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TensorOrWeights.hpp 3.23 KB
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Kevin Chen 提交于 2021-07-02 10:55 . ONNX-TensorRT 8.0 GA release (#706)
/*
* SPDX-License-Identifier: Apache-2.0
*/
#pragma once
#include "ShapedWeights.hpp"
#include <NvInfer.h>
#include <cassert>
namespace onnx2trt
{
class TensorOrWeights
{
union
{
nvinfer1::ITensor* _tensor;
ShapedWeights _weights;
};
enum
{
NODE_TENSOR,
NODE_WEIGHTS
} _variant;
public:
TensorOrWeights()
: _tensor(nullptr)
, _variant(NODE_TENSOR)
{
}
TensorOrWeights(nvinfer1::ITensor* tensor)
: _tensor(tensor)
, _variant(NODE_TENSOR)
{
}
TensorOrWeights(ShapedWeights const& weights)
: _weights(weights)
, _variant(NODE_WEIGHTS)
{
}
bool is_tensor() const
{
return _variant == NODE_TENSOR;
}
bool is_weights() const
{
return _variant == NODE_WEIGHTS;
}
bool isNullTensor() const
{
return is_tensor() && _tensor == nullptr;
}
nvinfer1::ITensor& tensor()
{
assert(!isNullTensor());
return *_tensor;
}
nvinfer1::ITensor const& tensor() const
{
assert(!isNullTensor());
return *_tensor;
}
ShapedWeights& weights()
{
assert(is_weights());
return _weights;
}
ShapedWeights const& weights() const
{
assert(is_weights());
return _weights;
}
nvinfer1::Dims shape() const
{
return is_tensor() ? _tensor->getDimensions() : _weights.shape;
}
explicit operator bool() const
{
return is_tensor() ? _tensor != nullptr : static_cast<bool>(_weights);
}
bool isInt32() const
{
return is_tensor() ? _tensor->getType() == nvinfer1::DataType::kINT32 : _weights.type == ::ONNX_NAMESPACE::TensorProto_DataType_INT32;
}
bool isBool() const
{
return is_tensor() ? _tensor->getType() == nvinfer1::DataType::kBOOL : _weights.type == ::ONNX_NAMESPACE::TensorProto_DataType_BOOL;
}
std::string getName() const
{
return is_tensor() ? _tensor->getName() : _weights.getName();
}
std::string getType() const
{
if (is_tensor())
{
switch(_tensor->getType())
{
case nvinfer1::DataType::kFLOAT:return "FLOAT";
case nvinfer1::DataType::kHALF: return "HALF";
case nvinfer1::DataType::kINT8: return "INT8";
case nvinfer1::DataType::kINT32: return "INT32";
case nvinfer1::DataType::kBOOL: return "BOOL";
default: return "UNKNOWN TYPE";
}
}
else
{
switch(_weights.type)
{
case ::ONNX_NAMESPACE::TensorProto::DOUBLE: return "DOUBLE -> FLOAT";
case ::ONNX_NAMESPACE::TensorProto::FLOAT: return "FLOAT";
case ::ONNX_NAMESPACE::TensorProto::INT8: return "INT8";
case ::ONNX_NAMESPACE::TensorProto::FLOAT16: return "HALF";
case ::ONNX_NAMESPACE::TensorProto::BOOL: return "BOOL";
case ::ONNX_NAMESPACE::TensorProto::INT32: return "INT32";
case ::ONNX_NAMESPACE::TensorProto::INT64: return "INT64 -> INT32";
default: return "UNKNOWN TYPE";
}
}
}
};
} // namespace onnx2trt
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