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Merge pull request #18902 from mpashchenkov:mp/onnx-const-input

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G-API: ONNX. Const input

* Added const input for ONNX backend

* Returned initMatrixRandu, added some comments, rebase
This commit is contained in:
Maxim Pashchenkov
2021-01-13 00:31:15 +03:00
committed by GitHub
parent d3bc563c6e
commit 3eaeca58da
3 changed files with 315 additions and 92 deletions
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modules/gapi/src/backends/ie/giebackend.cpp
-8
View File
@@ -263,14 +263,6 @@ struct IEUnit {
// Still, constant data is to set only once.
this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
}
// Bind const data to infer request
for (auto &&p : params.const_inputs) {
// FIXME: SetBlob is known to be inefficient,
// it is worth to make a customizable "initializer" and pass the
// cv::Mat-wrapped blob there to support IE's optimal "GetBlob idiom"
// Still, constant data is to set only once.
this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
}
return {this_plugin, this_network, this_request};
}
modules/gapi/src/backends/onnx/gonnxbackend.cpp
+64 -11
View File
@@ -16,6 +16,7 @@
#include <opencv2/gapi/gframe.hpp>
#include "api/gbackend_priv.hpp" // FIXME: Make it part of Backend SDK!
#include "logger.hpp"
namespace {
struct ONNXCallContext;
@@ -30,12 +31,35 @@ enum TensorPosition : int {
OUTPUT
};
static std::string pdims(const std::vector<int64_t> &dims) {
std::stringstream ss;
auto it = dims.begin();
ss << *it++;
for (; it != dims.end(); ++it) {
ss << '/' << *it;
}
return ss.str();
}
struct TensorInfo {
TensorInfo() = default;
explicit TensorInfo(const Ort::TensorTypeAndShapeInfo& info)
: dims(info.GetShape())
, type(info.GetElementType())
, is_dynamic(std::find(dims.begin(), dims.end(), -1) != dims.end()) {
// Double-check if the tensor is really dynamic
// Allow N to be -1
if (is_dynamic
&& dims[0] == -1
&& dims.size() > 1
&& std::find(dims.begin() + 1, dims.end(), -1) == dims.end()) {
GAPI_LOG_WARNING(NULL, "Promoting N=-1 to N=1 for tensor " << pdims(dims));
dims[0] = 1;
is_dynamic = false;
}
if (!is_dynamic) {
size = std::accumulate(dims.begin(),
dims.end(),
@@ -81,6 +105,7 @@ class ONNXCompiled {
std::vector<TensorInfo> in_tensor_info;
std::vector<TensorInfo> out_tensor_info;
bool is_dynamic = false;
bool is_postproc = false;
// G-API <Net> description
gapi::onnx::detail::ParamDesc params;
@@ -95,6 +120,7 @@ class ONNXCompiled {
void Run(const std::vector<cv::Mat>& ins,
const std::vector<cv::Mat>& outs);
std::vector<std::string> in_names_without_const;
public:
explicit ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp);
@@ -142,6 +168,7 @@ inline int toCV(ONNXTensorElementDataType prec) {
switch (prec) {
case ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8: return CV_8U;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: return CV_32F;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: return CV_32S;
default: GAPI_Assert(false && "Unsupported data type");
}
return -1;
@@ -308,6 +335,8 @@ inline Ort::Value createTensor(const Ort::MemoryInfo& memory_info,
return createTensor<uint8_t>(memory_info, tensor_params, data);
case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT:
return createTensor<float>(memory_info, tensor_params, data);
case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32:
return createTensor<int32_t>(memory_info, tensor_params, data);
default:
GAPI_Assert(false && "Unsupported data type");
}
@@ -523,7 +552,6 @@ namespace onnx {
ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
: params(pp) {
// Validate input parameters before allocating any resources
if (params.num_in > 1u && params.num_in != params.input_names.size()) {
cv::util::throw_error(std::logic_error("Please specify input layer names for "
@@ -553,6 +581,7 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
"Please provide a custom post-processing function "
"(.cfgPostProc) in network parameters"));
}
is_postproc = (params.custom_post_proc != nullptr);
// Update parameters based on session information
if (params.num_in == 1u && params.input_names.empty()) {
@@ -563,8 +592,6 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
}
// Validate what is supported currently
GAPI_Assert(params.const_inputs.empty()
&& "Const inputs are not currently supported");
GAPI_Assert(std::all_of(in_tensor_info.begin(),
in_tensor_info.end(),
[](const cv::gimpl::onnx::TensorInfo &p) {
@@ -593,6 +620,17 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
}
}
if (!params.const_inputs.empty()) {
// Form input names order without const input names
in_names_without_const.clear();
std::copy_if(params.input_names.begin(), params.input_names.end(),
std::back_inserter(in_names_without_const),
[&](const std::string& name) {
const auto it = params.const_inputs.find(name);
return it == params.const_inputs.end();
});
}
// Pre-allocate vectors (not buffers) for runtime info
in_data.resize(params.num_in);
out_data.resize(params.num_out);
@@ -626,9 +664,9 @@ std::vector<TensorInfo> ONNXCompiled::getTensorInfo(TensorPosition pos) {
}
cv::GMatDesc ONNXCompiled::outMeta(int idx) const {
if (is_dynamic) {
if (is_dynamic || is_postproc) {
GAPI_Assert(!params.out_metas.empty()
&& "Metadata must be specified if NN has dynamic inputs!");
&& "Metadata must be specified if NN has dynamic inputs or post-processing function is used!");
return params.out_metas.at(idx);
}
const auto ort_idx = getIdxByName(out_tensor_info, params.output_names[idx]);
@@ -678,9 +716,12 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
const std::vector<cv::Mat>& outs) {
std::vector<Ort::Value> in_tensors, out_tensors;
auto in_run_names = getCharNames(params.input_names);
for (const auto it : ade::util::indexed(params.input_names)) {
// Layer names order for run
auto input_names = (in_names_without_const.empty() && params.const_inputs.empty())
? params.input_names
: in_names_without_const;
// Creates tensors for unique names that don't contain constant input
for (const auto it : ade::util::indexed(input_names)) {
auto i = ade::util::index(it);
auto in_name = ade::util::value(it);
const auto idx = getIdxByName(in_tensor_info, in_name);
@@ -689,7 +730,19 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
ins[i]));
}
if (!is_dynamic) {
for (auto &&c_in_pair : params.const_inputs) {
const auto idx = getIdxByName(in_tensor_info, c_in_pair.first);
in_tensors.emplace_back(createTensor(this_memory_info,
in_tensor_info[idx],
c_in_pair.second.first));
// Puts const input names in sequence for Run
// ONNXRuntime can match input tensors to CNN inputs by names
input_names.emplace_back(c_in_pair.first);
}
GAPI_Assert(input_names.size() == this_session.GetInputCount());
auto in_run_names = getCharNames(input_names);
if (!is_dynamic && !is_postproc) {
// Easy path - just run the session which is bound to G-API's
// internal data
for (auto i : ade::util::iota(params.output_names.size())) {
@@ -701,7 +754,7 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
this_session.Run(Ort::RunOptions{nullptr},
in_run_names.data(),
&in_tensors.front(),
params.input_names.size(),
input_names.size(),
out_run_names.data(),
&out_tensors.front(),
params.output_names.size());
@@ -716,7 +769,7 @@ void ONNXCompiled::Run(const std::vector<cv::Mat>& ins,
auto outputs = this_session.Run(Ort::RunOptions{nullptr},
in_run_names.data(),
&in_tensors.front(),
params.input_names.size(),
input_names.size(),
out_names.data(),
out_names.size());
std::unordered_map<std::string, cv::Mat> onnx_outputs;