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handling I32/I64 data types in G-API ONNX back-end #25817

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Merged
merged 16 commits into from
Jul 12, 2024
Merged

handling I32/I64 data types in G-API ONNX back-end #25817

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d0f85e5
handling CV_32S
lamiayous Jun 26, 2024
762ac0f
Extending assert for type I32
lamiayous Jun 27, 2024
f8ffdae
extending to handle I64
lamiayous Jun 27, 2024
dd242b3
Changing GAPI ASSERT message
lamiayous Jun 27, 2024
341874a
Revert "Changing GAPI ASSERT message"
lamiayous Jun 28, 2024
7c888ce
Changing/Adding comments
lamiayous Jul 1, 2024
2271103
converting input from int32 to int64
lamiayous Jul 2, 2024
4227801
i64 handling
lamiayous Jul 5, 2024
e3c5212
adding copyFromONNX function INT64
lamiayous Jul 10, 2024
0ab22dd
fix ONNXCompiled::run, copyFromONNX section
lamiayous Jul 10, 2024
101654c
fixing format
lamiayous Jul 10, 2024
df83303
fix format II
lamiayous Jul 10, 2024
231f927
fixing trailing whitepaces
lamiayous Jul 11, 2024
562aade
Removing temp vector and removing extra if statement
lamiayous Jul 11, 2024
e53eaf1
Adding comments
lamiayous Jul 11, 2024
afe10d7
Remove trailing whitespace
lamiayous Jul 11, 2024
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48 changes: 39 additions & 9 deletions modules/gapi/src/backends/onnx/gonnxbackend.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,7 @@ class ONNXCompiled {
std::vector<cv::Mat> out_data;

void Run(const std::vector<cv::Mat>& ins,
const std::vector<cv::Mat>& outs);
std::vector<cv::Mat>& outs);

std::vector<std::string> in_names_without_const;
public:
Expand Down Expand Up @@ -322,22 +322,20 @@ inline std::vector<int64_t> toORT(const cv::MatSize &sz) {
inline void preprocess(const cv::Mat& src,
const cv::gimpl::onnx::TensorInfo& ti,
cv::Mat& dst) {
GAPI_Assert(src.depth() == CV_32F || src.depth() == CV_8U);
// CNN input type
const auto type = toCV(ti.type);
if (src.depth() == CV_32F) {
if (src.depth() != CV_8U) {
// Just pass the tensor as-is.
// No layout or dimension transformations done here!
// TODO: This needs to be aligned across all NN backends.
GAPI_Assert(type == CV_32F && "Only 32F model input is supported for 32F input data");
const auto tensor_dims = toORT(src.size);
if (tensor_dims.size() == ti.dims.size()) {
for (size_t i = 0; i < ti.dims.size(); ++i) {
GAPI_Assert((ti.dims[i] == -1 || ti.dims[i] == tensor_dims[i]) &&
"32F tensor dimensions should match with all non-dynamic NN input dimensions");
"Non-U8 tensor dimensions should match with all non-dynamic NN input dimensions");
}
} else {
GAPI_Error("32F tensor size should match with NN input");
GAPI_Error("Non-U8 tensor size should match with NN input");
}

dst = src;
Expand Down Expand Up @@ -471,6 +469,25 @@ inline Ort::Value createTensor(const Ort::MemoryInfo& memory_info,
return createTensor<float>(memory_info, tensor_params, data);
case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
return createTensor<int32_t>(memory_info, tensor_params, data);
case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64:{
// cv::Mat does not support int64 data directly.
// Following steps are applied to create an ONNX tensor from cv::Mat data:
// - First create a new ONNX tensor 'i64_tensor' with data type int64_t using the default allocator
// - Next retrieve a pointer to the mutable data buffer of 'i64_tensor'
// - Convert the data from int32 (see toCV function) to int64 and deep copy it into 'i64_tensor'
auto ort_dims = toORT(data.size);

Ort::AllocatorWithDefaultOptions allocator;
Ort::Value i64_tensor = Ort::Value::CreateTensor<int64_t>(allocator,
ort_dims.data(),
ort_dims.size());
int64_t* tensor_data = i64_tensor.GetTensorMutableData<int64_t>();

cv::gimpl::convertInt32ToInt64(data.ptr<int>(),
tensor_data,
data.total());
return i64_tensor;
}
default:
GAPI_Error("ONNX. Unsupported data type");
}
Expand Down Expand Up @@ -747,9 +764,11 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
in_tensor_info.end(),
[](const cv::gimpl::onnx::TensorInfo &p) {
return p.type == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT
|| p.type == ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8;
|| p.type == ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8
|| p.type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32
|| p.type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64;
})
&& "Only FP32 and U8 inputs for NN are supported");
&& "Only FP32, INT32, INT64 and U8 inputs for NN are supported");

// Put mean and std in appropriate tensor params
if (!params.mean.empty() || !params.stdev.empty()) {
Expand Down Expand Up @@ -864,7 +883,7 @@ cv::Mat ONNXCompiled::allocOutput(int i) const {
}

void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
const std::vector<cv::Mat>& outs) {
std::vector<cv::Mat>& outs) {
std::vector<Ort::Value> in_tensors, out_tensors;

// Layer names order for run
Expand Down Expand Up @@ -909,6 +928,17 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
out_run_names.data(),
&out_tensors.front(),
params.output_names.size());
if (out_tensor_info[0].type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64) {
// cv::Mat does not support int64 output data.
// Conversion from int 64 to int 32 is carried in the copyFromONNX function
// The output is written to out_mat
for (auto &&iter : ade::util::zip(ade::util::toRange(out_tensors),
ade::util::toRange(outs))) {
auto &out_tensor = std::get<0>(iter);
auto &out_mat = std::get<1>(iter);
copyFromONNX(out_tensor, out_mat);
}
}
} else {
// Hard path - run session & user-defined post-processing
// NOTE: use another list of output names here
Expand Down