mirror of
https://github.com/saymrwulf/onnxruntime.git
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79436a2d5b
Updated several DML EP files to avoid warning C5038: data member 'member1' will be initialized after data member 'member2' / base class 'base_class' More information: https://docs.microsoft.com/en-us/cpp/error-messages/compiler-warnings/c5038?view=msvc-160
95 lines
No EOL
2.2 KiB
C++
95 lines
No EOL
2.2 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 "NumericData.h"
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#include "StringData.h"
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//
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// the Tensor class is the actual object for CPU memory buffers.
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// TensorBase contains one of these to represent the raw memory
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// GetCpuResource() returns it
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//
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namespace _winml {
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inline size_t compute_size_of_shape(const std::vector<int64_t>& shape) {
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auto size_of_shape =
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static_cast<size_t>(
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std::accumulate(
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std::begin(shape),
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std::end(shape),
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static_cast<int64_t>(1),
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std::multiplies<int64_t>()));
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return size_of_shape;
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}
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template <typename T>
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inline auto create_data(
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const std::vector<int64_t>& shape,
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const wfc::IIterable<wss::IBuffer>& buffers) {
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return _winml::numeric_data::create(compute_size_of_shape(shape), sizeof(T), buffers);
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}
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template <>
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inline auto create_data<std::string>(
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const std::vector<int64_t>& shape,
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const wfc::IIterable<wss::IBuffer>& /*buffers*/) {
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return _winml::string_data::create(compute_size_of_shape(shape));
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}
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template <typename T>
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class Tensor {
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private:
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std::shared_ptr<_winml::idata> data_;
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std::vector<int64_t> shape_;
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private:
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Tensor() = delete;
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public:
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Tensor(const std::vector<int64_t>& shape) :
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data_(create_data<T>(shape, nullptr)),
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shape_(shape) {}
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Tensor(
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const std::vector<int64_t>& shape,
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const wfc::IIterable<wss::IBuffer>& buffers) :
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data_(create_data<T>(shape, buffers)),
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shape_(shape) {}
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auto size_in_bytes() const {
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return data_->size_in_bytes();
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}
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auto num_buffers() {
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return data_->num_buffers();
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}
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auto& buffers() {
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return data_->buffers();
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}
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gsl::span<T> buffer(bool should_sync_buffer = true) {
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auto span = data_->buffer(should_sync_buffer);
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return gsl::span<T>(reinterpret_cast<T*>(span.data()), data_->num_elements());
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}
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auto flush() {
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return data_->flush();
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}
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void set(size_t size, const T* data) {
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auto size_in_bytes = size * sizeof(T);
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data_->set(size_in_bytes, reinterpret_cast<const byte*>(data));
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}
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const std::vector<int64_t>& shape() const {
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return shape_;
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}
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auto get_data() {
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return data_;
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}
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};
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} // namespace _winml
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