mirror of
https://github.com/saymrwulf/onnxruntime.git
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4e1dac67cd
Fix memory leak when a Python list passed as a feed. Create a custom allocator that can take ownership of python arrays that are created inside pybind. Allow direct memory use if continuous array is a copy because we now can take ownership of it by the allocator.
40 lines
1.1 KiB
C++
40 lines
1.1 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#include <pybind11/iostream.h>
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include "core/common/logging/logging.h"
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#include "core/common/logging/sinks/clog_sink.h"
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#include "core/common/logging/sinks/cerr_sink.h"
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#include "core/framework/allocatormgr.h"
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#include "core/session/environment.h"
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#include "core/framework/ml_value.h"
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#include "core/session/inference_session.h"
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namespace onnxruntime {
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namespace python {
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namespace py = pybind11;
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int OnnxRuntimeTensorToNumpyType(const DataTypeImpl* tensor_type);
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MLDataType NumpyTypeToOnnxRuntimeType(int numpy_type);
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void CreateGenericMLValue(const onnxruntime::InputDefList* input_def_list, const AllocatorPtr& alloc, const std::string& name_input,
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py::object& value, OrtValue* p_mlvalue);
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template <class T>
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struct DecRefFn {
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void operator()(T* pyobject) const {
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Py_XDECREF(pyobject);
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}
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};
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template <class T>
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using UniqueDecRefPtr = std::unique_ptr<T, DecRefFn<T>>;
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} // namespace python
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} // namespace onnxruntime
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