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onnx2trt_utils.cpp
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onnx2trt_utils.cpp
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/*
* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#include "onnx2trt_utils.hpp"
#include "OnnxAttrs.hpp"
#include "ShapeTensor.hpp"
#include <set>
namespace onnx2trt
{
NodeImportResult activationHelper(IImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node,
std::vector<TensorOrWeights>& inputs, nvinfer1::ActivationType op, float* alpha, float* beta)
{
nvinfer1::ITensor& input = convertToTensor(inputs.at(0), ctx);
ASSERT(input.getType() != nvinfer1::DataType::kINT32 && input.getType() != nvinfer1::DataType::kBOOL
&& "TensorRT does not support activations on INT32 or BOOL inputs!", ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::IActivationLayer* layer = ctx->network()->addActivation(input, op);
if (alpha)
{
layer->setAlpha(*alpha);
}
if (beta)
{
layer->setBeta(*beta);
}
ctx->registerLayer(layer, node.name());
return {{layer->getOutput(0)}};
}
nvinfer1::ITensor* addClip(IImporterContext* ctx, nvinfer1::ITensor* input, float clip)
{
if (clip >= 0.f)
{
nvinfer1::IActivationLayer* layer = ctx->network()->addActivation(*input, nvinfer1::ActivationType::kCLIP);
layer->setAlpha(-clip);
layer->setBeta(clip);
return layer->getOutput(0);
}
return input;
};
NodeImportResult argMinMaxHelper(IImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node,
std::vector<TensorOrWeights>& inputs, nvinfer1::TopKOperation op)
{
nvinfer1::ITensor& tensor = convertToTensor(inputs.at(0), ctx);
ASSERT(tensor.getType() != nvinfer1::DataType::kINT32, ErrorCode::kUNSUPPORTED_NODE);
// Get attributes.
OnnxAttrs attrs(node, ctx);
int keepdims = attrs.get("keepdims", 1);
int axis = attrs.get("axis", 0);
// Insert a TopK layer with k set to 1.
int nbDims = tensor.getDimensions().nbDims;
TRT_CHECK(convertAxis(axis, nbDims));
uint32_t axisMask = 1 << axis;
nvinfer1::ITopKLayer* layer = ctx->network()->addTopK(tensor, op, 1, axisMask);
ctx->registerLayer(layer, node.name());
ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
// We don't care about the TopK values, just the indices.
nvinfer1::ITensor* indices = layer->getOutput(1);
indices->setType(nvinfer1::DataType::kINT32);
if (keepdims)
{
// The default behavior of the TopK layer is to keepdims.
return {{indices}};
}
else
{
// Otherwise, we need to squeeze the axis dimension
std::vector<int> axes{axis};
indices = squeezeTensor(ctx, node, *indices, axes);
return {{indices}};
}
}
//! If t has rank less than nbDims, reshape it to have nbDims by prepending ones to its dimensions.
//! Assert failure if t has rank greater than nbDims.
static Status broadcastTensor(IImporterContext* ctx, nvinfer1::ITensor*& t, const int nbDims)
{
ASSERT(ctx->getOpsetVersion() >= 7 && "Pre-opset 7 broadcasting is unsupported in this version of the ONNX parser", ErrorCode::kUNSUPPORTED_NODE);
const auto inputDims = shapeOf(*t);
const int nbInputDims = inputDims.size();
assert(nbInputDims <= nbDims);
if (nbInputDims < nbDims)
{
nvinfer1::IShuffleLayer* reshape = addShuffle(ctx, *t, concat(ctx, fillShapeVector(ctx, 1, shapeVector(nbDims - nbInputDims)), shapeOf(*t)));
t = reshape->getOutput(0);
}
return Status::success();
}
Status broadcastTensors(IImporterContext* ctx, nvinfer1::ITensor*& t1, nvinfer1::ITensor*& t2)
{
const int t1Dims = t1->getDimensions().nbDims;
const int t2Dims = t2->getDimensions().nbDims;
if (t1Dims == t2Dims)
{
return Status::success();
}
if (t1Dims > t2Dims)
{
return broadcastTensor(ctx, t2, t1Dims);
}
return broadcastTensor(ctx, t1, t2Dims);
}
Status broadcastTensors(IImporterContext* ctx, nvinfer1::ITensor*& t1, nvinfer1::ITensor*& t2, nvinfer1::ITensor*& t3)
{
const int maxDims = std::max({t1->getDimensions().nbDims, t2->getDimensions().nbDims, t3->getDimensions().nbDims});
TRT_CHECK(broadcastTensor(ctx, t1, maxDims));
TRT_CHECK(broadcastTensor(ctx, t2, maxDims));
TRT_CHECK(broadcastTensor(ctx, t3, maxDims));
return Status::success();
}
bool canUseLinearResize(const size_t scaleSize, const float* scaleFactors)
{
// Linear resize supports up to 3D resize on the outermost dimensions.
if (scaleSize > 3)
{
for (size_t i = 0; i < scaleSize - 3; i++)
{
if (scaleFactors[i] != 1)
{
return false;
}
}
}
return true;
}
nvinfer1::ITensor* constantOfShape(IImporterContext* ctx, const ::ONNX_NAMESPACE::NodeProto& node, nvinfer1::ITensor* constant, nvinfer1::ITensor* shape)
{
int rank = shape->getDimensions().d[0];
std::vector<int> starts(rank);
std::fill(starts.begin(), starts.end(), 0);
nvinfer1::Dims strides{rank};
std::fill(strides.d, strides.d + strides.nbDims, 0);
// Slice will not work if constant does not have the same rank as start/size/strides.
nvinfer1::Dims unsqueezeDims{rank};
std::fill(unsqueezeDims.d, unsqueezeDims.d + unsqueezeDims.nbDims, 1);
nvinfer1::IShuffleLayer* unsqueeze = ctx->network()->addShuffle(*constant);
unsqueeze->setReshapeDimensions(unsqueezeDims);
unsqueeze->setZeroIsPlaceholder(false);
constant = unsqueeze->getOutput(0);
nvinfer1::ISliceLayer* broadcast = ctx->network()->addSlice(*constant, nvinfer1::Dims{}, nvinfer1::Dims{}, strides);
broadcast->setInput(1,
*addConstant(ctx, starts, ::ONNX_NAMESPACE::TensorProto_DataType_INT32, nvinfer1::Dims{1, rank})->getOutput(0));
broadcast->setInput(2, *shape);
ctx->registerLayer(broadcast, node.name());
return broadcast->getOutput(0);
}
Status convertAxis(int& axis, int nbDims)
{
// Support negative indexing
if (axis < 0)
{
axis += nbDims;
}
ASSERT(axis >= 0 && axis < nbDims, ErrorCode::kUNSUPPORTED_NODE);
return Status::success();
}
bool convertDtype(int32_t onnx_dtype, nvinfer1::DataType* trt_dtype)
{
switch (onnx_dtype)
{
case ::ONNX_NAMESPACE::TensorProto::FLOAT: *trt_dtype = nvinfer1::DataType::kFLOAT; break;
case ::ONNX_NAMESPACE::TensorProto::INT8: *trt_dtype = nvinfer1::DataType::kINT8; break;
case ::ONNX_NAMESPACE::TensorProto::FLOAT16: *trt_dtype = nvinfer1::DataType::kHALF; break;
case ::ONNX_NAMESPACE::TensorProto::BOOL: *trt_dtype = nvinfer1::DataType::kBOOL; break;
case ::ONNX_NAMESPACE::TensorProto::INT32:
*trt_dtype = nvinfer1::DataType::kINT32;
break;
// See convertOnnxWeights for sanity check if all values can be safetly downcasted to INT32
case ::ONNX_NAMESPACE::TensorProto::INT64: *trt_dtype = nvinfer1::DataType::kINT32; break;
default:
std::cerr << "Unsupported ONNX data type: " << getDtypeName(onnx_dtype) << " (" << std::to_string(onnx_dtype)
<< ")" << std::endl;
return false;
}
return true;
}
int32_t* convertINT64(const int64_t* weightValues, nvinfer1::Dims shape, IImporterContext* ctx)
{
static bool logged = false;
if (!logged)
{
LOG_WARNING(
"Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. "
"Attempting to cast down to INT32.");
logged = true;
}
const size_t nbWeights = volume(shape);
int32_t* int32Weights{
reinterpret_cast<int32_t*>(ctx->createTempWeights(::ONNX_NAMESPACE::TensorProto::INT32, shape).values)};
bool outOfBounds{false};
for (size_t i = 0; i < nbWeights; i++)
{
if (weightValues[i] > static_cast<int64_t>(INT32_MAX) || weightValues[i] < static_cast<int64_t>(INT32_MIN))
{
int32Weights[i] = static_cast<int32_t>(
std::max(std::min(weightValues[i], static_cast<int64_t>(INT32_MAX)), static_cast<int64_t>(INT32_MIN)));
LOG_VERBOSE("Weight at index " << i << ": " << weightValues[i]
<< " is out of range. Clamping to: " << int32Weights[i]);
outOfBounds = true;
}
else
{
int32Weights[i] = static_cast<int32_t>(weightValues[i]);
}
}
if (outOfBounds)
{
LOG_WARNING("One or more weights outside the range of INT32 was clamped");
}
return int32Weights;
}
bool convertOnnxPadding(const std::vector<int64_t>& onnxPadding, nvinfer1::Dims2* begPadding, nvinfer1::Dims2* endPadding)
{
const size_t size = onnxPadding.size();
const size_t half = size / 2;
for (size_t i = 0; i < half - 2; i++)
{
if (onnxPadding[i] != 0)
{
return false;
}
}
begPadding->d[0] = onnxPadding[half - 2];
begPadding->d[1] = onnxPadding[half - 1];
for (size_t i = half; i < size - 2; i++)
{
if (onnxPadding[i] != 0)
{
return false;
}
}
endPadding->d[0] = onnxPadding[size - 2];
endPadding->d[1] = onnxPadding[size - 1];
return true;
}
onnx2trt::ShapedWeights createZeroShifts(const onnx2trt::ShapedWeights& shiftInt8, int32_t type, IImporterContext* ctx)
{
const auto* v = static_cast<const int8_t*>(shiftInt8.values);
if (!std::all_of(v, v + shiftInt8.count(), [](int8_t x) { return x == 0; }))
{
LOG_WARNING("TensorRT currenly supports only zero shifts values for QuatizeLinear/DequantizeLinear ops");
}
auto shift = ctx->createTempWeights(type, shiftInt8.shape);
float* sh = static_cast<float*>(shift.values);
for (int i = 0, n = shift.count(); i < n; i++)
{
sh[i] = 0.0f;
}
return shift;
}
template <typename DataType>
DataType* convertINT32Data(const int32_t* weightValues, nvinfer1::Dims shape, int32_t onnxdtype, IImporterContext* ctx)
{
const size_t nbWeights = volume(shape);
DataType* newWeights{
reinterpret_cast<DataType*>(ctx->createTempWeights(onnxdtype, shape).values)};
for (size_t i = 0; i < nbWeights; i++)
{
newWeights[i] = static_cast<DataType>(weightValues[i]);
}
return newWeights;
}
bool convertOnnxWeights(
const ::ONNX_NAMESPACE::TensorProto& onnxTensor, onnx2trt::ShapedWeights* weights, IImporterContext* ctx)
{
void* dataPtr{nullptr};
size_t nbytes{0};
nvinfer1::Dims shape;
auto onnxDtype = onnxTensor.data_type();
// ONNX weight values can be stored in either the TensorProto itself, or in an external file in the case
// of large models. Check for this here.
auto dataLocation = onnxTensor.data_location();
// External Data
if (dataLocation == 1)
{
std::string location{""};
int offset{0};
int length{0};
// onnxTensor.external_data() is a String : String map that holds metadata about how to read from an external file
for (auto onnxMapEntry : onnxTensor.external_data())
{
auto keyName = onnxMapEntry.key();
if (keyName == "location")
{
location = onnxMapEntry.value();
}
else if (keyName == "offset")
{
offset = std::atoi(onnxMapEntry.value().c_str());
}
else if (keyName == "length")
{
length = std::atoi(onnxMapEntry.value().c_str());
}
// Not used at the moment
else if (keyName == "checksum")
{
continue;
}
else
{
LOG_ERROR("Key value of: " << keyName << " was not expected!");
return false;
}
}
// Buffer to hold the data read from the file
std::vector<char> dataBuf;
// Will update dataBuf and nbytes by reference.
if (!parseExternalWeights(ctx, location, ctx->getOnnxFileLocation(), offset, length, dataBuf, nbytes))
{
return false;
}
shape.nbDims = onnxTensor.dims().size();
std::copy(onnxTensor.dims().begin(), onnxTensor.dims().end(), shape.d);
// For weights parsed from external files, createTempWeights is necessary to keep them in scope
ShapedWeights externalWeights;
// Downcast INT64 weights to INT32 weights before copying the values to externalWeights
if (onnxDtype == ::ONNX_NAMESPACE::TensorProto::INT64)
{
dataPtr = dataBuf.data();
dataPtr = convertINT64(reinterpret_cast<const int64_t*>(dataPtr), shape, ctx);
nbytes = nbytes / 2;
onnxDtype = ::ONNX_NAMESPACE::TensorProto::INT32;
externalWeights = ctx->createTempWeights(onnxDtype, shape);
std::memcpy(externalWeights.values, dataPtr, nbytes);
}
// Copy weight values directly to externalWeights
else
{
externalWeights = ctx->createTempWeights(onnxDtype, shape);
std::memcpy(externalWeights.values, dataBuf.data(), nbytes);
}
*weights = externalWeights;
return true;
}
// Weights information is within the TensorProto itself
else
{
// Pass through for optional (empty) initializers for unused attributes.
if (isOnnxTensorEmpty(onnxTensor))
{
auto empty = onnx2trt::ShapedWeights::empty(::ONNX_NAMESPACE::TensorProto::FLOAT);
*weights = empty;
return true;
}
shape.nbDims = onnxTensor.dims().size();
std::copy(onnxTensor.dims().begin(), onnxTensor.dims().end(), shape.d);
if (onnxDtype == ::ONNX_NAMESPACE::TensorProto::INT64)
{
if (onnxTensor.raw_data().size() > 0)
{
dataPtr = convertINT64(reinterpret_cast<const int64_t*>(onnxTensor.raw_data().data()), shape, ctx);
nbytes = onnxTensor.raw_data().size() / 2;
}
else if (onnxTensor.int64_data().size() > 0)
{
dataPtr = convertINT64(onnxTensor.int64_data().data(), shape, ctx);
nbytes = onnxTensor.int64_data().size() * sizeof(int32_t);
}
onnxDtype = ::ONNX_NAMESPACE::TensorProto::INT32;
}
// Check for supported types that can be found in the int32_data field in the TensorProto
// https://github.com/onnx/onnx/blob/master/onnx/onnx.proto#L382-L387
else if (onnxDtype == ::ONNX_NAMESPACE::TensorProto::INT32 || onnxDtype == ::ONNX_NAMESPACE::TensorProto::FLOAT16
|| onnxDtype == ::ONNX_NAMESPACE::TensorProto::INT8 || onnxDtype == ::ONNX_NAMESPACE::TensorProto::BOOL)
{
if (onnxTensor.raw_data().size() > 0)
{
dataPtr = (void*)(onnxTensor.raw_data().data());
nbytes = onnxTensor.raw_data().size();
}
else
{
switch (onnxDtype)
{
// Import INT32 and FP16 weights as is.
case ::ONNX_NAMESPACE::TensorProto::INT32:
case ::ONNX_NAMESPACE::TensorProto::FLOAT16:
dataPtr = (void*) (onnxTensor.int32_data().data());
break;
case ::ONNX_NAMESPACE::TensorProto::INT8:
dataPtr = convertINT32Data<int8_t>(onnxTensor.int32_data().data(), shape, onnxDtype, ctx);
break;
case ::ONNX_NAMESPACE::TensorProto::BOOL:
dataPtr = convertINT32Data<uint8_t>(onnxTensor.int32_data().data(), shape, onnxDtype, ctx);
break;
default:
LOG_ERROR("Found unsupported datatype (" << onnxDtype << ") when importing initializer: " << onnxTensor.name());
break;
}
nbytes = onnxTensor.int32_data().size() * getDtypeSize(onnxDtype);
}
}
else if (onnxDtype == ::ONNX_NAMESPACE::TensorProto::FLOAT)
{
if (onnxTensor.raw_data().size() > 0)
{
dataPtr = (void*)(onnxTensor.raw_data().data());
nbytes = onnxTensor.raw_data().size();
}
else
{
dataPtr = (void*)(onnxTensor.float_data().data());
nbytes = onnxTensor.float_data().size() * sizeof(float);
}
}
else
{
LOG_ERROR("Found unsupported datatype (" << onnxDtype << ") when importing initializer: " << onnxTensor.name());
return false;
}
onnx2trt::ShapedWeights trt_weights(onnxDtype, dataPtr, shape);
// Sanity check that weights were converted properly
if (trt_weights.size_bytes() != nbytes)
{
LOG_ERROR("Size mismatch when importing initializer: " << onnxTensor.name() << ". Expected size: " << nbytes << " , actual size: " << trt_weights.size_bytes());
return false;
}
*weights = trt_weights;
return true;
}
}
nvinfer1::ITensor* convertToScalar(IImporterContext* ctx, nvinfer1::ITensor* inpTensor)
{
if (inpTensor->getDimensions().nbDims == 0)
{
return inpTensor;
}
const auto tensorVolume = volume(inpTensor->getDimensions());
if (tensorVolume != 1)
{
LOG_VERBOSE("Cannot convert tensor to scalar. Note: Tensor dimensions were: "
<< inpTensor->getDimensions() << ", with volume: " << tensorVolume);
return nullptr;
}
nvinfer1::IShuffleLayer* reshape = ctx->network()->addShuffle(*inpTensor);
reshape->setReshapeDimensions(nvinfer1::Dims{0});
// Do not need to call setZeroIsPlaceholder, since reshape dimensions are empty.
return reshape->getOutput(0);
}
nvinfer1::ITensor& convertToTensor(TensorOrWeights& input, IImporterContext* ctx)
{
if (input.is_tensor())
{
return input.tensor();
}
else
{
// Handle non-tensor indices input by adding a new constant layer to the network.
ShapedWeights& weights = input.weights();
// Note the TRT doesn't natively handle boolean weights. First create an INT32 weights copy of the boolean weights, then cast it back to bool within TRT.
if (weights.type == ::ONNX_NAMESPACE::TensorProto::BOOL)
{
ShapedWeights convertedWeights = ctx->createTempWeights(::ONNX_NAMESPACE::TensorProto::INT32, weights.shape);
int* intValues = static_cast<int*>(weights.values);
std::memcpy(convertedWeights.values, intValues, weights.count() * sizeof(int));
auto* boolTensor = ctx->network()->addConstant(convertedWeights.shape, convertedWeights)->getOutput(0);
auto* castLayer = ctx->network()->addIdentity(*boolTensor);
castLayer->setOutputType(0,nvinfer1::DataType::kBOOL);
return *(castLayer->getOutput(0));
}
else
{
auto* constantLayer = ctx->network()->addConstant(weights.shape, weights);
// Register layer and constant name (if set) into RefitMap:
if (weights.getName())
{
ctx->registerLayer(constantLayer, weights.getName());
ctx->insertRefitMap(weights.getName(), weights.getName(), nvinfer1::WeightsRole::kCONSTANT);
}
return *(constantLayer->getOutput(0));
}
}
}
nvinfer1::ITensor* convertToScalar(TensorOrWeights& input, IImporterContext* ctx)
{
if (input.is_tensor())
{
return convertToScalar(ctx, &input.tensor());
}
else
{
ShapedWeights& weights = input.weights();
if (volume(weights.shape) != 1)
{
LOG_VERBOSE("Cannot convert weights to scalar. Note: Tensor dimensions were: "
<< weights.shape << ", with volume: " << volume(weights.shape));
return nullptr;
}
return ctx->network()->addConstant(nvinfer1::Dims{0, {0}}, weights)->getOutput(0);
}
}
bool convertWeightDescriptor(
onnxTensorDescriptorV1 const& desc, onnx2trt::ShapedWeights* weights, IImporterContext* ctx)
{
nvinfer1::Dims shape;
shape.nbDims = desc.dimensions;
// Special case for scalars
if (shape.nbDims == 0)
{
shape.nbDims = 1;
shape.d[0] = 1;
}
else
{
std::copy(desc.shape, desc.shape + desc.dimensions, shape.d);
}
size_t element_count = 1;
for (int i = 0; i < shape.nbDims; ++i)
{
element_count *= shape.d[i];
}
void* dataPtr;
size_t nbytes;
int32_t dtype;
dataPtr = (void*) (desc.buffer);
if (desc.dataType == ONNXIFI_DATATYPE_FLOAT32)
{
dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT;
nbytes = element_count * sizeof(float);
}
else if (desc.dataType == ONNXIFI_DATATYPE_FLOAT16)
{
dtype = ::ONNX_NAMESPACE::TensorProto::FLOAT16;
nbytes = element_count * sizeof(float) / 2;
}
else if (desc.dataType == ONNXIFI_DATATYPE_INT32)
{
dtype = ::ONNX_NAMESPACE::TensorProto::INT32;
nbytes = element_count * sizeof(int32_t);
}
else if (desc.dataType == ONNXIFI_DATATYPE_INT64)
{
dataPtr = convertINT64(reinterpret_cast<const int64_t*>(desc.buffer), shape, ctx);
dtype = ::ONNX_NAMESPACE::TensorProto::INT32;
nbytes = element_count * sizeof(int32_t);
}
else
{
// Unsupported format
return false;
}
onnx2trt::ShapedWeights trt_weights(dtype, dataPtr, shape);
(void) nbytes;
assert(trt_weights.size_bytes() == nbytes);
*weights = trt_weights;
return true;
}
int divCeil(int n, int d)
{
return (n - 1) / d + 1;
}
bool elementwiseCheck(const std::vector<TensorOrWeights>& inputs, const nvinfer1::ElementWiseOperation op)
{
switch (op)
{
// These operations only support boolean inputs
case nvinfer1::ElementWiseOperation::kAND:
case nvinfer1::ElementWiseOperation::kOR:
case nvinfer1::ElementWiseOperation::kXOR:
if (!std::all_of(inputs.begin(), inputs.end(), [](const TensorOrWeights& input) {return input.isBool();}))
{
return false;
}
break;
// These operations do not support boolean types
case nvinfer1::ElementWiseOperation::kDIV:
case nvinfer1::ElementWiseOperation::kEQUAL:
case nvinfer1::ElementWiseOperation::kFLOOR_DIV:
case nvinfer1::ElementWiseOperation::kGREATER:
case nvinfer1::ElementWiseOperation::kLESS:
case nvinfer1::ElementWiseOperation::kMAX:
case nvinfer1::ElementWiseOperation::kMIN:
case nvinfer1::ElementWiseOperation::kPROD:
case nvinfer1::ElementWiseOperation::kSUB:
case nvinfer1::ElementWiseOperation::kSUM:
if (std::any_of(inputs.begin(), inputs.end(), [](const TensorOrWeights& input) {return input.isBool();}))
{
return false;
}
break;
// Pow does not support bool or INT32 types
case nvinfer1::ElementWiseOperation::kPOW:
if (std::any_of(inputs.begin(), inputs.end(), [](const TensorOrWeights& input) {return input.isBool() || input.isInt32();}))
{
return false;
}
break;
}
return true;
}
NodeImportResult elementwiseHelper(IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node,
std::vector<TensorOrWeights>& inputs, nvinfer1::ElementWiseOperation binary_op)
{
ASSERT(!inputs.empty(), ErrorCode::kINVALID_NODE);
ASSERT(elementwiseCheck(inputs, binary_op), ErrorCode::kUNSUPPORTED_NODE);
std::vector<nvinfer1::ITensor*> inputTensors;
int maxNbDims = -1;
for (auto input : inputs)
{
maxNbDims = std::max(maxNbDims, input.shape().nbDims);
}
for (auto input : inputs)
{
auto* tensor_ptr = &convertToTensor(input, ctx);
// Broadcast all input tensors to size of maxNbDims
broadcastTensor(ctx, tensor_ptr, maxNbDims);
ASSERT(tensor_ptr->getDimensions().nbDims == maxNbDims && "Failed to broadcast tensors elementwise!",
ErrorCode::kUNSUPPORTED_NODE);
inputTensors.push_back(tensor_ptr);
}
// Use the first tensor input as the base for the elementwise operation
nvinfer1::ITensor* combined = inputTensors.at(0);
if (inputTensors.size() == 1)
{
// Note: Single input must be wrapped in identity to avoid messing up network outputs
return {{identity(ctx, combined)}};
}
for (size_t i = 1; i < inputTensors.size(); ++i)
{
nvinfer1::ITensor* tensor = inputTensors.at(i);
ASSERT(tensor->getDimensions().nbDims == combined->getDimensions().nbDims, ErrorCode::kUNSUPPORTED_NODE);
auto* layer = ctx->network()->addElementWise(*combined, *tensor, binary_op);
ctx->registerLayer(layer, node.name());
ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
combined = layer->getOutput(0);
}
return {{combined}};
}
nvinfer1::ITensor* flattenTensor(IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, nvinfer1::ITensor& tensor, int axis, bool regLayer)
{
const auto dims = shapeOf(tensor);
const auto d0 = product(ctx, dims, 0, axis, 1);
const auto d1 = product(ctx, dims, axis, dims.size(), 1);
// ShuffleLayer here interprets dim extent 0 as empty dim to support empty tensor
nvinfer1::IShuffleLayer* flattenLayer = addShuffle(ctx, tensor, concat(ctx, d0, d1), /*zeroIsPlaceholder=*/false);
if (regLayer)
{
ctx->registerLayer(flattenLayer, node.name());
}
return flattenLayer->getOutput(0);
}
nvinfer1::ITensor* gatherDimension(IImporterContext* ctx, nvinfer1::ITensor* shapeTensor, int dim, nvinfer1::Dims shape)
{
auto& axisValue = *addConstantScalar(ctx, dim, ::ONNX_NAMESPACE::TensorProto_DataType_INT32, shape)->getOutput(0);
return ctx->network()->addGather(*shapeTensor, axisValue, 0)->getOutput(0);
}
// Helper function to generate padding values for convTranspose
void generatePadding(nvinfer1::Dims inputShape, nvinfer1::Dims outputShape, nvinfer1::Dims kernelSize,
nvinfer1::Dims strides, nvinfer1::Dims dilations, const int nbSpatialDims, nvinfer1::Dims& begPadding,
nvinfer1::Dims& endPadding, nvinfer1::Dims& outputPadding, nvinfer1::PaddingMode paddingMode)
{
nvinfer1::Dims totalPadding {nbSpatialDims, {}};
// Pre and post padding calculated as per https://github.com/onnx/onnx/blob/master/docs/Operators.md#ConvTranspose
for (int i = 0; i < nbSpatialDims; i++)
{
totalPadding.d[i] = strides.d[i] * (inputShape.d[2+i] - 1) + outputPadding.d[i] + ((kernelSize.d[i] - 1) * dilations.d[i] + 1) - outputShape.d[i];
// Same upper is calculated differently
if (paddingMode != nvinfer1::PaddingMode::kSAME_UPPER)
{
begPadding.d[i] = totalPadding.d[i] / 2;
endPadding.d[i] = totalPadding.d[i] - (totalPadding.d[i] / 2);
}
else
{
begPadding.d[i] = totalPadding.d[i] - (totalPadding.d[i] / 2);
endPadding.d[i] = (totalPadding.d[i] / 2);
}
}
}
float getActivationDefaultAlpha(nvinfer1::ActivationType type)
{
switch (type)
{
case nvinfer1::ActivationType::kRELU: return 0.f;
case nvinfer1::ActivationType::kSIGMOID: return 0.f;
case nvinfer1::ActivationType::kTANH: return 0.f;
case nvinfer1::ActivationType::kLEAKY_RELU: return 0.01f;
case nvinfer1::ActivationType::kELU: return 1.0f;
case nvinfer1::ActivationType::kSELU: return 1.67326319217681884765625f;
case nvinfer1::ActivationType::kSOFTSIGN: return 0.f;
case nvinfer1::ActivationType::kSOFTPLUS: return 0.f;
case nvinfer1::ActivationType::kCLIP: return 0.f;
case nvinfer1::ActivationType::kHARD_SIGMOID: return 0.2f;
case nvinfer1::ActivationType::kSCALED_TANH: return 1.0f;
case nvinfer1::ActivationType::kTHRESHOLDED_RELU: return 1.0f;
}
throw std::runtime_error{"Unrecognized activation type"};
}
float getActivationDefaultBeta(nvinfer1::ActivationType type)
{
switch (type)
{
case nvinfer1::ActivationType::kRELU: return 0.f;
case nvinfer1::ActivationType::kSIGMOID: return 0.f;
case nvinfer1::ActivationType::kTANH: return 0.f;
case nvinfer1::ActivationType::kLEAKY_RELU: return 0.f;
case nvinfer1::ActivationType::kELU: return 0.f;
case nvinfer1::ActivationType::kSELU: return 1.05070102214813232421875f;
case nvinfer1::ActivationType::kSOFTSIGN: return 0.f;
case nvinfer1::ActivationType::kSOFTPLUS: return 0.f;
case nvinfer1::ActivationType::kCLIP: return 0.f;
case nvinfer1::ActivationType::kHARD_SIGMOID: return 0.5f;
case nvinfer1::ActivationType::kSCALED_TANH: return 1.0f;
case nvinfer1::ActivationType::kTHRESHOLDED_RELU: return 0.f;
}
throw std::runtime_error{"Unrecognized activation type"};
}
nvinfer1::ITensor* getAxisLength(IImporterContext* ctx, nvinfer1::ITensor* inpTensor, int axis, nvinfer1::Dims shape)
{
// fast path for static dims
auto dims = inpTensor->getDimensions();
int d = dims.d[axis];
if (d >= 0)
{
return addConstantScalar(ctx, d, ::ONNX_NAMESPACE::TensorProto_DataType_INT32, shape)->getOutput(0);
}
else
{
nvinfer1::ITensor* inpShape = ctx->network()->addShape(*inpTensor)->getOutput(0);
return gatherDimension(ctx, inpShape, axis, shape);
}
}
int getConvOutputSize(int input_size, int filter_size, int stride, int dilation_rate, int total_padding);
const char* getDtypeName(int32_t onnxDtype)
{
switch (onnxDtype)
{
case ::ONNX_NAMESPACE::TensorProto::FLOAT: return "FLOAT";
case ::ONNX_NAMESPACE::TensorProto::UINT8: return "UINT8";
case ::ONNX_NAMESPACE::TensorProto::INT8: return "INT8";
case ::ONNX_NAMESPACE::TensorProto::UINT16: return "UINT16";
case ::ONNX_NAMESPACE::TensorProto::INT16: return "INT16";
case ::ONNX_NAMESPACE::TensorProto::INT32: return "INT32";
case ::ONNX_NAMESPACE::TensorProto::INT64: return "INT64";
case ::ONNX_NAMESPACE::TensorProto::STRING: return "STRING";
case ::ONNX_NAMESPACE::TensorProto::BOOL: return "BOOL";
case ::ONNX_NAMESPACE::TensorProto::FLOAT16: return "FLOAT16";
case ::ONNX_NAMESPACE::TensorProto::DOUBLE: return "DOUBLE";
case ::ONNX_NAMESPACE::TensorProto::UINT32: return "UINT32";
case ::ONNX_NAMESPACE::TensorProto::UINT64: return "UINT64";
case ::ONNX_NAMESPACE::TensorProto::COMPLEX64: return "COMPLEX64";
case ::ONNX_NAMESPACE::TensorProto::COMPLEX128: return "COMPLEX128";
default: return "<UNKNOWN>";
}
}
int getDtypeSize(int32_t onnxDtype)
{
switch (onnxDtype)
{
case ::ONNX_NAMESPACE::TensorProto::FLOAT16: return 2;
case ::ONNX_NAMESPACE::TensorProto::FLOAT: return 4;
case ::ONNX_NAMESPACE::TensorProto::DOUBLE: return 8;
case ::ONNX_NAMESPACE::TensorProto::COMPLEX64: return 8;
case ::ONNX_NAMESPACE::TensorProto::COMPLEX128: return 16;
case ::ONNX_NAMESPACE::TensorProto::UINT8: return 1;
case ::ONNX_NAMESPACE::TensorProto::INT8: return 1;
case ::ONNX_NAMESPACE::TensorProto::UINT16: return 2;
case ::ONNX_NAMESPACE::TensorProto::INT16: return 2;
case ::ONNX_NAMESPACE::TensorProto::UINT32:
return 4;
// Booleans are stored in int32 tensors in ONNX
case ::ONNX_NAMESPACE::TensorProto::BOOL: return 1;
case ::ONNX_NAMESPACE::TensorProto::INT32: return 4;
case ::ONNX_NAMESPACE::TensorProto::UINT64: return 8;
case ::ONNX_NAMESPACE::TensorProto::INT64: return 8;
default: return -1;
}
}
void getKernelParams(IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& onnx_node, nvinfer1::Dims* kernel_size,
nvinfer1::Dims* strides, nvinfer1::Dims* beg_padding, nvinfer1::Dims* end_padding,
nvinfer1::PaddingMode& paddingMode, bool& count_exclude_padding, nvinfer1::Dims* dilations,
nvinfer1::Dims* output_padding, const bool poolingCeilMode)
{
const int nbSpatialDims = kernel_size->nbDims;
OnnxAttrs attrs(onnx_node, ctx);
if (attrs.count("kernel_shape"))
{
auto const* onnx_kernel_size = attrs.at("kernel_shape");
setAttr(kernel_size, onnx_kernel_size, nbSpatialDims, 1);
}
if (attrs.count("strides"))
{
auto const* onnx_strides = attrs.at("strides");
setAttr(strides, onnx_strides, nbSpatialDims, 1);
}
if (dilations && attrs.count("dilations"))
{
auto const* onnx_dilations = attrs.at("dilations");
setAttr(dilations, onnx_dilations, nbSpatialDims, 1);
}
if (attrs.count("count_include_pad"))
{
auto const* include_pad = attrs.at("count_include_pad");
int val = include_pad->i();
val == 1 ? count_exclude_padding = false : count_exclude_padding = true;
}
// For ConvTranspose Layer
if (attrs.count("output_padding"))
{
*output_padding = attrs.get<nvinfer1::Dims>("output_padding");
}
paddingMode = poolingCeilMode ? nvinfer1::PaddingMode::kEXPLICIT_ROUND_UP : nvinfer1::PaddingMode::kEXPLICIT_ROUND_DOWN;
auto onnx_auto_pad = attrs.get("auto_pad", std::string("NOTSET"));
if (onnx_auto_pad != "SAME_LOWER" && onnx_auto_pad != "SAME_UPPER")
{
if (attrs.count("pads"))
{
auto onnx_padding = attrs.get<std::vector<int>>("pads");
int ndim = onnx_padding.size() / 2;
for (int i = 0; i < nbSpatialDims; ++i)
{
if (i < ndim)
{
beg_padding->d[i] = onnx_padding.at(i);
end_padding->d[i] = onnx_padding.at(i + ndim);
}
else
{
beg_padding->d[i] = 0;
end_padding->d[i] = 0;
}
}
}
if (onnx_auto_pad != "VALID" && onnx_auto_pad != "NOTSET")
{
if (onnx_auto_pad == "EXPLICIT_ROUND_UP")
{
paddingMode = nvinfer1::PaddingMode::kEXPLICIT_ROUND_UP;
}
else if (onnx_auto_pad == "CAFFE_ROUND_DOWN")
{
paddingMode = nvinfer1::PaddingMode::kCAFFE_ROUND_DOWN;
}
else if (onnx_auto_pad == "CAFFE_ROUND_UP")
{
paddingMode = nvinfer1::PaddingMode::kCAFFE_ROUND_UP;
}
}
}
else
{
// If auto_pad is SAME_LOWER or SAME_UPPER, input padding should be calculated
// "pads" attribute should not be specified
assert(!attrs.count("pads"));
// Note: ONNX is always NCHW ordering
if (onnx_auto_pad == "SAME_LOWER")
{
paddingMode = nvinfer1::PaddingMode::kSAME_LOWER;
}
else if (onnx_auto_pad == "SAME_UPPER")
{
paddingMode = nvinfer1::PaddingMode::kSAME_UPPER;
}
else
{
throw std::invalid_argument("Unexpected auto_pad value: " + onnx_auto_pad);
}
}
}
const std::string getNodeName(const ::ONNX_NAMESPACE::NodeProto& node)
{
if (node.name().empty() && (node.output_size() != 0))
{
return "node_of_" + node.output(0);
}
else
{
return node.name();
}
}
nvinfer1::ITensor* globalPoolingHelper(IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, nvinfer1::ITensor& tensor, nvinfer1::ReduceOperation op)
{
nvinfer1::Dims dims = tensor.getDimensions();
// Generate a bitmask of all 1s except the last 2 bits (N and C axes)
uint32_t reduceAxes = ((1 << dims.nbDims) - 1) & ~0b11;
auto* layer = ctx->network()->addReduce(tensor, op, reduceAxes, /*keepDimensions=*/true);
ctx->registerLayer(layer, node.name());
return layer->getOutput(0);
}
nvinfer1::IPluginCreator* importPluginCreator(const std::string& pluginName, const std::string& pluginVersion, const std::string& pluginNamespace)
{
return getPluginRegistry()->getPluginCreator(pluginName.c_str(), pluginVersion.c_str(), pluginNamespace.c_str());
}
nvinfer1::IPluginV2* createPlugin(const std::string& name, nvinfer1::IPluginCreator* pluginCreator, const std::vector<nvinfer1::PluginField>& pluginFields)
{
if (!pluginCreator)
{
return nullptr;
}
nvinfer1::PluginFieldCollection fc;
fc.nbFields = pluginFields.size();
fc.fields = pluginFields.data();
return pluginCreator->createPlugin(name.c_str(), &fc);
}
bool isDynamic(const nvinfer1::Dims& shape)
{
return std::any_of(shape.d, shape.d + shape.nbDims, [](int dim) { return dim < 0; });
}
bool isOnnxTensorEmpty(const ::ONNX_NAMESPACE::TensorProto& onnxTensor)
{
return onnxTensor.raw_data().empty() && onnxTensor.double_data().empty()
&& onnxTensor.float_data().empty() && onnxTensor.int32_data().empty()
&& onnxTensor.int64_data().empty() && onnxTensor.string_data().empty()
&& onnxTensor.uint64_data().empty();
}
bool isTransposeRequired(nvinfer1::Dims const& shape, nvinfer1::Permutation const& perm)