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builtin_op_importers.cpp
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builtin_op_importers.cpp
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/*
* Copyright (c) 2018, 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 "builtin_op_importers.hpp"
#include "onnx2trt_utils.hpp"
#include <algorithm> // For std::min, std::max
#include <numeric> // For std::iota
#include <sstream> // For std::stringstream
#include <cstring>
namespace onnx2trt
{
string_map<NodeImporter>& getBuiltinOpImporterMap()
{
static string_map<NodeImporter> builtin_op_importers;
return builtin_op_importers;
}
namespace
{
bool registerBuiltinOpImporter(std::string op, NodeImporter const& importer)
{
bool inserted = getBuiltinOpImporterMap().insert({op, importer}).second;
assert(inserted);
return inserted;
}
#define IGNORE_UNUSED_GLOBAL(x) \
static void _ignore_unused2_##x(); \
static void _ignore_unused1_##x() \
{ \
(void) _ignore_unused2_##x; \
(void) x; \
} \
static void _ignore_unused2_##x() \
{ \
(void) _ignore_unused1_##x; \
} \
struct SwallowSemicolon##x \
{ \
}
#define DECLARE_BUILTIN_OP_IMPORTER(op) \
NodeImportResult import##op( \
IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<TensorOrWeights>& inputs)
#define DEFINE_BUILTIN_OP_IMPORTER(op) \
NodeImportResult import##op( \
IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<TensorOrWeights>& inputs); \
static const bool op##_registered_builtin_op = registerBuiltinOpImporter(#op, import##op); \
IGNORE_UNUSED_GLOBAL(op##_registered_builtin_op); \
NodeImportResult import##op( \
IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<TensorOrWeights>& inputs)
#define RETURN_FIRST_OUTPUT(layer) \
do \
{ \
nvinfer1::ILayer* layer_ptr = layer; \
ASSERT(layer_ptr, ErrorCode::kUNSUPPORTED_NODE); \
return {{layer_ptr->getOutput(0)}}; \
} while (0)
#define RETURN_IDENTITY(input) \
do \
{ \
TensorOrWeights output = identity(ctx, input); \
ASSERT(output, ErrorCode::kUNSUPPORTED_NODE); \
return {{output}}; \
} while (0)
DEFINE_BUILTIN_OP_IMPORTER(Abs)
{
return apply_unary_function(ctx, inputs.at(0), nvinfer1::UnaryOperation::kABS);
}
DEFINE_BUILTIN_OP_IMPORTER(Acos)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kACOS);
}
DEFINE_BUILTIN_OP_IMPORTER(Acosh)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kACOSH);
}
DEFINE_BUILTIN_OP_IMPORTER(Add)
{
return elementwiseHelper(ctx, node, inputs, nvinfer1::ElementWiseOperation::kSUM);
}
DEFINE_BUILTIN_OP_IMPORTER(ArgMax)
{
return argMinMaxHelper(ctx, node, inputs, nvinfer1::TopKOperation::kMAX);
}
DEFINE_BUILTIN_OP_IMPORTER(ArgMin)
{
return argMinMaxHelper(ctx, node, inputs, nvinfer1::TopKOperation::kMIN);
}
DEFINE_BUILTIN_OP_IMPORTER(Asin)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kASIN);
}
DEFINE_BUILTIN_OP_IMPORTER(Asinh)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kASINH);
}
DEFINE_BUILTIN_OP_IMPORTER(Atan)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kATAN);
}
DEFINE_BUILTIN_OP_IMPORTER(Atanh)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kATANH);
}
DEFINE_BUILTIN_OP_IMPORTER(AveragePool)
{
return poolingHelper(ctx, node, inputs, nvinfer1::PoolingType::kAVERAGE);
}
NodeImportResult batchnormFallback(IImporterContext* ctx, ::ONNX_NAMESPACE::NodeProto const& node, std::vector<TensorOrWeights>& inputs)
{
using eOp = nvinfer1::ElementWiseOperation;
using uOp = nvinfer1::UnaryOperation;
nvinfer1::ITensor& input = convertToTensor(inputs.at(0), ctx);
const int rank = input.getDimensions().nbDims;
nvinfer1::ITensor* scale = &convertToTensor(inputs.at(1), ctx);
nvinfer1::ITensor* bias = &convertToTensor(inputs.at(2), ctx);
nvinfer1::ITensor* mean = &convertToTensor(inputs.at(3), ctx);
nvinfer1::ITensor* variance = &convertToTensor(inputs.at(4), ctx);
const bool hasCDimension = rank > 1;
if (hasCDimension)
{
std::vector<int> axes(rank - 1);
axes[0] = 0;
std::iota(axes.begin() + 1, axes.end(), 2);
scale = unsqueezeTensor(ctx, *scale, axes);
bias = unsqueezeTensor(ctx, *bias, axes);
mean = unsqueezeTensor(ctx, *mean, axes);
variance = unsqueezeTensor(ctx, *variance, axes);
}
OnnxAttrs attrs(node);
float eps = attrs.get<float>("epsilon", 1e-5f);
nvinfer1::Dims scalarShape{rank};
std::fill(scalarShape.d, scalarShape.d + scalarShape.nbDims, 1);
nvinfer1::ITensor* epsilon = addConstantScalar(ctx, eps, ::ONNX_NAMESPACE::TensorProto::FLOAT, scalarShape)->getOutput(0);
// batchnorm = scale * (input - mean) / sqrt(variance + epsilon) + bias
nvinfer1::IElementWiseLayer* layer = ctx->network()->addElementWise(
*ctx->network()->addElementWise(
*scale,
*ctx->network()->addElementWise(
*ctx->network()->addElementWise(input, *mean, eOp::kSUB)->getOutput(0),
*ctx->network()->addUnary(
*ctx->network()->addElementWise(*variance, *epsilon, eOp::kSUM)->getOutput(0),
uOp::kSQRT
)->getOutput(0),
eOp::kDIV
)->getOutput(0),
eOp::kPROD
)->getOutput(0),
*bias,
eOp::kSUM
);
RETURN_FIRST_OUTPUT(layer);
}
DEFINE_BUILTIN_OP_IMPORTER(BatchNormalization)
{
// Scale, bias, mean, and variance must be initializers
auto scale_weights = inputs.at(1).weights();
auto bias_weights = inputs.at(2).weights();
auto mean_weights = inputs.at(3).weights();
auto variance_weights = inputs.at(4).weights();
const bool allInputsWeights = inputs.at(1).is_weights() && inputs.at(2).is_weights() && inputs.at(3).is_weights() && inputs.at(4).is_weights();
const bool allWeightsFloat = scale_weights.type == ::ONNX_NAMESPACE::TensorProto::FLOAT && bias_weights.type == ::ONNX_NAMESPACE::TensorProto::FLOAT && mean_weights.type == ::ONNX_NAMESPACE::TensorProto::FLOAT && variance_weights.type == ::ONNX_NAMESPACE::TensorProto::FLOAT;
const bool canFoldWeights = allInputsWeights && allWeightsFloat;
if (!canFoldWeights)
{
return batchnormFallback(ctx, node, inputs);
}
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
OnnxAttrs attrs(node);
float eps = attrs.get<float>("epsilon", 1e-5f);
nvinfer1::Dims dims = tensor_ptr->getDimensions();
bool need_to_expand_dims = (dims.nbDims == 3);
if (need_to_expand_dims)
{
// Expand spatial dims from 1D to 2D
std::vector<int> axes{3};
tensor_ptr = unsqueezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
dims = tensor_ptr->getDimensions();
}
// Number of channels is equal to the length of scale_weights.
int nchan = scale_weights.shape.d[0];
nvinfer1::Dims weights_shape{1, {nchan}};
ASSERT(scale_weights.shape == weights_shape, ErrorCode::kINVALID_NODE);
ASSERT(bias_weights.shape == weights_shape, ErrorCode::kINVALID_NODE);
ASSERT(mean_weights.shape == weights_shape, ErrorCode::kINVALID_NODE);
ASSERT(variance_weights.shape == weights_shape, ErrorCode::kINVALID_NODE);
auto combined_scale_weights = ctx->createTempWeights(scale_weights.type, scale_weights.shape);
auto combined_bias_weights = ctx->createTempWeights(bias_weights.type, bias_weights.shape);
size_t nweight = nchan;
// Fold the weights together into a single bias and scale
for (size_t i = 0; i < nweight; ++i)
{
float scale = (static_cast<float const*>(scale_weights.values))[i];
float bias = (static_cast<float const*>(bias_weights.values))[i];
float mean = (static_cast<float const*>(mean_weights.values))[i];
float variance = (static_cast<float const*>(variance_weights.values))[i];
float& combined_scale_ref = const_cast<float*>(static_cast<float const*>(combined_scale_weights.values))[i];
float& combined_bias_ref = const_cast<float*>(static_cast<float const*>(combined_bias_weights.values))[i];
combined_scale_ref = scale / sqrtf(variance + eps);
combined_bias_ref = bias - mean * combined_scale_ref;
}
// If dimensions were not expanded return the output of the scale operation
if (!need_to_expand_dims)
{
return addScale(ctx, *tensor_ptr, nvinfer1::ScaleMode::kCHANNEL, combined_bias_weights, combined_scale_weights, {});
}
else
{
auto scaledResult = addScale(ctx, *tensor_ptr, nvinfer1::ScaleMode::kCHANNEL, combined_bias_weights, combined_scale_weights, {});
// Squeeze spatial dims back to 1D
tensor_ptr = &convertToTensor(scaledResult.value().at(0), ctx);
std::vector<int> axes{3};
tensor_ptr = squeezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
return {{tensor_ptr}};
}
}
DEFINE_BUILTIN_OP_IMPORTER(Cast) {
// Get input node.
OnnxAttrs attrs(node);
auto cast_dtype = attrs.get<int32_t>("to");
auto * tensor_ptr = &convertToTensor(inputs.at(0), ctx);
auto trt_dtype = tensor_ptr->getType();
// TensorRT currently only supports the following conversion: FP16 -> FP32.
ASSERT(trt_dtype == nvinfer1::DataType::kHALF && cast_dtype == ::ONNX_NAMESPACE::TensorProto::FLOAT,
ErrorCode::kUNSUPPORTED_NODE);
// Add the layer.
nvinfer1::IIdentityLayer* layer = ctx->network()->addIdentity(inputs.at(0).tensor());
layer->setPrecision(nvinfer1::DataType::kFLOAT);
RETURN_FIRST_OUTPUT(layer);
}
DEFINE_BUILTIN_OP_IMPORTER(Ceil)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kCEIL);
}
DEFINE_BUILTIN_OP_IMPORTER(Clip)
{
OnnxAttrs attrs(node);
// beta is the upper bound.
float alpha = attrs.get("min", std::numeric_limits<float>::lowest());
float beta = attrs.get("max", std::numeric_limits<float>::max());
return activationHelper(ctx, node, inputs, nvinfer1::ActivationType::kCLIP, &alpha, &beta);
}
DEFINE_BUILTIN_OP_IMPORTER(Concat)
{
std::vector<nvinfer1::ITensor*> tensors;
for (auto& input : inputs)
{
tensors.push_back(&convertToTensor(input, ctx));
}
OnnxAttrs attrs(node);
int axis = attrs.get<int>("axis");
int nbDims = inputs.at(0).shape().nbDims;
TRT_CHECK(convert_axis(axis, nbDims));
auto* layer = ctx->network()->addConcatenation(tensors.data(), tensors.size());
ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
layer->setAxis(axis);
RETURN_FIRST_OUTPUT(layer);
}
DEFINE_BUILTIN_OP_IMPORTER(Constant)
{
// TODO: This silently fails if the dtype is not supported
OnnxAttrs attrs(node);
return {{attrs.get<ShapedWeights>("value")}};
}
DEFINE_BUILTIN_OP_IMPORTER(Conv)
{
// Convolution Weights must be an initializer
ASSERT(inputs.at(1).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
auto kernel_weights = inputs.at(1).weights();
nvinfer1::Dims dims = tensor_ptr->getDimensions();
bool need_to_expand_dims = (dims.nbDims == 3);
if (need_to_expand_dims)
{
// Expand spatial dims from 1D to 2D
std::vector<int>axes {3};
tensor_ptr = unsqueezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
dims = tensor_ptr->getDimensions();
}
if (kernel_weights.shape.nbDims == 3)
{
kernel_weights.shape.nbDims = 4;
kernel_weights.shape.d[3] = 1;
}
const int nbSpatialDims = dims.nbDims - 2;
// Check that the number of spatial dimensions and the kernel shape matches up.
ASSERT(nbSpatialDims == kernel_weights.shape.nbDims - 2, ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::Weights bias_weights;
if (inputs.size() == 3)
{
ASSERT(inputs.at(2).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
auto shaped_bias_weights = inputs.at(2).weights();
ASSERT(shaped_bias_weights.shape.nbDims == 1, ErrorCode::kINVALID_NODE);
ASSERT(shaped_bias_weights.shape.d[0] == kernel_weights.shape.d[0], ErrorCode::kINVALID_NODE);
bias_weights = shaped_bias_weights;
}
else
{
bias_weights = ShapedWeights::empty(kernel_weights.type);
}
nvinfer1::Dims kernel_size;
kernel_size.nbDims = nbSpatialDims;
for (int i = 1; i <= nbSpatialDims; ++i)
{
kernel_size.d[nbSpatialDims - i] = kernel_weights.shape.d[kernel_weights.shape.nbDims - i];
}
nvinfer1::Dims strides = makeDims(nbSpatialDims, 1);
nvinfer1::Dims beg_padding = makeDims(nbSpatialDims, 0);
nvinfer1::Dims end_padding = makeDims(nbSpatialDims, 0);
nvinfer1::Dims dilations = makeDims(nbSpatialDims, 1);
nvinfer1::PaddingMode paddingMode;
bool exclude_padding;
get_kernel_params(
node, &kernel_size, &strides, &beg_padding, &end_padding, paddingMode, exclude_padding, &dilations);
for (int i = 1; i <= nbSpatialDims; ++i)
{
ASSERT(kernel_size.d[nbSpatialDims - i] == kernel_weights.shape.d[kernel_weights.shape.nbDims - i],
ErrorCode::kUNSUPPORTED_NODE);
}
int noutput = kernel_weights.shape.d[0];
nvinfer1::IConvolutionLayer* layer
= ctx->network()->addConvolutionNd(*tensor_ptr, noutput, kernel_size, kernel_weights, bias_weights);
ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
layer->setStrideNd(strides);
layer->setPaddingMode(paddingMode);
layer->setPrePadding(beg_padding);
layer->setPostPadding(end_padding);
layer->setDilationNd(dilations);
OnnxAttrs attrs(node);
int ngroup = attrs.get("group", 1);
layer->setNbGroups(ngroup);
tensor_ptr = layer->getOutput(0);
dims = tensor_ptr->getDimensions();
if (need_to_expand_dims)
{
// Un-expand spatial dims back to 1D
std::vector<int>axes {3};
tensor_ptr = squeezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
}
return {{tensor_ptr}};
}
// TRT only supports 2D or 3D deconvolutions (Layout: [N,C,D1,D2,(D3)])
// Inputs should be of dimension 4 or 5.
// When input.nbDims = 3, we expand it to 4D
DEFINE_BUILTIN_OP_IMPORTER(ConvTranspose)
{
// Deconvolution input must be at least 3D.
ASSERT(inputs.at(0).shape().nbDims >= 3, ErrorCode::kUNSUPPORTED_NODE);
// Deconvolution weights must be an initializer
ASSERT(inputs.at(1).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
// Kernel weights have layout [C, M/group, k1, k2, (k3)]
auto kernel_weights = inputs.at(1).weights();
nvinfer1::Dims dims = tensor_ptr->getDimensions();
bool need_to_expand_dims = (dims.nbDims == 3);
if (need_to_expand_dims)
{
std::vector<int>axes {3};
tensor_ptr = unsqueezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
dims = tensor_ptr->getDimensions();
}
if (kernel_weights.shape.nbDims == 3)
{
kernel_weights.shape.nbDims = 4;
kernel_weights.shape.d[3] = 1;
}
const int nbSpatialDims = dims.nbDims - 2;
// Check that the number of spatial dimensions and the kernel shape matches up.
ASSERT(nbSpatialDims == kernel_weights.shape.nbDims - 2, ErrorCode::kUNSUPPORTED_NODE);
// Check for bias_weights
nvinfer1::Weights bias_weights;
if (inputs.size() == 3)
{
ASSERT(inputs.at(2).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
auto shaped_bias_weights = inputs.at(2).weights();
// ONNX requires shaped_bias_weights to be 1D
ASSERT(shaped_bias_weights.shape.nbDims == 1, ErrorCode::kINVALID_NODE);
ASSERT(shaped_bias_weights.shape.d[0] == kernel_weights.shape.d[1], ErrorCode::kINVALID_NODE);
bias_weights = shaped_bias_weights;
}
else
{
bias_weights = ShapedWeights::empty(kernel_weights.type);
}
// Get all attributes
OnnxAttrs attrs(node);
nvinfer1::Dims output_shape;
nvinfer1::Dims output_padding = makeDims(nbSpatialDims, 0);
nvinfer1::Dims kernel_size;
nvinfer1::Dims strides = makeDims(nbSpatialDims, 1);
nvinfer1::Dims beg_padding = makeDims(nbSpatialDims, 0);
nvinfer1::Dims end_padding = makeDims(nbSpatialDims, 0);
nvinfer1::Dims dilations = makeDims(nbSpatialDims, 1);
nvinfer1::PaddingMode paddingMode;
bool exclude_padding = false;
bool explicit_output_shape = false;
int ngroup = attrs.get("group", 1);
int noutput = kernel_weights.shape.d[1] * ngroup; // Note: Weights order is CKRS
if (attrs.count("output_shape"))
{
output_shape = attrs.get<nvinfer1::Dims>("output_shape");
explicit_output_shape = true;
}
kernel_size.nbDims = nbSpatialDims;
for (int i = 1; i <= nbSpatialDims; ++i)
{
kernel_size.d[nbSpatialDims - i] = kernel_weights.shape.d[kernel_weights.shape.nbDims - i];
}
get_kernel_params(node, &kernel_size, &strides, &beg_padding, &end_padding, paddingMode, exclude_padding,
&dilations, &output_padding);
// TRT only support 2D padding
ASSERT(output_padding.nbDims == 2 || (output_padding.nbDims == 3 && output_padding.d[0] == 0),
ErrorCode::kUNSUPPORTED_NODE);
for (int i = 1; i <= nbSpatialDims; ++i)
{
ASSERT(kernel_size.d[nbSpatialDims - i] == kernel_weights.shape.d[kernel_weights.shape.nbDims - i],
ErrorCode::kUNSUPPORTED_NODE);
// TRT does not support dilated deconvolutions
ASSERT(dilations.d[nbSpatialDims - i] == 1, ErrorCode::kUNSUPPORTED_GRAPH);
}
// If output shape is given, calculate the input or output padding values
if (explicit_output_shape)
{
nvinfer1::Dims spatialDims = makeDims(nbSpatialDims, 1);
std::copy(&dims.d[2], &dims.d[2]+nbSpatialDims, &spatialDims.d[0]);
ASSERT(!isDynamic(spatialDims) && "Cannot calculate expected output padding for dynamic shapes!", ErrorCode::kUNSUPPORTED_NODE);
auto_gen_input_output_padding(dims, output_shape, kernel_size, strides, dilations, nbSpatialDims, beg_padding,
end_padding, output_padding, paddingMode);
// TRT only support 2D padding
ASSERT(output_padding.nbDims == 2 || (output_padding.nbDims == 3 && output_padding.d[0] == 0),
ErrorCode::kUNSUPPORTED_NODE);
}
nvinfer1::IDeconvolutionLayer* deconv_layer
= ctx->network()->addDeconvolutionNd(*tensor_ptr, noutput, kernel_size, kernel_weights, bias_weights);
ASSERT(deconv_layer, ErrorCode::kUNSUPPORTED_NODE);
deconv_layer->setStrideNd(strides);
deconv_layer->setPaddingMode(paddingMode);
deconv_layer->setPrePadding(beg_padding);
deconv_layer->setPostPadding(end_padding);
deconv_layer->setNbGroups(ngroup);
tensor_ptr = deconv_layer->getOutput(0);
nvinfer1::DimsHW output_padding_HW;
if (output_padding.nbDims == 2)
{
output_padding_HW = nvinfer1::DimsHW(output_padding.d[0], output_padding.d[1]);
}
else
{
output_padding_HW = nvinfer1::DimsHW(output_padding.d[1], output_padding.d[2]);
}
if (output_padding_HW != nvinfer1::DimsHW(0, 0))
{
tensor_ptr = ctx->network()->addPadding(*tensor_ptr, nvinfer1::DimsHW(), output_padding_HW)->getOutput(0);
}
dims = tensor_ptr->getDimensions();
if (need_to_expand_dims)
{
std::vector<int>axes {3};
tensor_ptr = squeezeTensor(ctx, *tensor_ptr, axes);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
}
return {{tensor_ptr}};
}
DEFINE_BUILTIN_OP_IMPORTER(Cos)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kCOS);
}
DEFINE_BUILTIN_OP_IMPORTER(Cosh)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kCOSH);
}
DEFINE_BUILTIN_OP_IMPORTER(DepthToSpace)
{
// The Tensor has to be of shape NCHW
ASSERT(inputs.at(0).shape().nbDims == 4, ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
nvinfer1::IShuffleLayer* layer = ctx->network()->addShuffle(*tensor_ptr);
ASSERT(layer, ErrorCode::kUNSUPPORTED_NODE);
OnnxAttrs attrs(node);
int block_size = attrs.get<int>("blocksize");
nvinfer1::Dims dims = tensor_ptr->getDimensions();
int ndim_spatial = dims.nbDims - 2;
nvinfer1::Dims new_shape1;
new_shape1.nbDims = dims.nbDims + ndim_spatial;
new_shape1.d[0] = dims.d[0];
new_shape1.d[ndim_spatial + 1] = dims.d[1];
for (int i = 1; i <= ndim_spatial; ++i)
{
ASSERT(new_shape1.d[ndim_spatial + 1] % block_size == 0, ErrorCode::kINVALID_NODE);
new_shape1.d[ndim_spatial + 1] /= block_size;
new_shape1.d[i] = block_size;
new_shape1.d[ndim_spatial + 1 + i] = dims.d[1 + i];
}
layer->setReshapeDimensions(new_shape1);
nvinfer1::Permutation perm;
perm.order[0] = 0;
perm.order[1] = ndim_spatial + 1;
for (int i = 1; i < ndim_spatial + 1; ++i)
{
perm.order[2 * i] = ndim_spatial + 1 + i;
perm.order[2 * i + 1] = i;
}
layer->setSecondTranspose(perm);
tensor_ptr = layer->getOutput(0);
dims = tensor_ptr->getDimensions();
nvinfer1::Dims new_shape2;
new_shape2.nbDims = dims.nbDims - ndim_spatial;
new_shape2.d[0] = dims.d[0];
new_shape2.d[1] = dims.d[1];
for (int i = 1; i <= ndim_spatial; ++i)
{
new_shape2.d[1 + i] = dims.d[2 * i] * dims.d[1 + 2 * i];
}
tensor_ptr = reshape_tensor(ctx, *tensor_ptr, new_shape2);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
return {{tensor_ptr}};
}
DEFINE_BUILTIN_OP_IMPORTER(Div)
{
ASSERT(inputs.size() == 2, ErrorCode::kINVALID_NODE);
return elementwiseHelper(ctx, node, inputs, nvinfer1::ElementWiseOperation::kDIV);
}
DEFINE_BUILTIN_OP_IMPORTER(Dropout)
{
int noutputs = node.output().size();
if (noutputs == 1)
{
RETURN_IDENTITY(inputs.at(0));
}
else
{
// Error if opset version >= 10 as boolean not supported right now
ASSERT(ctx->getOpsetVersion() < 10, ErrorCode::kUNSUPPORTED_NODE);
// Add identity layer twice for both Dropout outputs: (output + mask)
std::vector<TensorOrWeights> outputs;
outputs.push_back(identity(ctx, inputs.at(0)));
outputs.push_back(identity(ctx, inputs.at(0)));
return outputs;
}
}
DEFINE_BUILTIN_OP_IMPORTER(Elu)
{
OnnxAttrs attrs(node);
float alpha = attrs.get<float>("alpha", 1.f);
return activationHelper(ctx, node, inputs, nvinfer1::ActivationType::kELU, &alpha);
}
DEFINE_BUILTIN_OP_IMPORTER(Exp)
{
return apply_unary_function(ctx, inputs.at(0), nvinfer1::UnaryOperation::kEXP);
}
DEFINE_BUILTIN_OP_IMPORTER(Flatten)
{
OnnxAttrs attrs(node);
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
int nbDims = tensor_ptr->getDimensions().nbDims;
int axis = attrs.get("axis", 1);
TRT_CHECK(convert_axis(axis, nbDims));
if (nbDims > 2)
{
tensor_ptr = flattenTensor(ctx, *tensor_ptr, axis);
ASSERT(tensor_ptr, ErrorCode::kUNSUPPORTED_NODE);
}
return {{tensor_ptr}};
}
DEFINE_BUILTIN_OP_IMPORTER(Floor)
{
return unaryHelper(ctx, node, inputs, nvinfer1::UnaryOperation::kFLOOR);
}
DEFINE_BUILTIN_OP_IMPORTER(Gather)
{
nvinfer1::ITensor* data = &convertToTensor(inputs.at(0), ctx);
nvinfer1::ITensor* indices;
bool shuffleWAR{false};
if (inputs.at(1).shape().nbDims == 0 && inputs.at(1).is_weights())
{
int32_t index = static_cast<int32_t*>(inputs.at(1).weights().values)[0];
indices = addConstantScalar<int32_t>(ctx, static_cast<int32_t>(index), ::ONNX_NAMESPACE::TensorProto::INT32)->getOutput(0);
shuffleWAR = true;
}
else
{
indices = &convertToTensor(inputs.at(1),ctx);
}
OnnxAttrs attrs(node);
int axis = attrs.get<int>("axis", 0);
nvinfer1::Dims dataDims = data->getDimensions();
int nbDims = dataDims.nbDims;
// Indicies must have at least one dimension in TRT 6.0.
bool expandIndices = inputs.at(1).shape().nbDims == 0;
// Input tensor to gather on must be at least two dimensions in TRT 6.0
bool expandInput = nbDims == 1;
// Cannot perform gather on shape tensors in TRT 6.0
ASSERT(!(data->getType() == nvinfer1::DataType::kINT32 && nbDims == 1)
&& "Cannot perform gather on a shape tensor!", ErrorCode::kUNSUPPORTED_NODE);
if (expandIndices && !shuffleWAR)
{
std::vector<int> unsqueezeAxis {0};
indices = unsqueezeTensor(ctx, *indices, unsqueezeAxis);
}
if (expandInput)
{
std::vector<int> unsqueezeAxis {0};
data = unsqueezeTensor(ctx, *data, unsqueezeAxis);
// update nbDims and axis since we expanded the input to 2D.
nbDims = data->getDimensions().nbDims;
axis = axis + 1;
}
TRT_CHECK(convert_axis(axis, nbDims));
auto* layer = ctx->network()->addGather(*data, *indices, axis);
auto* layerOutput = layer->getOutput(0);
// Extra dimension would have been inserted on the axis we are gathering on. Remove it here.
if (expandIndices)
{
std::vector<int> squeezeAxis{axis};
layerOutput = squeezeTensor(ctx, *layerOutput, squeezeAxis);
}
// Remove leading dimension if it was added to make output a scalar or squeeze back to 1D.
if (expandInput)
{
const auto currentDims = layerOutput->getDimensions().nbDims;
ASSERT(currentDims == 1 || currentDims == 2, ErrorCode::kUNSUPPORTED_NODE);
std::vector<int> squeezeAxis{0};
layerOutput = squeezeTensor(ctx, *layerOutput, squeezeAxis);
}
return {{layerOutput}};
}
DEFINE_BUILTIN_OP_IMPORTER(Gemm)
{
OnnxAttrs attrs(node);
float alpha = attrs.get("alpha", 1.f);
float beta = attrs.get("beta", 1.f);
bool transA = attrs.get("transA", false);
bool transB = attrs.get("transB", false);
nvinfer1::ITensor& inputA = convertToTensor(inputs.at(0), ctx);
nvinfer1::ITensor* inputB = &convertToTensor(inputs.at(1), ctx);
nvinfer1::ITensor& inputC = convertToTensor(inputs.at(2), ctx);
// Use FC if it is likely to be faster - which is usually when no Shuffles are required.
bool canUseFC = inputs.at(0).is_tensor() && inputs.at(1).is_weights() && inputs.at(2).is_weights() && alpha == 1.f
&& beta == 1.f && inputs.at(0).tensor().getDimensions().nbDims == 3 && inputs.at(1).weights().shape.nbDims == 2
&& inputs.at(2).weights().shape.nbDims == 1;
if (canUseFC)
{
nvinfer1::ITensor& tensor = inputs.at(0).tensor();
ShapedWeights weights = inputs.at(1).weights();
if (!transB)
{
auto transposedWeights = ctx->createTempWeights(weights.type, weights.shape);
ASSERT(transposeWeights(weights, {1, 0}, &transposedWeights), ErrorCode::kUNSUPPORTED_NODE);
weights = transposedWeights;
}
ShapedWeights biases = inputs.at(2).weights();
RETURN_FIRST_OUTPUT(ctx->network()->addFullyConnected(tensor, biases.shape.d[0], weights, biases));
}
// If input B is a constant, we transpose at parse time if necessary,
// because In some cases, A * Bt is much slower than A * B.
if (inputs.at(1).is_weights())
{
ShapedWeights weights = inputs.at(1).weights();
if (transB)
{
auto transposedWeights = ctx->createTempWeights(weights.type, weights.shape);
ASSERT(transposeWeights(weights, {1, 0}, &transposedWeights), ErrorCode::kUNSUPPORTED_NODE);
weights = transposedWeights;
// Since we've already transposed now, we can set transpose to false.
transB = false;
}
nvinfer1::IConstantLayer* weightsLayer
= ctx->network()->addConstant(weights.shape, static_cast<nvinfer1::Weights>(weights));
inputB = weightsLayer->getOutput(0);
}
else
{
inputB = &inputs.at(1).tensor();
}
nvinfer1::ITensor* inputASqueezed = &inputA;
nvinfer1::Dims newDims = squeeze_trailing_dims(inputA.getDimensions());
// When A has more than 2 dimensions, it needs to be flattened.
if (newDims.nbDims > 2)
{
newDims = nvinfer1::Dims{1, {-1}};
}
// Due to other TRT layers, inputA may sometimes have trailing 1s that need to be removed.
if (newDims.nbDims < inputA.getDimensions().nbDims)
{
nvinfer1::IShuffleLayer* squeeze = ctx->network()->addShuffle(inputA);
squeeze->setReshapeDimensions(newDims);
inputASqueezed = squeeze->getOutput(0);
}
constexpr auto getMatrixOp = [](const nvinfer1::ITensor& input, bool transpose) {
if (input.getDimensions().nbDims == 1)
{
return nvinfer1::MatrixOperation::kVECTOR;
}
else if (transpose)
{
return nvinfer1::MatrixOperation::kTRANSPOSE;
}
return nvinfer1::MatrixOperation::kNONE;
};
nvinfer1::MatrixOperation opA = getMatrixOp(*inputASqueezed, transA);
nvinfer1::MatrixOperation opB = getMatrixOp(*inputB, transB);
nvinfer1::IMatrixMultiplyLayer* matmul = ctx->network()->addMatrixMultiply(*inputASqueezed, opA, *inputB, opB);
nvinfer1::ITensor* matmulTensor = matmul->getOutput(0);
// Scale A*B if needed.
if (alpha != 1.f)
{
nvinfer1::IConstantLayer* alphaConstant
= addConstantScalar(ctx, alpha, ::ONNX_NAMESPACE::TensorProto_DataType_FLOAT);
nvinfer1::ITensor* alphaConstantTensor = alphaConstant->getOutput(0);
broadcastTensors(ctx, alphaConstantTensor, matmulTensor);
nvinfer1::IElementWiseLayer* scaledMatmul = ctx->network()->addElementWise(
*alphaConstantTensor, *matmulTensor, nvinfer1::ElementWiseOperation::kPROD);
matmulTensor = scaledMatmul->getOutput(0);
}
// Scale C if needed.
nvinfer1::ITensor* biasTensor = &inputC;
if (beta != 1.f)
{
nvinfer1::IConstantLayer* betaConstant
= addConstantScalar(ctx, beta, ::ONNX_NAMESPACE::TensorProto_DataType_FLOAT);
nvinfer1::ITensor* betaConstantTensor = betaConstant->getOutput(0);
broadcastTensors(ctx, betaConstantTensor, biasTensor);
nvinfer1::IElementWiseLayer* scaledBias
= ctx->network()->addElementWise(*betaConstantTensor, *biasTensor, nvinfer1::ElementWiseOperation::kPROD);
biasTensor = scaledBias->getOutput(0);
}
// A*B may be lower rank than C in TRT, so need to squeeze C.
if (ctx->getOpsetVersion() < 7 && !attrs.get("broadcast", false))
{
nvinfer1::Dims squeezeDims = squeeze_leading_dims(biasTensor->getDimensions());
biasTensor = reshape_tensor(ctx, *biasTensor, squeezeDims);
}
broadcastTensors(ctx, matmulTensor, biasTensor);
nvinfer1::IElementWiseLayer* biasAdd
= ctx->network()->addElementWise(*matmulTensor, *biasTensor, nvinfer1::ElementWiseOperation::kSUM);
return {{biasAdd->getOutput(0)}};
}
DEFINE_BUILTIN_OP_IMPORTER(GlobalAveragePool)
{
nvinfer1::ITensor& tensor = convertToTensor(inputs.at(0), ctx);
nvinfer1::Dims dims = tensor.getDimensions();
int nbSpatialDims = dims.nbDims - 2;
ASSERT((nbSpatialDims == 2 || nbSpatialDims == 3) && "TensorRT only supports 2D and 3D pooling!", ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::Dims kernelSize = nbSpatialDims == 2 ?
static_cast<nvinfer1::Dims>(nvinfer1::Dims2{dims.d[2], dims.d[3]}) :
static_cast<nvinfer1::Dims>(nvinfer1::Dims3{dims.d[2], dims.d[3], dims.d[4]});
ASSERT(!isDynamic(kernelSize) && "Cannot run GlobalAveragePool on an input with dynamic spatial dimensions!", ErrorCode::kUNSUPPORTED_NODE);
RETURN_FIRST_OUTPUT(ctx->network()->addPoolingNd(tensor, nvinfer1::PoolingType::kAVERAGE, kernelSize));
}
// TODO: GlobalLpPool: pow(reduce_mean(pow(abs(x), p)), 1./p)
DEFINE_BUILTIN_OP_IMPORTER(GlobalMaxPool)
{
nvinfer1::ITensor& tensor = convertToTensor(inputs.at(0), ctx);
nvinfer1::Dims dims = tensor.getDimensions();
int nbSpatialDims = dims.nbDims - 2;
ASSERT((nbSpatialDims == 2 || nbSpatialDims == 3) && "TensorRT only supports 2D and 3D pooling!", ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::Dims kernelSize = nbSpatialDims == 2 ?
static_cast<nvinfer1::Dims>(nvinfer1::Dims2{dims.d[2], dims.d[3]}) :
static_cast<nvinfer1::Dims>(nvinfer1::Dims3{dims.d[2], dims.d[3], dims.d[4]});
ASSERT(!isDynamic(kernelSize) && "Cannot run GlobalMaxPool on an input with dynamic spatial dimensions!", ErrorCode::kUNSUPPORTED_NODE);
RETURN_FIRST_OUTPUT(ctx->network()->addPoolingNd(tensor, nvinfer1::PoolingType::kAVERAGE, kernelSize));
}
DEFINE_BUILTIN_OP_IMPORTER(HardSigmoid)
{
OnnxAttrs attrs(node);
float alpha = attrs.get<float>("alpha", 0.2f);
float beta = attrs.get<float>("beta", 0.5f);
return activationHelper(ctx, node, inputs, nvinfer1::ActivationType::kHARD_SIGMOID, &alpha, &beta);
}
DEFINE_BUILTIN_OP_IMPORTER(Identity)
{
RETURN_IDENTITY(inputs.at(0));
}
DEFINE_BUILTIN_OP_IMPORTER(ImageScaler)
{
nvinfer1::ITensor& tensor = convertToTensor(inputs.at(0), ctx);
OnnxAttrs attrs{node};
// Shift the input by a per-channel bias value.
std::vector<float> biases = attrs.get<std::vector<float>>("bias");
nvinfer1::Dims dims{1, static_cast<int>(biases.size())};
ShapedWeights shiftWeights = ctx->createTempWeights(::ONNX_NAMESPACE::TensorProto_DataType_FLOAT, dims);
std::copy(biases.begin(), biases.end(), static_cast<float*>(shiftWeights.values));
// Scale is applied to every element of the input, but we need to duplicate it over every channel.
float scale = attrs.get<float>("scale", 1.0f);
ShapedWeights scaleWeights = ctx->createTempWeights(::ONNX_NAMESPACE::TensorProto_DataType_FLOAT, dims);
std::fill(static_cast<float*>(scaleWeights.values), static_cast<float*>(scaleWeights.values) + scaleWeights.count(),
scale);
// Finally add the scale layer.
RETURN_FIRST_OUTPUT(ctx->network()->addScale(
tensor, nvinfer1::ScaleMode::kCHANNEL, shiftWeights, scaleWeights, nvinfer1::Weights{}));
}
DEFINE_BUILTIN_OP_IMPORTER(InstanceNormalization)
{
// Scales and biases must be initializers
ASSERT(inputs.at(1).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
ASSERT(inputs.at(2).is_weights(), ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::ITensor* tensor_ptr = &convertToTensor(inputs.at(0), ctx);
ASSERT(!isDynamic(tensor_ptr->getDimensions()) && "InstanceNormalization does not support dynamic inputs!", ErrorCode::kUNSUPPORTED_NODE);
auto scale_weights = inputs.at(1).weights();
auto bias_weights = inputs.at(2).weights();
OnnxAttrs attrs(node);
float epsilon = attrs.get("epsilon", 1e-5f);
// TensorRT only supports epsilon values >= 1e-4.
epsilon = std::max(epsilon, 1e-4f);
// Populate instanceNormalization plugin properties.
const std::string pluginName = "InstanceNormalization_TRT";
const std::string pluginVersion = "001";
std::vector<nvinfer1::PluginField> f;
f.emplace_back("epsilon", &epsilon, nvinfer1::PluginFieldType::kFLOAT32, 1);
f.emplace_back("scales", scale_weights.values, nvinfer1::PluginFieldType::kFLOAT32, scale_weights.count());
f.emplace_back("bias", bias_weights.values, nvinfer1::PluginFieldType::kFLOAT32, bias_weights.count());
// Create plugin from registry
nvinfer1::IPluginV2* plugin = importPluginFromRegistry(ctx, pluginName, pluginVersion, node.name(), f);
ASSERT(plugin != nullptr && "InstanceNormalization plugin was not found in the plugin registry!", ErrorCode::kINTERNAL_ERROR);
RETURN_FIRST_OUTPUT(ctx->network()->addPluginV2(&tensor_ptr, 1, *plugin));
}
DEFINE_BUILTIN_OP_IMPORTER(LeakyRelu)
{
OnnxAttrs attrs(node);
float alpha = attrs.get<float>("alpha", 0.01f);
return activationHelper(ctx, node, inputs, nvinfer1::ActivationType::kLEAKY_RELU, &alpha);
}
DEFINE_BUILTIN_OP_IMPORTER(Log)
{
return apply_unary_function(ctx, inputs.at(0), nvinfer1::UnaryOperation::kLOG);
}
DECLARE_BUILTIN_OP_IMPORTER(Softmax);
DEFINE_BUILTIN_OP_IMPORTER(LogSoftmax)
{
auto result = importSoftmax(ctx, node, inputs);
if (result.is_error())
{
return result;
}
else
{
auto& input = result.value().at(0);
return apply_unary_function(ctx, input, nvinfer1::UnaryOperation::kLOG);
}
}
DEFINE_BUILTIN_OP_IMPORTER(LRN)
{
nvinfer1::ITensor& tensor = convertToTensor(inputs.at(0), ctx);
OnnxAttrs attrs(node);
int size = attrs.get<int>("size");
float alpha = attrs.get<float>("alpha", 0.0001f);
float beta = attrs.get<float>("beta", 0.75f);
float bias = attrs.get<float>("bias", 1.0f);
RETURN_FIRST_OUTPUT(ctx->network()->addLRN(tensor, size, alpha, beta, bias));
}
DEFINE_BUILTIN_OP_IMPORTER(LSTM) {
// Input
nvinfer1::ITensor& raw_input = convertToTensor(inputs.at(0), ctx);
ASSERT(3 == raw_input.getDimensions().nbDims && "Input tensor must be 3 dimensional",
ErrorCode::kINVALID_NODE);
ASSERT((raw_input.getType() == nvinfer1::DataType::kFLOAT ||
raw_input.getType() == nvinfer1::DataType::kHALF) &&