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Removed ov::runtime namespace (#9781)

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* Removed ov::runtime namespace

* clang-format

* Fixes

* template reference

* Fixes
This commit is contained in:
Ilya Lavrenov
2022-01-20 16:17:57 +03:00
committed by GitHub
parent 29c3b8e201
commit 8f3d030d79
362 changed files with 4231 additions and 4262 deletions
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92
samples/cpp/benchmark_app/inputs_filling.cpp
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@@ -24,12 +24,12 @@ using uniformDistribution = typename std::conditional<
typename std::conditional<std::is_integral<T>::value, std::uniform_int_distribution<T>, void>::type>::type;
template <typename T>
ov::runtime::Tensor create_tensor_from_image(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName,
std::string* filenames_used = nullptr) {
ov::Tensor create_tensor_from_image(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName,
std::string* filenames_used = nullptr) {
size_t tensor_size =
std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
@@ -92,15 +92,15 @@ ov::runtime::Tensor create_tensor_from_image(const std::vector<std::string>& fil
}
}
auto tensor = ov::runtime::Tensor(inputInfo.type, inputInfo.dataShape, ov::runtime::Allocator(allocator));
auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
return tensor;
}
template <typename T>
ov::runtime::Tensor create_tensor_im_info(const std::pair<size_t, size_t>& image_size,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName) {
ov::Tensor create_tensor_im_info(const std::pair<size_t, size_t>& image_size,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName) {
size_t tensor_size =
std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
@@ -127,17 +127,17 @@ ov::runtime::Tensor create_tensor_im_info(const std::pair<size_t, size_t>& image
}
}
auto tensor = ov::runtime::Tensor(inputInfo.type, inputInfo.dataShape, ov::runtime::Allocator(allocator));
auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
return tensor;
}
template <typename T>
ov::runtime::Tensor create_tensor_from_binary(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName,
std::string* filenames_used = nullptr) {
ov::Tensor create_tensor_from_binary(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const benchmark_app::InputInfo& inputInfo,
const std::string& inputName,
std::string* filenames_used = nullptr) {
size_t tensor_size =
std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
@@ -185,14 +185,14 @@ ov::runtime::Tensor create_tensor_from_binary(const std::vector<std::string>& fi
}
}
auto tensor = ov::runtime::Tensor(inputInfo.type, inputInfo.dataShape, ov::runtime::Allocator(allocator));
auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
return tensor;
}
template <typename T, typename T2>
ov::runtime::Tensor create_tensor_random(const benchmark_app::InputInfo& inputInfo,
T rand_min = std::numeric_limits<uint8_t>::min(),
T rand_max = std::numeric_limits<uint8_t>::max()) {
ov::Tensor create_tensor_random(const benchmark_app::InputInfo& inputInfo,
T rand_min = std::numeric_limits<uint8_t>::min(),
T rand_max = std::numeric_limits<uint8_t>::max()) {
size_t tensor_size =
std::accumulate(inputInfo.dataShape.begin(), inputInfo.dataShape.end(), 1, std::multiplies<size_t>());
auto allocator = std::make_shared<SharedTensorAllocator>(tensor_size * sizeof(T));
@@ -204,15 +204,15 @@ ov::runtime::Tensor create_tensor_random(const benchmark_app::InputInfo& inputIn
data[i] = static_cast<T>(distribution(gen));
}
auto tensor = ov::runtime::Tensor(inputInfo.type, inputInfo.dataShape, ov::runtime::Allocator(allocator));
auto tensor = ov::Tensor(inputInfo.type, inputInfo.dataShape, ov::Allocator(allocator));
return tensor;
}
ov::runtime::Tensor get_image_tensor(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
std::string* filenames_used = nullptr) {
ov::Tensor get_image_tensor(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
std::string* filenames_used = nullptr) {
auto type = inputInfo.second.type;
if (type == ov::element::f32) {
return create_tensor_from_image<float>(files,
@@ -254,9 +254,9 @@ ov::runtime::Tensor get_image_tensor(const std::vector<std::string>& files,
}
}
ov::runtime::Tensor get_im_info_tensor(const std::pair<size_t, size_t>& image_size,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
ov::Tensor get_im_info_tensor(const std::pair<size_t, size_t>& image_size,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
auto type = inputInfo.second.type;
if (type == ov::element::f32) {
return create_tensor_im_info<float>(image_size, batchSize, inputInfo.second, inputInfo.first);
@@ -271,11 +271,11 @@ ov::runtime::Tensor get_im_info_tensor(const std::pair<size_t, size_t>& image_si
}
}
ov::runtime::Tensor get_binary_tensor(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
std::string* filenames_used = nullptr) {
ov::Tensor get_binary_tensor(const std::vector<std::string>& files,
size_t inputId,
size_t batchSize,
const std::pair<std::string, benchmark_app::InputInfo>& inputInfo,
std::string* filenames_used = nullptr) {
const auto& type = inputInfo.second.type;
if (type == ov::element::f32) {
return create_tensor_from_binary<float>(files,
@@ -317,7 +317,7 @@ ov::runtime::Tensor get_binary_tensor(const std::vector<std::string>& files,
}
}
ov::runtime::Tensor get_random_tensor(const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
ov::Tensor get_random_tensor(const std::pair<std::string, benchmark_app::InputInfo>& inputInfo) {
auto type = inputInfo.second.type;
if (type == ov::element::f32) {
return create_tensor_random<float, float>(inputInfo.second);
@@ -358,9 +358,9 @@ std::string get_test_info_stream_header(benchmark_app::InputInfo& inputInfo) {
return strOut.str();
}
std::map<std::string, ov::runtime::TensorVector> get_tensors(std::map<std::string, std::vector<std::string>> inputFiles,
std::vector<benchmark_app::InputsInfo>& app_inputs_info) {
std::map<std::string, ov::runtime::TensorVector> tensors;
std::map<std::string, ov::TensorVector> get_tensors(std::map<std::string, std::vector<std::string>> inputFiles,
std::vector<benchmark_app::InputsInfo>& app_inputs_info) {
std::map<std::string, ov::TensorVector> tensors;
if (app_inputs_info.empty()) {
throw std::logic_error("Inputs Info for network is empty!");
}
@@ -519,11 +519,11 @@ std::map<std::string, ov::runtime::TensorVector> get_tensors(std::map<std::strin
return tensors;
}
std::map<std::string, ov::runtime::TensorVector> get_tensors_static_case(const std::vector<std::string>& inputFiles,
const size_t& batchSize,
benchmark_app::InputsInfo& app_inputs_info,
size_t requestsNum) {
std::map<std::string, ov::runtime::TensorVector> blobs;
std::map<std::string, ov::TensorVector> get_tensors_static_case(const std::vector<std::string>& inputFiles,
const size_t& batchSize,
benchmark_app::InputsInfo& app_inputs_info,
size_t requestsNum) {
std::map<std::string, ov::TensorVector> blobs;
std::vector<std::pair<size_t, size_t>> net_input_im_sizes;
for (auto& item : app_inputs_info) {
@@ -691,7 +691,7 @@ std::map<std::string, ov::runtime::TensorVector> get_tensors_static_case(const s
return blobs;
}
void copy_tensor_data(ov::runtime::Tensor& dst, const ov::runtime::Tensor& src) {
void copy_tensor_data(ov::Tensor& dst, const ov::Tensor& src) {
if (src.get_shape() != dst.get_shape() || src.get_byte_size() != dst.get_byte_size()) {
throw std::runtime_error(
"Source and destination tensors shapes and byte sizes are expected to be equal for data copying.");