetworkPtr ICaffeParser::CreateNetworkFromString(
- const char* protoText,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- return pCaffeParserImpl->CreateNetworkFromString(protoText, inputShapes, requestedOutputs);
-}
-
-BindingPointInfo ICaffeParser::GetNetworkInputBindingInfo(const std::string& name) const
-{
- return pCaffeParserImpl->GetNetworkInputBindingInfo(name);
-}
-
-BindingPointInfo ICaffeParser::GetNetworkOutputBindingInfo(const std::string& name) const
-{
- return pCaffeParserImpl->GetNetworkOutputBindingInfo(name);
-}
-
-namespace
-{
-
-const float* GetArrayPtrFromBlob(const LayerParameter& layerParam, unsigned int blobIndex)
-{
- auto nBlobs = layerParam.blobs_size();
- if (blobIndex >= armnn::numeric_cast<unsigned int>(nBlobs))
- {
- throw ParseException(
- fmt::format("Expected data blob at index {} in layer {} not found. nBlobs={}. {}",
- blobIndex,
- layerParam.name(),
- nBlobs,
- CHECK_LOCATION().AsString()));
- }
-
- const BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(blobIndex));
-
- const float* arrayPtr = blob.data().data();
- return arrayPtr;
-}
-
-void GetDataFromBlob(const LayerParameter& layerParam, vector<float>& outData, unsigned int blobIndex)
-{
- auto nBlobs = layerParam.blobs_size();
- if (blobIndex >= armnn::numeric_cast<unsigned int>(nBlobs))
- {
- throw ParseException(
- fmt::format("Expected data blob at index {} in layer {} not found. {}",
- blobIndex,
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- const BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(blobIndex));
-
- size_t blobSize = armnn::numeric_cast<size_t>(blob.data_size());
- if (blobSize != outData.size())
- {
- throw ParseException(
- fmt::format("Data blob at index {} in layer {} has an unexpected size. "
- "Expected {} elements but got {} elements. {}",
- blobIndex,
- layerParam.name(),
- outData.size(),
- blobSize,
- CHECK_LOCATION().AsString()));
- }
-
- int outSizeInt = armnn::numeric_cast<int>(outData.size());
- for (int i = 0; i < outSizeInt; ++i)
- {
- outData[static_cast<size_t>(i)] = blob.data(i);
- }
-}
-
-template <typename T>
-size_t SizeOfVectorData(const vector<T>& vec)
-{
- return vec.size() * sizeof(T);
-}
-
-void ValidateNumInputsOutputs(const caffe::LayerParameter& layerParameter,
- unsigned int numInputs,
- unsigned int numOutputs)
-{
- int numInputsActual = layerParameter.bottom_size();
- if (numInputs != armnn::numeric_cast<unsigned int>(numInputsActual))
- {
- throw ParseException(
- fmt::format("Invalid number of inputs requested {} for layer {} "
- "while only {} present. {}",
- numInputs,
- layerParameter.name(),
- numInputsActual,
- CHECK_LOCATION().AsString()));
- }
-
- int numOutputsActual = layerParameter.top_size();
- if (numOutputs != armnn::numeric_cast<unsigned int>(numOutputsActual))
- {
- throw ParseException(
- fmt::format("Invalid number of outputs requested {} for layer {} "
- "while only {} present. {}",
- numOutputs,
- layerParameter.name(),
- numOutputsActual,
- CHECK_LOCATION().AsString()));
- }
-}
-
-template <typename ParamType, typename ExtractOptional, typename ExtractFallback, typename ValueType>
-ValueType GetOptionalWithFallback(const ParamType& param,
- ExtractOptional extractOptional,
- ExtractFallback extractFallback,
- ValueType defaultValue)
-{
- auto optValue = extractOptional(param, defaultValue);
- if (optValue.first)
- {
- return optValue.second;
- }
- auto fallbackValue = extractFallback(param, defaultValue);
- return fallbackValue.second;
-}
-
-#define GET_OPTIONAL_WITH_VECTOR_FALLBACK(PARAM, \
- PARAM_TYPE, \
- OPTIONAL_VALUE, \
- FALLBACK_VECTOR, \
- VALUE_TYPE, \
- DEFAULT_VALUE) \
- GetOptionalWithFallback( \
- PARAM, \
- [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \
- { \
- if (param.has_##OPTIONAL_VALUE ()) \
- { \
- return std::make_pair(true, param.OPTIONAL_VALUE ()); \
- } \
- else \
- { \
- return std::make_pair(false, defaultValue); \
- } \
- }, \
- [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \
- { \
- if (param.FALLBACK_VECTOR##_size() > 0) \
- { \
- return std::make_pair(true, (param.FALLBACK_VECTOR ()).Get(0)); \
- } \
- else \
- { \
- return std::make_pair(false, defaultValue); \
- } \
- }, \
- DEFAULT_VALUE)
-
-#define GET_OPTIONAL_WITH_FALLBACK(PARAM, \
- PARAM_TYPE, \
- OPTIONAL_VALUE, \
- FALLBACK_VALUE, \
- VALUE_TYPE, \
- DEFAULT_VALUE) \
- GetOptionalWithFallback( \
- PARAM, \
- [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \
- { \
- if (param.has_##OPTIONAL_VALUE ()) \
- { \
- return std::make_pair(true, param.OPTIONAL_VALUE ()); \
- } \
- else \
- { \
- return std::make_pair(false, defaultValue); \
- } \
- }, \
- [](const PARAM_TYPE & param, VALUE_TYPE defaultValue) \
- { \
- if (param.has_##FALLBACK_VALUE ()) \
- { \
- return std::make_pair(true, param.FALLBACK_VALUE ()); \
- } \
- else \
- { \
- return std::make_pair(false, defaultValue); \
- } \
- }, \
- DEFAULT_VALUE)
-
-} // namespace <anonymous>
-
-const std::map<std::string, ICaffeParser::CaffeParserImpl::OperationParsingFunction>
- ICaffeParser::CaffeParserImpl::ms_CaffeLayerNameToParsingFunctions = {
- { "Input", &CaffeParserImpl::ParseInputLayer },
- { "Convolution", &CaffeParserImpl::ParseConvLayer },
- { "Deconvolution",&CaffeParserImpl::ParseDeconvLayer },
- { "Pooling", &CaffeParserImpl::ParsePoolingLayer },
- { "ReLU", &CaffeParserImpl::ParseReluLayer },
- { "LRN", &CaffeParserImpl::ParseLRNLayer },
- { "InnerProduct", &CaffeParserImpl::ParseInnerProductLayer },
- { "Softmax", &CaffeParserImpl::ParseSoftmaxLayer },
- { "Eltwise", &CaffeParserImpl::ParseEltwiseLayer },
- { "Concat", &CaffeParserImpl::ParseConcatLayer },
- { "BatchNorm", &CaffeParserImpl::ParseBatchNormLayer },
- { "Scale", &CaffeParserImpl::ParseScaleLayer },
- { "Split", &CaffeParserImpl::ParseSplitLayer },
- { "Dropout", &CaffeParserImpl::ParseDropoutLayer},
- { "ArgMax", &CaffeParserImpl::ParseArgmaxLayer},
-};
-
-ICaffeParser::CaffeParserImpl::CaffeParserImpl()
- : m_Network(nullptr, nullptr)
-{
-
-}
-
-CaffeParser::CaffeParser()
-: CaffeParserImpl()
-{
-
-}
-
-BindingPointInfo ICaffeParser::CaffeParserImpl::GetNetworkInputBindingInfo(const std::string& name) const
-{
- return GetBindingInfo(name, "input", m_NetworkInputsBindingInfo);
-}
-
-BindingPointInfo ICaffeParser::CaffeParserImpl::GetNetworkOutputBindingInfo(const std::string& name) const
-{
- return GetBindingInfo(name, "output", m_NetworkOutputsBindingInfo);
-}
-
-std::pair<armnn::LayerBindingId, armnn::TensorInfo> ICaffeParser::CaffeParserImpl::GetBindingInfo(
- const std::string& layerName,
- const char* bindingPointDesc,
- const std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)
-{
- auto it = nameToBindingInfo.find(layerName);
- if (it == nameToBindingInfo.end())
- {
- throw InvalidArgumentException(
- fmt::format("Unknown binding {} for layer '{}'. {}",
- bindingPointDesc,
- layerName,
- CHECK_LOCATION().AsString()));
- }
- return it->second;
-}
-
-TensorInfo ICaffeParser::CaffeParserImpl::BlobShapeToTensorInfo(const caffe::BlobShape& blobShape) const
-{
- std::vector<unsigned int> shape;
- for (int j = 0; j < blobShape.dim_size(); ++j)
- {
- shape.push_back(static_cast<unsigned int>(blobShape.dim(j)));
- }
-
- return TensorInfo(armnn::numeric_cast<unsigned int>(shape.size()), shape.data(), DataType::Float32);
-}
-
-BlobShape TensorDescToBlobShape(const TensorInfo& desc)
-{
- BlobShape ret;
- for (unsigned int i = 0; i < desc.GetNumDimensions(); ++i)
- {
- ret.add_dim(i);
- ret.set_dim(armnn::numeric_cast<int>(i), desc.GetShape()[i]);
- }
-
- return ret;
-}
-
-// Note: can move to CaffeParser when/if we optimise the text/string format
-// to load on a layer by layer basis
-vector<const LayerParameter*> ICaffeParser::CaffeParserImpl::GetInputs(const LayerParameter& layerParam)
-{
- std::vector<const caffe::LayerParameter*> ret;
- ret.reserve(armnn::numeric_cast<size_t>(layerParam.bottom_size()));
- for (int j = 0; j < layerParam.bottom_size(); ++j)
- {
- std::string inputName = layerParam.bottom(j);
- auto inputIt = m_CaffeLayersByTopName.find(inputName);
- if (inputIt == m_CaffeLayersByTopName.end())
- {
- throw ParseException(
- fmt::format("Can't find Caffe layer with top called '{}', "
- "which is listed as an input of '{}'. {}",
- inputName,
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- ret.push_back(inputIt->second);
- }
-
- return ret;
-}
-
-void ICaffeParser::CaffeParserImpl::ParseInputLayer(const LayerParameter& layerParam)
-{
- ARMNN_ASSERT(layerParam.type() == "Input");
- ValidateNumInputsOutputs(layerParam, 0, 1);
-
- const InputParameter& param = layerParam.input_param();
-
- const armnn::LayerBindingId inputId = armnn::numeric_cast<armnn::LayerBindingId>(
- m_NetworkInputsBindingInfo.size());
- armnn::IConnectableLayer* const inputLayer = m_Network->AddInputLayer(inputId, layerParam.name().c_str());
-
- // Decides the tensor info for this input. This can be specified in the Caffe network but can also
- // be overriden by user input (m_inputShapes).
- armnn::TensorInfo inputTensorInfo;
-
- const BlobShape* originalShape = param.shape_size() > 0 && param.shape(0).dim_size() > 0 ?
- &param.shape(0) : nullptr;
- if (originalShape)
- {
- inputTensorInfo = BlobShapeToTensorInfo(*originalShape);
- }
-
- auto overrideIt = m_InputShapes.find(layerParam.name());
- if (overrideIt != m_InputShapes.end())
- {
- const TensorShape& overrideShape = overrideIt->second;
- if (originalShape &&
- ( originalShape->dim(1) != overrideShape[1]
- || originalShape->dim(2) != overrideShape[2]
- || originalShape->dim(3) != overrideShape[3]))
- {
- throw ParseException(
- fmt::format("Parsed input shape for '{}' is incompatible with the override provided. {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- inputTensorInfo.SetShape(overrideShape);
- }
- else if (!originalShape)
- {
- throw ParseException(
- fmt::format("No input descriptor given for '{}' and no input shape found in caffe model. {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- TrackInputBinding(inputLayer, inputId, inputTensorInfo);
- inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), inputLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::AddConvLayerWithSplits(const caffe::LayerParameter& layerParam,
- const armnn::Convolution2dDescriptor& desc,
- unsigned int kernelW,
- unsigned int kernelH)
-{
- ARMNN_ASSERT(layerParam.type() == "Convolution");
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- ConvolutionParameter convParam = layerParam.convolution_param();
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
- const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1;
-
- // asusme these were already verified by the caller ParseConvLayer() function
- ARMNN_ASSERT(numGroups < inputShape.dim(1));
- ARMNN_ASSERT(numGroups > 1);
-
- // Handle grouping
- armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
-
- vector<string> convLayerNames(numGroups);
- vector<armnn::IConnectableLayer*> convLayers(numGroups);
- convLayerNames[0] = layerParam.name();
-
- // This convolution is to be applied to chunks of the input data so add a splitter layer
-
- // Redirect the convolution input to the splitter
- unsigned int splitterDimSizes[4] = {static_cast<unsigned int>(inputShape.dim(0)),
- static_cast<unsigned int>(inputShape.dim(1)),
- static_cast<unsigned int>(inputShape.dim(2)),
- static_cast<unsigned int>(inputShape.dim(3))};
-
- // Split dimension 1 of the splitter output shape and conv input shapes
- // according to the number of groups
-
- splitterDimSizes[1] /= numGroups;
- inputShape.set_dim(1, splitterDimSizes[1]);
-
- // This is used to describe how the input is to be split
- ViewsDescriptor splitterDesc(numGroups);
-
- // Create an output node for each group, giving each a unique name
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- // Work out the names of the splitter layers child convolutions
- stringstream ss;
- ss << layerParam.name() << "_" << g;
- convLayerNames[g] = ss.str();
-
- splitterDesc.SetViewOriginCoord(g, 1, splitterDimSizes[1] * g);
-
- // Set the size of the views.
- for (unsigned int dimIdx=0; dimIdx < 4; dimIdx++)
- {
- splitterDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]);
- }
- }
-
- const std::string splitterLayerName = std::string("splitter_") + layerParam.bottom(0);
- armnn::IConnectableLayer* splitterLayer = m_Network->AddSplitterLayer(splitterDesc, splitterLayerName.c_str());
-
- inputConnection.Connect(splitterLayer->GetInputSlot(0));
- for (unsigned int i = 0; i < splitterLayer->GetNumOutputSlots(); i++)
- {
- splitterLayer->GetOutputSlot(i).SetTensorInfo(BlobShapeToTensorInfo(inputShape));
- }
-
- unsigned int numFilters = convParam.num_output();
-
- // Populates convolution output tensor descriptor dimensions.
- BlobShape outputShape;
- outputShape.add_dim(0);
- outputShape.set_dim(0, inputShape.dim(0));
- outputShape.add_dim(1);
- // Ensures that dimension 1 of the convolution output is split according to the number of groups.
- outputShape.set_dim(1, numFilters / numGroups);
- outputShape.add_dim(2);
- outputShape.set_dim(
- 2, (static_cast<int>(
- static_cast<float>(inputShape.dim(2) + 2 * desc.m_PadBottom - (desc.m_DilationX * (kernelH - 1) + 1)) /
- static_cast<float>(desc.m_StrideY)) + 1));
- outputShape.add_dim(3);
- outputShape.set_dim(
- 3, (static_cast<int>(
- static_cast<float>(inputShape.dim(3) + 2 * desc.m_PadRight - (desc.m_DilationY * (kernelW - 1) + 1)) /
- static_cast<float>(desc.m_StrideX)) + 1));
-
- // Load the weight data for ALL groups
- vector<float> weightData(armnn::numeric_cast<size_t>(numGroups *
- inputShape.dim(1) * // number of input channels
- outputShape.dim(1) * // number of output channels
- kernelH *
- kernelW));
- GetDataFromBlob(layerParam, weightData, 0);
-
- const unsigned int weightDimSizes[4] = {
- static_cast<unsigned int>(outputShape.dim(1)),
- static_cast<unsigned int>(inputShape.dim(1)),
- kernelH,
- kernelW};
-
- TensorInfo biasInfo;
- vector<float> biasData;
-
- if (desc.m_BiasEnabled)
- {
- biasData.resize(armnn::numeric_cast<size_t>(numGroups * outputShape.dim(1)), 1.f);
- GetDataFromBlob(layerParam, biasData, 1);
-
- const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))};
- biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32);
- }
-
- const unsigned int numWeightsPerGroup = armnn::numeric_cast<unsigned int>(weightData.size()) / numGroups;
- const unsigned int numBiasesPerGroup = armnn::numeric_cast<unsigned int>(biasData.size()) / numGroups;
-
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- // Sets the slot index, group 0 should be connected to the 0th output of the splitter
- // group 1 should be connected to the 1st output of the splitter.
-
- // Pulls out the weights for this group from that loaded from the model file earlier.
- ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32),
- weightData.data() + numWeightsPerGroup * g);
-
- IConnectableLayer* convLayer = nullptr;
- Optional<ConstTensor> optionalBiases;
- if (desc.m_BiasEnabled)
- {
- // Pulls out the biases for this group from that loaded from the model file earlier.
- ConstTensor biases(biasInfo, biasData.data() + numBiasesPerGroup * g);
- optionalBiases = Optional<ConstTensor>(biases);
- }
- convLayer = m_Network->AddConvolution2dLayer(desc,
- weights,
- optionalBiases,
- convLayerNames[g].c_str());
- convLayers[g] = convLayer;
-
- // If we have more than one group then the input to the nth convolution the splitter layer's nth output,
- // otherwise it's the regular input to this layer.
- armnn::IOutputSlot& splitterInputConnection =
- splitterLayer ? splitterLayer->GetOutputSlot(g) : inputConnection;
- splitterInputConnection.Connect(convLayer->GetInputSlot(0));
- convLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape));
- }
-
- // If the convolution was performed in chunks, add a layer to concatenate the results
-
- // The merge input shape matches that of the convolution output
- unsigned int concatDimSizes[4] = {static_cast<unsigned int>(outputShape.dim(0)),
- static_cast<unsigned int>(outputShape.dim(1)),
- static_cast<unsigned int>(outputShape.dim(2)),
- static_cast<unsigned int>(outputShape.dim(3))};
-
- // This is used to describe how the input is to be concatenated
- OriginsDescriptor concatDesc(numGroups);
-
- // Now create an input node for each group, using the name from
- // the output of the corresponding convolution
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- concatDesc.SetViewOriginCoord(g, 1, concatDimSizes[1] * g);
- }
-
- // Make sure the output from the concat is the correct size to hold the data for all groups
- concatDimSizes[1] *= numGroups;
- outputShape.set_dim(1, concatDimSizes[1]);
-
- // Finally add the concat layer
- IConnectableLayer* concatLayer = m_Network->AddConcatLayer(concatDesc, layerParam.name().c_str());
-
- if (!concatLayer)
- {
- throw ParseException(
- fmt::format("Failed to create final concat layer for Split+Convolution+Concat. "
- "Layer={} #groups={} #filters={} {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- convLayers[g]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(g));
- }
- concatLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(4, concatDimSizes, DataType::Float32));
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), concatLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::AddDeconvLayerWithSplits(const caffe::LayerParameter& layerParam,
- const armnn::TransposeConvolution2dDescriptor& desc,
- unsigned int kernelW,
- unsigned int kernelH)
-{
- ARMNN_ASSERT(layerParam.type() == "Deconvolution");
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- ConvolutionParameter convParam = layerParam.convolution_param();
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
- const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1;
-
- // asusme these were already verified by the caller ParseDeconvLayer() function
- ARMNN_ASSERT(numGroups <= inputShape.dim(1));
- ARMNN_ASSERT(numGroups > 1);
-
- // Handle grouping
- armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
-
- vector<string> convLayerNames(numGroups);
- vector<armnn::IConnectableLayer*> convLayers(numGroups);
- convLayerNames[0] = layerParam.name();
-
- // This deconvolution is to be applied to chunks of the input data so add a splitter layer
-
- // Redirect the deconvolution input to the splitter
- unsigned int splitterDimSizes[4] = {static_cast<unsigned int>(inputShape.dim(0)),
- static_cast<unsigned int>(inputShape.dim(1)),
- static_cast<unsigned int>(inputShape.dim(2)),
- static_cast<unsigned int>(inputShape.dim(3))};
-
- // Split dimension 1 of the splitter output shape and deconv input shapes
- // according to the number of groups
-
- splitterDimSizes[1] /= numGroups;
- inputShape.set_dim(1, splitterDimSizes[1]);
-
- // This is used to describe how the input is to be split
- ViewsDescriptor splitterDesc(numGroups);
-
- // Create an output node for each group, giving each a unique name
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- // Work out the names of the splitter layers child deconvolutions
- stringstream ss;
- ss << layerParam.name() << "_" << g;
- convLayerNames[g] = ss.str();
-
- splitterDesc.SetViewOriginCoord(g, 1, splitterDimSizes[1] * g);
-
- // Set the size of the views.
- for (unsigned int dimIdx=0; dimIdx < 4; dimIdx++)
- {
- splitterDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]);
- }
- }
-
- const std::string splitterLayerName = std::string("splitter_") + layerParam.bottom(0);
- armnn::IConnectableLayer* splitterLayer = m_Network->AddSplitterLayer(splitterDesc, splitterLayerName.c_str());
-
- inputConnection.Connect(splitterLayer->GetInputSlot(0));
- for (unsigned int i = 0; i < splitterLayer->GetNumOutputSlots(); i++)
- {
- splitterLayer->GetOutputSlot(i).SetTensorInfo(BlobShapeToTensorInfo(inputShape));
- }
-
- unsigned int numFilters = convParam.num_output();
-
- // Populates deconvolution output tensor descriptor dimensions.
- BlobShape outputShape;
- outputShape.add_dim(0);
- outputShape.set_dim(0, inputShape.dim(0));
- outputShape.add_dim(1);
- // Ensures that dimension 1 of the deconvolution output is split according to the number of groups.
- outputShape.set_dim(1, numFilters / numGroups);
- outputShape.add_dim(2);
- outputShape.set_dim(
- 2, (static_cast<int>(
- desc.m_StrideY * (inputShape.dim(2) - 1) - 2 * desc.m_PadBottom + kernelH)));
- outputShape.add_dim(3);
- outputShape.set_dim(
- 3, (static_cast<int>(
- desc.m_StrideX * (inputShape.dim(3) - 1) - 2 * desc.m_PadRight + kernelW)));
-
- // Load the weight data for ALL groups
- vector<float> weightData(armnn::numeric_cast<size_t>(numGroups *
- inputShape.dim(1) * // number of input channels
- outputShape.dim(1) * // number of output channels
- kernelH *
- kernelW));
- GetDataFromBlob(layerParam, weightData, 0);
-
- const unsigned int weightDimSizes[4] = {
- static_cast<unsigned int>(outputShape.dim(1)),
- static_cast<unsigned int>(inputShape.dim(1)),
- kernelH,
- kernelW};
-
- TensorInfo biasInfo;
- vector<float> biasData;
-
- if (desc.m_BiasEnabled)
- {
- biasData.resize(armnn::numeric_cast<size_t>(numGroups * outputShape.dim(1)), 1.f);
- GetDataFromBlob(layerParam, biasData, 1);
-
- const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))};
- biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32);
- }
-
- const unsigned int numWeightsPerGroup = armnn::numeric_cast<unsigned int>(weightData.size()) / numGroups;
- const unsigned int numBiasesPerGroup = armnn::numeric_cast<unsigned int>(biasData.size()) / numGroups;
-
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- // Sets the slot index, group 0 should be connected to the 0th output of the splitter
- // group 1 should be connected to the 1st output of the splitter.
-
- // Pulls out the weights for this group from that loaded from the model file earlier.
- ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32),
- weightData.data() + numWeightsPerGroup * g);
-
- IConnectableLayer* deconvLayer = nullptr;
- Optional<ConstTensor> optionalBiases;
- if (desc.m_BiasEnabled)
- {
- // Pulls out the biases for this group from that loaded from the model file earlier.
- ConstTensor biases(biasInfo, biasData.data() + numBiasesPerGroup * g);
- optionalBiases = Optional<ConstTensor>(biases);
- }
- deconvLayer = m_Network->AddTransposeConvolution2dLayer(desc,
- weights,
- optionalBiases,
- convLayerNames[g].c_str());
- convLayers[g] = deconvLayer;
-
- // If we have more than one group then the input to the nth deconvolution the splitter layer's nth output,
- // otherwise it's the regular input to this layer.
- armnn::IOutputSlot& splitterInputConnection =
- splitterLayer ? splitterLayer->GetOutputSlot(g) : inputConnection;
- splitterInputConnection.Connect(deconvLayer->GetInputSlot(0));
- deconvLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape));
- }
-
- // If the deconvolution was performed in chunks, add a layer to concatenate the results
-
- // The merge input shape matches that of the deconvolution output
- unsigned int concatDimSizes[4] = {static_cast<unsigned int>(outputShape.dim(0)),
- static_cast<unsigned int>(outputShape.dim(1)),
- static_cast<unsigned int>(outputShape.dim(2)),
- static_cast<unsigned int>(outputShape.dim(3))};
-
- // This is used to describe how the input is to be concatenated
- OriginsDescriptor concatDesc(numGroups);
-
- // Now create an input node for each group, using the name from
- // the output of the corresponding deconvolution
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- concatDesc.SetViewOriginCoord(g, 1, concatDimSizes[1] * g);
- }
-
- // Make sure the output from the concat is the correct size to hold the data for all groups
- concatDimSizes[1] *= numGroups;
- outputShape.set_dim(1, concatDimSizes[1]);
-
- // Finally add the concat layer
- IConnectableLayer* concatLayer = m_Network->AddConcatLayer(concatDesc, layerParam.name().c_str());
-
- if (!concatLayer)
- {
- throw ParseException(
- fmt::format("Failed to create final concat layer for Split+Deconvolution+Concat. "
- "Layer={} #groups={} #filters={} {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- for (unsigned int g = 0; g < numGroups; ++g)
- {
- convLayers[g]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(g));
- }
- concatLayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(4, concatDimSizes, DataType::Float32));
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), concatLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::AddConvLayerWithDepthwiseConv(const caffe::LayerParameter& layerParam,
- const armnn::Convolution2dDescriptor& convDesc,
- unsigned int kernelW,
- unsigned int kernelH)
-{
- ARMNN_ASSERT(layerParam.type() == "Convolution");
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- ConvolutionParameter convParam = layerParam.convolution_param();
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
-
- DepthwiseConvolution2dDescriptor desc;
- desc.m_PadLeft = convDesc.m_PadLeft;
- desc.m_PadRight = convDesc.m_PadRight;
- desc.m_PadTop = convDesc.m_PadTop;
- desc.m_PadBottom = convDesc.m_PadBottom;
- desc.m_StrideX = convDesc.m_StrideX;
- desc.m_StrideY = convDesc.m_StrideY;
- desc.m_DilationX = convDesc.m_DilationX;
- desc.m_DilationY = convDesc.m_DilationY;
- desc.m_BiasEnabled = convDesc.m_BiasEnabled;
-
- unsigned int numFilters = convParam.num_output();
-
- BlobShape outputShape;
- outputShape.add_dim(0);
- outputShape.set_dim(0, inputShape.dim(0));
- outputShape.add_dim(1);
- outputShape.set_dim(1, numFilters);
- outputShape.add_dim(2);
- outputShape.set_dim(
- 2, (static_cast<int>(
- static_cast<float>(inputShape.dim(2) + 2 * desc.m_PadBottom - (desc.m_DilationX * (kernelH - 1) + 1)) /
- static_cast<float>(desc.m_StrideY)) + 1));
- outputShape.add_dim(3);
- outputShape.set_dim(
- 3, (static_cast<int>(
- static_cast<float>(inputShape.dim(3) + 2 * desc.m_PadRight - (desc.m_DilationY * (kernelW - 1) + 1)) /
- static_cast<float>(desc.m_StrideX)) + 1));
-
- // Load the weight data
- size_t allWeightsSize = armnn::numeric_cast<size_t>(inputShape.dim(1) * kernelH * kernelW);
- vector<float> weightData(allWeightsSize);
-
- GetDataFromBlob(layerParam, weightData, 0);
-
- // depth multiplier will be 1 for the depthwise convolution
- const unsigned int weightDimSizes[4] = {
- static_cast<unsigned int>(1), // depth multiplier
- static_cast<unsigned int>(inputShape.dim(1)), // #channels
- kernelH,
- kernelW};
-
- armnn::IConnectableLayer* returnLayer = nullptr;
- ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), weightData.data());
- Optional<ConstTensor> optionalBiases;
- vector<float> biasData;
- if (desc.m_BiasEnabled)
- {
- TensorInfo biasInfo;
-
- biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);
- GetDataFromBlob(layerParam, biasData, 1);
-
- const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))};
- biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32);
-
- ConstTensor biases(biasInfo, biasData.data());
- optionalBiases = Optional<ConstTensor>(biases);
- }
- returnLayer = m_Network->AddDepthwiseConvolution2dLayer(desc,
- weights,
- optionalBiases,
- layerParam.name().c_str());
-
- if (!returnLayer)
- {
- throw ParseException(
- fmt::format("Failed to create depthwise convolution layer. "
- "Layer={} #filters={} {}",
- layerParam.name(),
- numFilters,
- CHECK_LOCATION().AsString()));
- }
- armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
- inputConnection.Connect(returnLayer->GetInputSlot(0));
- returnLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape));
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), returnLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseConvLayer(const LayerParameter& layerParam)
-{
- // Ignored Caffe Parameters
- // * Weight Filler
- // * Bias Filler
- // * Engine
- // * Force nd_im2col
- // * Axis
-
- // Not Available ArmNN Interface Parameters
- // * Rounding policy;
-
- ARMNN_ASSERT(layerParam.type() == "Convolution");
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- ConvolutionParameter convParam = layerParam.convolution_param();
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
- const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1;
- unsigned int numFilters = convParam.num_output();
-
- const auto notFound = std::numeric_limits<unsigned int>::max();
-
- unsigned int kernelH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- kernel_h, kernel_size, unsigned int, notFound);
- unsigned int kernelW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- kernel_w, kernel_size, unsigned int, notFound);
-
- unsigned int strideH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- stride_h, stride, unsigned int, 1u);
- unsigned int strideW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- stride_w, stride, unsigned int, 1u);
-
- unsigned int padH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- pad_h, pad, unsigned int, 0u);
- unsigned int padW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- pad_w, pad, unsigned int, 0u);
-
- unsigned int dilationH = convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;
- unsigned int dilationW = convParam.dilation_size() > 1 ? convParam.dilation(1) :
- convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;
-
- Convolution2dDescriptor convolution2dDescriptor;
- convolution2dDescriptor.m_PadLeft = padW;
- convolution2dDescriptor.m_PadRight = padW;
- convolution2dDescriptor.m_PadTop = padH;
- convolution2dDescriptor.m_PadBottom = padH;
- convolution2dDescriptor.m_StrideX = strideW;
- convolution2dDescriptor.m_StrideY = strideH;
- convolution2dDescriptor.m_DilationX = dilationW;
- convolution2dDescriptor.m_DilationY = dilationH;
- convolution2dDescriptor.m_BiasEnabled = convParam.has_bias_term() ? convParam.bias_term() : true;
-
- if (numGroups > numFilters)
- {
- throw ParseException(
- fmt::format("Error parsing Convolution: {}. "
- "The 'group'={} parameter cannot be larger than the "
- "number of filters supplied ='{}'. {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- if (inputShape.dim_size() != 4)
- {
- throw ParseException(
- fmt::format("Convolution input shape is expected to have 4 dimensions. "
- "{}'s input has only {}. {}",
- layerParam.name(),
- inputShape.dim_size(),
- CHECK_LOCATION().AsString()));
- }
-
- if (numGroups > 1)
- {
- if (numGroups > inputShape.dim(1))
- {
- throw ParseException(
- fmt::format("Error parsing Convolution: {}. "
- "The 'group'={} parameter cannot be larger than the "
- "channel of the input shape={} (in NCHW format). {}",
- layerParam.name(),
- numGroups,
- inputShape.dim(1),
- CHECK_LOCATION().AsString()));
- }
- else if (numGroups == inputShape.dim(1))
- {
- // we use a depthwise convolution here, because the number of groups equals to the
- // input channels
- AddConvLayerWithDepthwiseConv(layerParam, convolution2dDescriptor, kernelW, kernelH);
- return;
- }
- else
- {
- // we split the input by channels into channels/groups separate convolutions
- // and concatenate the results afterwards
- AddConvLayerWithSplits(layerParam, convolution2dDescriptor, kernelW, kernelH);
- return;
- }
- }
-
- // NOTE: at this point we only need to handle #group=1 case, all other cases should be
- // handled by the AddConvLayer* helpers
-
- // Populate convolution output tensor descriptor dimensions
- BlobShape outputShape;
- outputShape.add_dim(0);
- outputShape.set_dim(0, inputShape.dim(0));
- outputShape.add_dim(1);
- outputShape.set_dim(1, numFilters);
- outputShape.add_dim(2);
- outputShape.set_dim(
- 2, (static_cast<int>(
- static_cast<float>(inputShape.dim(2) + 2 * padH - (dilationH * (kernelH - 1) + 1)) /
- static_cast<float>(strideH)) + 1));
- outputShape.add_dim(3);
- outputShape.set_dim(
- 3, (static_cast<int>(
- static_cast<float>(inputShape.dim(3) + 2 * padW - (dilationW * (kernelW - 1) + 1)) /
- static_cast<float>(strideW)) + 1));
-
- // Load the weight data for ALL groups
- vector<float> weightData(armnn::numeric_cast<size_t>(inputShape.dim(1) *
- outputShape.dim(1) *
- kernelH *
- kernelW));
- GetDataFromBlob(layerParam, weightData, 0);
-
- const unsigned int weightDimSizes[4] = {
- static_cast<unsigned int>(outputShape.dim(1)), // output channels
- static_cast<unsigned int>(inputShape.dim(1)), // input channels
- kernelH,
- kernelW};
-
- armnn::IConnectableLayer* returnLayer = nullptr;
-
- // Pull out the weights for this group from that loaded from the model file earlier
- ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), weightData.data());
- Optional<ConstTensor> optionalBiases;
- vector<float> biasData;
- if (convolution2dDescriptor.m_BiasEnabled)
- {
- TensorInfo biasInfo;
-
- biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);
- GetDataFromBlob(layerParam, biasData, 1);
-
- const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))};
- biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32);
-
- // Pull out the biases for this group from that loaded from the model file earlier
- ConstTensor biases(biasInfo, biasData.data());
- optionalBiases = Optional<ConstTensor>(biases);
- }
- returnLayer = m_Network->AddConvolution2dLayer(convolution2dDescriptor,
- weights,
- optionalBiases,
- layerParam.name().c_str());
-
- armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
- inputConnection.Connect(returnLayer->GetInputSlot(0));
- returnLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape));
-
- if (!returnLayer)
- {
- throw ParseException(
- fmt::format("Failed to create Convolution layer. "
- "Layer={} #groups={} #filters={} {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), returnLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseDeconvLayer(const LayerParameter& layerParam)
-{
- // Ignored Caffe Parameters
- // * Weight Filler
- // * Bias Filler
- // * Engine
- // * Force nd_im2col
- // * Axis
-
- // Not Available ArmNN Interface Parameters
- // * Rounding policy;
-
- ARMNN_ASSERT(layerParam.type() == "Deconvolution");
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- ConvolutionParameter convParam = layerParam.convolution_param();
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
- const unsigned int numGroups = convParam.has_group() ? convParam.group() : 1;
- unsigned int numFilters = convParam.num_output();
-
- const auto notFound = std::numeric_limits<unsigned int>::max();
-
- unsigned int kernelH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- kernel_h, kernel_size, unsigned int, notFound);
- unsigned int kernelW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- kernel_w, kernel_size, unsigned int, notFound);
-
- unsigned int strideH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- stride_h, stride, unsigned int, 1u);
- unsigned int strideW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- stride_w, stride, unsigned int, 1u);
-
- unsigned int padH = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- pad_h, pad, unsigned int, 0u);
- unsigned int padW = GET_OPTIONAL_WITH_VECTOR_FALLBACK(convParam, ConvolutionParameter,
- pad_w, pad, unsigned int, 0u);
-
- unsigned int dilationH = convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;
- unsigned int dilationW = convParam.dilation_size() > 1 ? convParam.dilation(1) :
- convParam.dilation_size() > 0 ? convParam.dilation(0) : 1;
-
- if (dilationH != 1 || dilationW != 1) {
- fmt::format("Dilated decnvolution is not supported. "
- "{}'s input has dilation {} {}. {}",
- layerParam.name(),
- dilationW, dilationH,
- CHECK_LOCATION().AsString());
- }
-
- TransposeConvolution2dDescriptor deconvolution2dDescriptor;
- deconvolution2dDescriptor.m_PadLeft = padW;
- deconvolution2dDescriptor.m_PadRight = padW;
- deconvolution2dDescriptor.m_PadTop = padH;
- deconvolution2dDescriptor.m_PadBottom = padH;
- deconvolution2dDescriptor.m_StrideX = strideW;
- deconvolution2dDescriptor.m_StrideY = strideH;
- deconvolution2dDescriptor.m_BiasEnabled = convParam.has_bias_term() ? convParam.bias_term() : true;
-
- if (numGroups > numFilters)
- {
- throw ParseException(
- fmt::format("Error parsing Deconvolution: {}. "
- "The 'group'={} parameter cannot be larger than the "
- "number of filters supplied ='{}'. {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- if (inputShape.dim_size() != 4)
- {
- throw ParseException(
- fmt::format("Deconvolution input shape is expected to have 4 dimensions. "
- "{}'s input has only {}. {}",
- layerParam.name(),
- inputShape.dim_size(),
- CHECK_LOCATION().AsString()));
- }
-
- if (numGroups > 1)
- {
- if (numGroups > inputShape.dim(1))
- {
- throw ParseException(
- fmt::format("Error parsing Deconvolution: {}. "
- "The 'group'={} parameter cannot be larger than the "
- "channel of the input shape={} (in NCHW format). {}",
- layerParam.name(),
- numGroups,
- inputShape.dim(1),
- CHECK_LOCATION().AsString()));
- }
- else
- {
- // we split the input by channels into channels/groups separate convolutions
- // and concatenate the results afterwards
- AddDeconvLayerWithSplits(layerParam, deconvolution2dDescriptor, kernelW, kernelH);
- return;
- }
- }
-
- // NOTE: at this point we only need to handle #group=1 case, all other cases should be
- // handled by the AddDeconvLayer* helpers
-
- // Populate deconvolution output tensor descriptor dimensions
- BlobShape outputShape;
- outputShape.add_dim(0);
- outputShape.set_dim(0, inputShape.dim(0));
- outputShape.add_dim(1);
- outputShape.set_dim(1, numFilters);
- outputShape.add_dim(2);
- outputShape.set_dim(
- 2, (static_cast<int>(
- strideH * (inputShape.dim(2) - 1) - 2 * padH + (dilationH * (kernelH - 1) + 1))));
- outputShape.add_dim(3);
- outputShape.set_dim(
- 3, (static_cast<int>(
- strideW * (inputShape.dim(3) - 1) - 2 * padW + (dilationW * (kernelW - 1) + 1))));
-
- // Load the weight data for ALL groups
- vector<float> weightData(armnn::numeric_cast<size_t>(inputShape.dim(1) *
- outputShape.dim(1) *
- kernelH *
- kernelW));
- GetDataFromBlob(layerParam, weightData, 0);
-
- const unsigned int weightDimSizes[4] = {
- static_cast<unsigned int>(outputShape.dim(1)), // output channels
- static_cast<unsigned int>(inputShape.dim(1)), // input channels
- kernelH,
- kernelW};
-
- armnn::IConnectableLayer* returnLayer = nullptr;
-
- // Pull out the weights for this group from that loaded from the model file earlier
- ConstTensor weights(TensorInfo(4, weightDimSizes, DataType::Float32), weightData.data());
- Optional<ConstTensor> optionalBiases;
- vector<float> biasData;
- if (deconvolution2dDescriptor.m_BiasEnabled)
- {
- TensorInfo biasInfo;
-
- biasData.resize(armnn::numeric_cast<size_t>(outputShape.dim(1)), 1.f);
- GetDataFromBlob(layerParam, biasData, 1);
-
- const unsigned int biasDimSizes[1] = {static_cast<unsigned int>(outputShape.dim(1))};
- biasInfo = TensorInfo(1, biasDimSizes, DataType::Float32);
-
- // Pull out the biases for this group from that loaded from the model file earlier
- ConstTensor biases(biasInfo, biasData.data());
- optionalBiases = Optional<ConstTensor>(biases);
- }
- returnLayer = m_Network->AddTransposeConvolution2dLayer(deconvolution2dDescriptor,
- weights,
- optionalBiases,
- layerParam.name().c_str());
-
- armnn::IOutputSlot& inputConnection = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
- inputConnection.Connect(returnLayer->GetInputSlot(0));
- returnLayer->GetOutputSlot(0).SetTensorInfo(BlobShapeToTensorInfo(outputShape));
-
- if (!returnLayer)
- {
- throw ParseException(
- fmt::format("Failed to create Deconvolution layer. "
- "Layer={} #groups={} #filters={} {}",
- layerParam.name(),
- numGroups,
- numFilters,
- CHECK_LOCATION().AsString()));
- }
-
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), returnLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParsePoolingLayer(const LayerParameter& layerParam)
-{
- // Ignored Caffe Parameters
- // Stochastic Pooling
- // Engine
-
- ValidateNumInputsOutputs(layerParam, 1, 1);
- PoolingParameter param = layerParam.pooling_param();
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- const auto notFound = std::numeric_limits<unsigned int>::max();
-
- unsigned int kernel_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- kernel_h, kernel_size, unsigned int, notFound);
- unsigned int kernel_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- kernel_w, kernel_size, unsigned int, notFound);
-
- if ((kernel_h == notFound || kernel_w == notFound) && param.has_global_pooling())
- {
- kernel_h = inputInfo.GetShape()[2];
- kernel_w = inputInfo.GetShape()[3];
- }
-
- unsigned int stride_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- stride_h, stride, unsigned int, notFound);
- unsigned int stride_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- stride_h, stride, unsigned int, notFound);
-
- if ((stride_h == notFound || stride_w == notFound) && param.has_global_pooling())
- {
- stride_h = 1;
- stride_w = 1;
- }
-
- unsigned int pad_h = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- pad_h, pad, unsigned int, 0u);
- unsigned int pad_w = GET_OPTIONAL_WITH_FALLBACK(param, PoolingParameter,
- pad_w, pad, unsigned int, 0u);
-
- // Populate Weight and Bias Filter Descriptor
- Pooling2dDescriptor pooling2dDescriptor;
- if (param.has_pool())
- {
- PoolingParameter_PoolMethod p = param.pool();
- switch (p)
- {
- case PoolingParameter_PoolMethod_MAX:
- {
- pooling2dDescriptor.m_PoolType = PoolingAlgorithm::Max;
- break;
- }
- case PoolingParameter_PoolMethod_AVE:
- {
- pooling2dDescriptor.m_PoolType = PoolingAlgorithm::Average;
- break;
- }
- case PoolingParameter_PoolMethod_STOCHASTIC:
- {
- throw ParseException(
- fmt::format("Pooling Layer: Stochastic Pooling Not Supported. Layer={} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- default:
- {
- throw ParseException(
- fmt::format("Pooling Layer: unknown pooling method: {} for layer: {} {}",
- p,
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- }
- }
- else
- {
- throw ParseException(
- fmt::format("No Pooling Method Defined for {} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- pooling2dDescriptor.m_PadLeft = pad_w;
- pooling2dDescriptor.m_PadRight = pad_w;
- pooling2dDescriptor.m_PadTop = pad_h;
- pooling2dDescriptor.m_PadBottom = pad_h;
- pooling2dDescriptor.m_StrideX = stride_w;
- pooling2dDescriptor.m_StrideY = stride_h;
- pooling2dDescriptor.m_PoolWidth = kernel_w;
- pooling2dDescriptor.m_PoolHeight = kernel_h;
-
- pooling2dDescriptor.m_OutputShapeRounding = OutputShapeRounding::Ceiling;
- pooling2dDescriptor.m_PaddingMethod = PaddingMethod::IgnoreValue;
-
- armnn::IConnectableLayer* poolingLayer = m_Network->AddPooling2dLayer(pooling2dDescriptor,
- layerParam.name().c_str());
-
- TensorInfo outputInfo(
- { inputInfo.GetShape()[0],
- inputInfo.GetShape()[1],
- static_cast<unsigned int>(ceil(
- static_cast<float>(inputInfo.GetShape()[2] + 2 * pad_h - kernel_h) /
- armnn::numeric_cast<float>(stride_h))) + 1,
- static_cast<unsigned int>(ceil(
- static_cast<float>(inputInfo.GetShape()[3] + 2 * pad_w - kernel_w) /
- armnn::numeric_cast<float>(stride_w))) + 1 },
- DataType::Float32);
-
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(poolingLayer->GetInputSlot(0));
- poolingLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), poolingLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseArgmaxLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 1, 1);
- ArgMaxParameter param = layerParam.argmax_param();
-
- BlobShape inputShape = TensorDescToBlobShape(GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo());
-
- const unsigned int topK = param.has_top_k() ? param.top_k() : 1;
- if (topK != 1) {
- throw ParseException(
- fmt::format("ArgMaxLayer: Only support top_k equals to 1. Layer={} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- const unsigned int outMaxVal = param.has_out_max_val() ? param.out_max_val() : false;
- if (outMaxVal) {
- throw ParseException(
- fmt::format("ArgMaxLayer: Does not support out_max_val. Layer={} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- int axis = param.has_axis() ? param.axis() : 1;
- if (axis < 0) {
- axis = inputShape.dim_size() - axis;
- }
- if ((axis < 0) || (axis >= inputShape.dim_size())) {
- throw ParseException(
- fmt::format("ArgMaxLayer: Invalid axis value which outside range of input dims. "
- "{}'s input has input dim_size {}, requested axis: {}. {}",
- layerParam.name(),
- inputShape.dim_size(),
- axis,
- CHECK_LOCATION().AsString()));
- }
-
- ArgMinMaxDescriptor desc;
- desc.m_Axis = axis;
- desc.m_Function = ArgMinMaxFunction::Max;
-
- armnn::IConnectableLayer* argmaxLayer = m_Network->AddArgMinMaxLayer(desc,
- layerParam.name().c_str());
-
- TensorShape outputShape(static_cast<unsigned int>(inputShape.dim_size() - 1));
- int j = 0;
- // remove the flatten axis
- for (int i = 0; i < inputShape.dim_size(); ++i)
- {
- if (i == axis) continue;
- outputShape[static_cast<unsigned int>(j++)] = static_cast<unsigned int>(inputShape.dim(i));
- }
- TensorInfo outputInfo(outputShape, DataType::Signed32);
-
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(argmaxLayer->GetInputSlot(0));
- argmaxLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), argmaxLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseReluLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- const string& name = layerParam.name();
- const ReLUParameter& param = layerParam.relu_param();
-
- ActivationDescriptor activationDescriptor;
- const float negativeSlope = param.negative_slope();
- if (negativeSlope == 0.0f)
- {
- activationDescriptor.m_Function = ActivationFunction::ReLu;
- }
- else
- {
- activationDescriptor.m_Function = ActivationFunction::LeakyReLu;
- activationDescriptor.m_A = negativeSlope;
- }
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
- IConnectableLayer* const activationLayer = m_Network->AddActivationLayer(activationDescriptor, name.c_str());
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(activationLayer->GetInputSlot(0));
- activationLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), activationLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseLRNLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- LRNParameter param = layerParam.lrn_param();
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- // Ignored BATCH NORMALIZATION Caffe Parameters.
- // Ignored MVN Caffe Parameters.
- // Ignored LRN Caffe Parameters.
- // Engine
-
- NormalizationDescriptor normalizationDescriptor;
- if (param.has_norm_region())
- {
- LRNParameter_NormRegion n = param.norm_region();
- switch (n)
- {
- case LRNParameter_NormRegion_ACROSS_CHANNELS:
- {
- normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Across;
- break;
- }
- case LRNParameter_NormRegion_WITHIN_CHANNEL:
- {
- normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Within;
- break;
- }
- default:
- {
- throw ParseException(
- fmt::format("Unknown region {} for LRN layer {} {}",
- n,
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- }
- }
- else
- {
- // Caffe defaults to normalization across channels.
- normalizationDescriptor.m_NormChannelType = NormalizationAlgorithmChannel::Across;
- }
-
- normalizationDescriptor.m_NormMethodType = NormalizationAlgorithmMethod::LocalBrightness;
- if (param.has_local_size())
- {
- normalizationDescriptor.m_NormSize = param.local_size();
- }
- else
- {
- throw ParseException(
- fmt::format("local_size not defined for LRN layer {} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- if (param.has_alpha())
- {
- normalizationDescriptor.m_Alpha = param.alpha();
- normalizationDescriptor.m_Alpha /= armnn::numeric_cast<float>(param.local_size());
- }
- else
- {
- throw ParseException(
- fmt::format("Alpha not defined for LRN layer {} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- if (param.has_beta())
- {
- normalizationDescriptor.m_Beta = param.beta();
- }
- else
- {
- throw ParseException(
- fmt::format("Beta not defined for LRN layer {} {}",
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
-
- if (param.has_k())
- {
- normalizationDescriptor.m_K = param.k();
- }
- else
- {
- normalizationDescriptor.m_K = 1;
- }
-
- IConnectableLayer* const normLayer = m_Network->AddNormalizationLayer(normalizationDescriptor,
- layerParam.name().c_str());
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(normLayer->GetInputSlot(0));
- normLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), normLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseInnerProductLayer(const LayerParameter& layerParam)
-{
- InnerProductParameter param = layerParam.inner_product_param();
-
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- unsigned int outputSize = param.num_output();
-
- // Ignored Caffe Parameters:
- // Weight Filler
- // Bias Filler
- // Engine
- // Axis
-
- FullyConnectedDescriptor tensorFullyConnectedDescriptor;
-
- if (param.has_transpose())
- {
- // If true, assumes transposed weights.
- tensorFullyConnectedDescriptor.m_TransposeWeightMatrix = param.transpose();
- }
- else
- {
- // Caffe defaults to transposed.
- tensorFullyConnectedDescriptor.m_TransposeWeightMatrix = true;
- }
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- TensorInfo weightInfo;
- TensorInfo biasInfo;
-
- // Allows implicit flattening of extra dimensions.
- unsigned int inputSize = inputInfo.GetShape()[1];
- for (unsigned int i = 2; i < inputInfo.GetNumDimensions(); ++i)
- {
- inputSize *= inputInfo.GetShape()[i];
- }
-
- const float* weightDataPtr = GetArrayPtrFromBlob(layerParam, 0);
- const unsigned int swTD[2] = { outputSize, inputSize };
- ConstTensor weights(TensorInfo(2, swTD, DataType::Float32), weightDataPtr);
-
- tensorFullyConnectedDescriptor.m_BiasEnabled = true;
- // Todo: check whether bias enabled.
- armnn::IConnectableLayer* fullyConnectedLayer = nullptr;
- if (tensorFullyConnectedDescriptor.m_BiasEnabled)
- {
- // BIAS VALUE
- const float* biasDataPtr = GetArrayPtrFromBlob(layerParam, 1);
-
- const unsigned int sbTD[1] = { outputSize };
-
- ConstTensor biases(TensorInfo(1, sbTD, DataType::Float32), biasDataPtr);
-
- fullyConnectedLayer = m_Network->AddFullyConnectedLayer(tensorFullyConnectedDescriptor,
- weights,
- Optional<ConstTensor>(biases),
- layerParam.name().c_str());
- }
- else
- {
- fullyConnectedLayer = m_Network->AddFullyConnectedLayer(tensorFullyConnectedDescriptor,
- weights,
- EmptyOptional(),
- layerParam.name().c_str());
- }
-
- TensorInfo outputInfo({ inputInfo.GetShape()[0], outputSize }, DataType::Float32);
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(fullyConnectedLayer->GetInputSlot(0));
- fullyConnectedLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), fullyConnectedLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseSoftmaxLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- SoftmaxParameter param = layerParam.softmax_param();
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- // Ignored Caffe Parameters:
- // axis
- // Engine
-
- armnn::SoftmaxDescriptor softmaxDescriptor;
- softmaxDescriptor.m_Axis = 1;
- armnn::IConnectableLayer* const softmaxLayer = m_Network->AddSoftmaxLayer(
- softmaxDescriptor,
- layerParam.name().c_str());
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(softmaxLayer->GetInputSlot(0));
- softmaxLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), softmaxLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseEltwiseLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 2, 1);
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- // Ignored Caffe Parameters:
- // coeff
-
- EltwiseParameter_EltwiseOp operation = EltwiseParameter_EltwiseOp_SUM; // Defaults to sum as per caffe.
-
- if (layerParam.has_eltwise_param() && layerParam.eltwise_param().has_operation())
- {
- operation = layerParam.eltwise_param().operation();
- }
-
- armnn::IConnectableLayer* newLayer = nullptr;
- switch (operation)
- {
- case EltwiseParameter_EltwiseOp_SUM:
- {
- newLayer = m_Network->AddAdditionLayer(layerParam.name().c_str());
- break;
- }
- case EltwiseParameter_EltwiseOp_PROD:
- {
- newLayer = m_Network->AddMultiplicationLayer(layerParam.name().c_str());
- break;
- }
- default:
- {
- throw ParseException(
- fmt::format("Unsupported operation {} in Eltwise layer {} {}",
- operation,
- layerParam.name(),
- CHECK_LOCATION().AsString()));
- }
- }
-
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(newLayer->GetInputSlot(0));
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(1)).Connect(newLayer->GetInputSlot(1));
- newLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), newLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseConcatLayer(const LayerParameter& layerParam)
-{
- unsigned int numInputs = static_cast<unsigned int>(layerParam.bottom_size());
- // We assume concat happens along the channel dimension, which is 1 in (0, 1, 2, 3).
- unsigned int concatDim = 1;
- unsigned int numOfDims = 4;
-
- // we only consider 4-D tensor here
- OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numInputs), numOfDims);
- std::vector<unsigned int>mergeDimSizes(numOfDims, 0u);
-
- unsigned int mergeDim = 0;
- for (unsigned int viewIndex = 0; viewIndex < numInputs; ++viewIndex)
- {
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(
- layerParam.bottom(armnn::numeric_cast<int>(viewIndex))).GetTensorInfo();
- // Checks whether the dimensions of the input tensors are actually 4.
- if (inputInfo.GetNumDimensions()!=4)
- {
- throw ParseException(
- fmt::format("The number of dimensions for input tensors of "
- "the concatenation op should be 4. Inputs of {} has "
- "{} dimensions. {}",
- layerParam.name(),
- inputInfo.GetNumDimensions(),
- CHECK_LOCATION().AsString()));
- }
-
- mergeDimSizes[0] = inputInfo.GetShape()[0];
- mergeDimSizes[1] = inputInfo.GetShape()[1];
- mergeDimSizes[2] = inputInfo.GetShape()[2];
- mergeDimSizes[3] = inputInfo.GetShape()[3];
-
- for (unsigned int j = 0; j < concatDim; ++j)
- {
- concatDescriptor.SetViewOriginCoord(viewIndex, j, 0);
- }
-
- concatDescriptor.SetViewOriginCoord(viewIndex, concatDim, mergeDim);
- mergeDim += mergeDimSizes[concatDim];
-
- for (unsigned int j = concatDim+1; j < numOfDims; ++j)
- {
- concatDescriptor.SetViewOriginCoord(viewIndex, j, 0);
- }
- }
- mergeDimSizes[concatDim] = mergeDim;
-
- armnn::IConnectableLayer* concatlayer = m_Network->AddConcatLayer(concatDescriptor, layerParam.name().c_str());
- for (unsigned int i = 0; i < numInputs; ++i)
- {
- armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(armnn::numeric_cast<int>(i)));
- outputSlot.Connect(concatlayer->GetInputSlot(i));
- }
-
- concatlayer->GetOutputSlot(0).SetTensorInfo(armnn::TensorInfo(numOfDims, mergeDimSizes.data(), DataType::Float32));
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), concatlayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseBatchNormLayer(const LayerParameter& layerParam)
-{
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- string name = layerParam.name();
-
- BatchNormParameter param = layerParam.batch_norm_param();
- // If use_global_stats is not explicitly set in the model, assume it to be true (its default value
- // when the network is in the testing phase).
- if (param.has_use_global_stats())
- {
- if (!param.use_global_stats())
- {
- throw ParseException(
- fmt::format("Error parsing Batch Norm layer '{}': "
- "Parameter 'use_global_stats' is set to false, which is "
- "unsupported (value used for training). {}",
- name,
- CHECK_LOCATION().AsString()));
- }
- }
-
- BatchNormalizationDescriptor desc;
- desc.m_Eps = param.eps();
-
- unsigned int channels = inputInfo.GetShape()[1];
- unsigned int shape[] = {channels};
-
- vector<float> meanData(channels);
- GetDataFromBlob(layerParam, meanData, 0);
-
- vector<float> varianceData(channels);
- GetDataFromBlob(layerParam, varianceData, 1);
-
- // Reads moving average factor and applies scaling (if required).
- const BlobProto& blob = layerParam.blobs(armnn::numeric_cast<int>(2));
- const float movingAverageFactor = blob.data(armnn::numeric_cast<int>(0));
- if(movingAverageFactor != 0.0f)
- {
- const float scaleFactor = 1.0f / movingAverageFactor;
- auto scaleFunction = [scaleFactor](float f) -> float { return f * scaleFactor; };
-
- std::transform(varianceData.begin(), varianceData.end(), varianceData.begin(), scaleFunction);
- std::transform(meanData.begin(), meanData.end(), meanData.begin(), scaleFunction);
- }
-
- // Identifies scale operation.
- vector<float> betaData(channels, 0.0f);
- vector<float> gammaData(channels, 1.0f);
-
- ConstTensor mean(TensorInfo(1, shape, armnn::DataType::Float32), meanData);
- ConstTensor variance(TensorInfo(1, shape, armnn::DataType::Float32), varianceData);
- ConstTensor beta(TensorInfo(1, shape, armnn::DataType::Float32), betaData);
- ConstTensor gamma(TensorInfo(1, shape, armnn::DataType::Float32), gammaData);
-
- armnn::IConnectableLayer* const batchNormLayer = m_Network->AddBatchNormalizationLayer(desc,
- mean, variance, beta, gamma, name.c_str());
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(batchNormLayer->GetInputSlot(0));
- batchNormLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), batchNormLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseScaleLayer(const LayerParameter& layerParam)
-{
- // Current unoptimal solution: add a batchnormalization layer with 0 mean and 1 variance.
- ValidateNumInputsOutputs(layerParam, 1, 1);
-
- const TensorInfo& inputInfo = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).GetTensorInfo();
-
- string name = layerParam.name();
-
- ScaleParameter param = layerParam.scale_param();
- if (param.axis() != 1)
- {
- // Would have to use something other than BatchNormalizationLayer in this case
- throw ParseException(
- fmt::format("Loading Scale Layer: Only axis 1 is supported currently. "
- "Layer={} Axis={} {}",
- layerParam.name(),
- param.axis(),
- CHECK_LOCATION().AsString()));
- }
-
- unsigned int channels = inputInfo.GetShape()[1];
- unsigned int shape[] = {channels};
-
- BatchNormalizationDescriptor desc;
- desc.m_Eps = 0.0f; // Don't need epsilon if variance is 1.
- vector<float> meanData(channels, 0.0f);
- vector<float> varianceData(channels, 1.0f);
- vector<float> betaData(channels, 0.0f);
- vector<float> gammaData(channels);
-
- GetDataFromBlob(layerParam, gammaData, 0);
-
- if(param.has_bias_term())
- {
- GetDataFromBlob(layerParam, betaData, 1);
- }
-
- ConstTensor mean(TensorInfo(1, shape, armnn::DataType::Float32), meanData);
- ConstTensor variance(TensorInfo(1, shape, armnn::DataType::Float32), varianceData);
- ConstTensor beta(TensorInfo(1, shape, armnn::DataType::Float32), betaData);
- ConstTensor gamma(TensorInfo(1, shape, armnn::DataType::Float32), gammaData);
-
- armnn::IConnectableLayer* const batchNormLayer = m_Network->AddBatchNormalizationLayer(desc,
- mean, variance, beta, gamma, name.c_str());
- GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)).Connect(batchNormLayer->GetInputSlot(0));
- batchNormLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), batchNormLayer->GetOutputSlot(0));
-}
-
-void ICaffeParser::CaffeParserImpl::ParseSplitLayer(const caffe::LayerParameter& layerParam)
-{
- // Used in caffe to duplicate memory - not necessary in armnn.
- if (layerParam.bottom_size() != 1)
- {
- throw ParseException(
- fmt::format("Split layer '{}' should have exactly 1 bottom. "
- "#bottoms={} {}",
- layerParam.name(),
- layerParam.bottom_size(),
- CHECK_LOCATION().AsString()));
- }
- armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0));
- for (int i = 0; i < layerParam.top_size(); i++)
- {
- SetArmnnOutputSlotForCaffeTop(layerParam.top(i), outputSlot);
- }
-}
-
-void ICaffeParser::CaffeParserImpl::ParseDropoutLayer(const caffe::LayerParameter& layerParam)
-{
- // Ignored for inference, so patch the single input to its single output.
- if (layerParam.bottom_size() != 1 || layerParam.top_size() != 1)
- {
- throw ParseException(
- fmt::format("Dropout layer '{}' should have exactly 1 bottom and 1 top. "
- "#bottoms={} #tops={} {}",
- layerParam.name(),
- layerParam.bottom_size(),
- layerParam.top_size(),
- CHECK_LOCATION().AsString()));
- }
- SetArmnnOutputSlotForCaffeTop(layerParam.top(0), GetArmnnOutputSlotForCaffeTop(layerParam.bottom(0)));
-}
-
-void ICaffeParser::CaffeParserImpl::TrackInputBinding(armnn::IConnectableLayer* layer,
- armnn::LayerBindingId id,
- const armnn::TensorInfo& tensorInfo)
-{
- return TrackBindingPoint(layer, id, tensorInfo, layer->GetName(), m_NetworkInputsBindingInfo);
-}
-
-void ICaffeParser::CaffeParserImpl::TrackOutputBinding(armnn::IConnectableLayer* layer,
- armnn::LayerBindingId id,
- const armnn::TensorInfo& tensorInfo)
-{
- return TrackBindingPoint(layer, id, tensorInfo, layer->GetName(), m_NetworkOutputsBindingInfo);
-}
-
-void ICaffeParser::CaffeParserImpl::TrackBindingPoint(armnn::IConnectableLayer* layer,
- armnn::LayerBindingId id,
- const armnn::TensorInfo& tensorInfo,
- const char* bindingPointDesc,
- std::unordered_map<std::string, BindingPointInfo>& nameToBindingInfo)
-{
- const std::string layerName = layer->GetName();
- auto it = nameToBindingInfo.find(layerName);
- if (it == nameToBindingInfo.end())
- {
- nameToBindingInfo[layerName] = std::make_pair(id, tensorInfo);
- }
- else
- {
- throw ParseException(
- fmt::format("Id {} used by more than one {} layer {}",
- id,
- bindingPointDesc,
- CHECK_LOCATION().AsString()));
- }
-}
-
-armnn::IOutputSlot& ICaffeParser::CaffeParserImpl::GetArmnnOutputSlotForCaffeTop(const std::string& caffeTopName) const
-{
- auto it = m_ArmnnOutputSlotForCaffeTop.find(caffeTopName);
- if (it != m_ArmnnOutputSlotForCaffeTop.end())
- {
- return *it->second;
- }
- else
- {
- throw ParseException(
- fmt::format("Could not find armnn output slot for Caffe top '{}' {}",
- caffeTopName,
- CHECK_LOCATION().AsString()));
- }
-}
-
-void ICaffeParser::CaffeParserImpl::SetArmnnOutputSlotForCaffeTop(
- const std::string& caffeTopName, armnn::IOutputSlot& armnnOutputSlot)
-{
- auto it = m_ArmnnOutputSlotForCaffeTop.find(caffeTopName);
- if (it == m_ArmnnOutputSlotForCaffeTop.end())
- {
- m_ArmnnOutputSlotForCaffeTop[caffeTopName] = &armnnOutputSlot;
- }
- else
- {
- throw ParseException(
- fmt::format("Attempting to add duplicate entry for Caffe top '{}' {}",
- caffeTopName,
- CHECK_LOCATION().AsString()));
- }
-}
-
-// Note: can move to CaffeParser when/if we optimise the text/string format
-// to load on a layer by layer basis
-void ICaffeParser::CaffeParserImpl::ResolveInPlaceLayers(caffe::NetParameter& netParameter)
-{
- // Finds layers with the same top.
- std::map<std::string, std::vector<caffe::LayerParameter*>> layersByTop;
- for (int layerIdx = 0; layerIdx < netParameter.layer_size(); ++layerIdx)
- {
- caffe::LayerParameter& layer = *netParameter.mutable_layer(layerIdx);
- std::string name = layer.name();
- for (int i = 0; i < layer.top_size(); ++i)
- {
- layersByTop[layer.top(i)].push_back(&layer);
- }
- }
-
- // For each set of layers with the same top, resolves them to a linear chain rather than in-place layers.
- // Note that for 'regular' layers, there will be a single layer in each group and so this will be a no-op.
- for (auto layersWithSameTopIt : layersByTop)
- {
- const std::string& top = layersWithSameTopIt.first;
- const std::vector<caffe::LayerParameter*>& layersWithSameTop = layersWithSameTopIt.second;
-
- // Chains the layers together in the order that they are listed in the prototxt (hopefully this is correct).
- // Note that the last layer will not have its top modified so that other layers will continue to reference it.
- for (unsigned int layerIdx = 0; layerIdx < layersWithSameTop.size() - 1; ++layerIdx)
- {
- caffe::LayerParameter& layer1 = *layersWithSameTop[layerIdx];
- caffe::LayerParameter& layer2 = *layersWithSameTop[layerIdx+1];
- if (layer1.top_size() != 1)
- {
- throw ParseException(
- fmt::format("Node '{}' is an in-place layer but doesn't have exactly one "
- "top. It has {} instead. {}",
- layer1.name(),
- layer1.top_size(),
- CHECK_LOCATION().AsString()));
- }
- std::string newTop = layer1.name() + "_top";
- layer1.set_top(0, newTop);
- if (layer2.bottom_size() != 1 || layer2.bottom(0) != top)
- {
- throw ParseException(
- fmt::format("Node '{}' is an in-place layer but "
- "doesn't have exactly one bottom, or it doesn't match its top. "
- "#bottoms={}, first bottom is {}, top is {} {}",
- layer2.name(),
- layer2.bottom(0),
- top,
- CHECK_LOCATION().AsString()));
- }
- layer2.set_bottom(0, newTop);
- }
- }
-}
-
-// Note: can move to CaffeParser when/if we optimise the text/string format
-// to load on a layer by layer basis
-void ICaffeParser::CaffeParserImpl::LoadNetParam(NetParameter& netParameter)
-{
- // Caffe models sometimes have an implicit input layer.
- // In that case, add an explicit one.
- if (netParameter.input_size() > 0)
- {
- LayerParameter* newLayer = netParameter.add_layer();
-
- newLayer->set_type("Input");
- newLayer->set_name(netParameter.input(0));
- newLayer->add_top(netParameter.input(0));
-
- InputParameter* inputParam = newLayer->mutable_input_param();
- BlobShape* shape = inputParam->add_shape();
-
- int dim_size = netParameter.input_dim_size();
- for (int i = 0; i < dim_size; ++i)
- {
- shape->add_dim(netParameter.input_dim(i));
- }
- }
-
- // Replaces in-place layers with regular ones to make the rest of the parsing easier.
- ResolveInPlaceLayers(netParameter);
-
- // Creates a lookup of Caffe layers by name.
- for (int i = 0; i < netParameter.layer_size(); ++i)
- {
- const caffe::LayerParameter& layer = netParameter.layer(i);
- for (int i = 0; i < layer.top_size(); ++i)
- {
- m_CaffeLayersByTopName[layer.top(i)] = &layer;
- }
- }
-
- // Finds the output layers the user requested.
- std::vector<const caffe::LayerParameter*> targetLayers;
- for (const std::string& requestedOutputName : m_RequestedOutputs)
- {
- auto nodeIt = m_CaffeLayersByTopName.find(requestedOutputName);
- if (nodeIt == m_CaffeLayersByTopName.end())
- {
- throw ParseException(
- fmt::format("Couldn't find requested output layer '{}' in graph {}",
- requestedOutputName,
- CHECK_LOCATION().AsString()));
- }
- targetLayers.push_back(nodeIt->second);
- }
-
- // Sorts them into a linear ordering such that all inputs of a node are before the node itself.
- std::vector<const caffe::LayerParameter*> sortedNodes;
- if (!armnnUtils::GraphTopologicalSort<const caffe::LayerParameter*>(
- targetLayers,
- [this](const caffe::LayerParameter* node)
- {
- return GetInputs(*node);
- },
- sortedNodes))
- {
- throw ParseException(
- fmt::format("Cycle detected in graph. #nodes: {} {}",
- sortedNodes.size(),
- CHECK_LOCATION().AsString()));
- }
-
- // Parses each node in order, knowing that all inputs of a node will be processed before the node itself.
- for (const caffe::LayerParameter* current : sortedNodes)
- {
- auto it = ms_CaffeLayerNameToParsingFunctions.find(current->type());
- if (it == ms_CaffeLayerNameToParsingFunctions.end())
- {
- throw ParseException(
- fmt::format("Unsupported layer type: '{}' for layer {} {}",
- current->type(),
- current->name(),
- CHECK_LOCATION().AsString()));
- }
- auto func = it->second;
- (this->*func)(*current);
- }
-
- // Adds ArmNN output layers connected to each requested output.
- for (const std::string& requestedOutput : m_RequestedOutputs)
- {
- armnn::IOutputSlot& outputSlot = GetArmnnOutputSlotForCaffeTop(requestedOutput);
-
- const armnn::LayerBindingId outputId = armnn::numeric_cast<armnn::LayerBindingId>(
- m_NetworkOutputsBindingInfo.size());
- armnn::IConnectableLayer* const outputLayer = m_Network->AddOutputLayer(outputId, requestedOutput.c_str());
- outputSlot.Connect(outputLayer->GetInputSlot(0));
-
- TrackOutputBinding(outputLayer, outputId, outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo());
- }
-}
-
-INetworkPtr ICaffeParser::CaffeParserImpl::CreateNetworkFromTextFile(const char* graphFile,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- FILE* fd = fopen(graphFile, "r");
-
- if (fd == nullptr)
- {
- throw FileNotFoundException(
- fmt::format("Failed to open graph file: {} {}",
- graphFile,
- CHECK_LOCATION().AsString()));
- }
-
- // Parses the file into a message.
- NetParameter netParam;
- auto input = new google::protobuf::io::FileInputStream(fileno(fd));
- bool success = google::protobuf::TextFormat::Parse(input, &netParam);
- delete input;
- fclose(fd);
-
- if (!success)
- {
- throw ParseException(
- fmt::format("Failed to parse graph file: {} {}",
- graphFile,
- CHECK_LOCATION().AsString()));
- }
-
- return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs);
-}
-
-INetworkPtr ICaffeParser::CaffeParserImpl::CreateNetworkFromString(const char* protoText,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- // Parses the string into a message.
- NetParameter netParam;
- bool success = google::protobuf::TextFormat::ParseFromString(protoText, &netParam);
-
- if (!success)
- {
- throw ParseException(
- fmt::format("Failed to parse graph string {}",
- CHECK_LOCATION().AsString()));
- }
-
- return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs);
-}
-
-INetworkPtr CaffeParser::CreateNetworkFromBinaryFile(const char* graphFile,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- FILE* fd = fopen(graphFile, "rb");
-
- if (fd == nullptr)
- {
- throw FileNotFoundException(
- fmt::format("Failed to open graph file at: {} {}",
- graphFile,
- CHECK_LOCATION().AsString()));
- }
-
- // Parses the file into a message.
- NetParameter netParam;
-
- FileInputStream inStream(fileno(fd));
- CodedInputStream codedStream(&inStream);
- codedStream.SetTotalBytesLimit(INT_MAX);
- bool success = netParam.ParseFromCodedStream(&codedStream);
- fclose(fd);
-
- if (!success)
- {
- throw ParseException(
- fmt::format("Failed to parse protobuf file: {} {}",
- graphFile,
- CHECK_LOCATION().AsString()));
- }
-
- return CreateNetworkFromNetParameter(netParam, inputShapes, requestedOutputs);
-}
-
-// Note: can move to CaffeParser when/if we optimise the text/string format
-// to load on a layer by layer basis
-INetworkPtr ICaffeParser::CaffeParserImpl::CreateNetworkFromNetParameter(NetParameter& netParam,
- const std::map<std::string, armnn::TensorShape>& inputShapes,
- const std::vector<std::string>& requestedOutputs)
-{
- m_NetworkInputsBindingInfo.clear();
- m_NetworkOutputsBindingInfo.clear();
-
- m_Network = INetwork::Create();
-
- m_InputShapes = inputShapes;
- if (requestedOutputs.size() == 0)
- {
- throw ParseException("requestedOutputs must have at least one entry");
- }
- m_RequestedOutputs = requestedOutputs;
-
- try
- {
- LoadNetParam(netParam);
- }
- catch (const ParseException& e)
- {
- Cleanup();
- throw e;
- }
-
- Cleanup();
-
- return move(m_Network);
-}
-
-const std::string ICaffeParser::CaffeParserImpl::GetVersion()
-{
- return CAFFE_PARSER_VERSION;
-}
-
-void ICaffeParser::CaffeParserImpl::Cleanup() {
- // cleanup, in case we reuse this parser
- m_InputShapes.clear();
- m_RequestedOutputs.clear();
- m_ArmnnOutputSlotForCaffeTop.clear();
- // NOTE: when we get the text/string format
- // optimised for memory then this data structure can
- // also move to the CaffeParser class
- m_CaffeLayersByTopName.clear();
-}
-
-}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-06 22:23:44 +0000