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ffmpeg (v6.0/v7.x), gensim, numpy, opencv
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018 Intel Corporation
#include "precomp.hpp"
#include <algorithm> // remove_if
#include <cctype> // isspace (non-locale version)
#include <ade/util/algorithm.hpp>
#include <ade/util/zip_range.hpp> // util::indexed
#include "logger.hpp" // GAPI_LOG
#include <opencv2/gapi/gcomputation.hpp>
#include <opencv2/gapi/gkernel.hpp>
#include "api/gcomputation_priv.hpp"
#include "api/gcall_priv.hpp"
#include "api/gnode_priv.hpp"
#include "compiler/gmodelbuilder.hpp"
#include "compiler/gcompiler.hpp"
#include "compiler/gcompiled_priv.hpp"
#include "compiler/gstreaming_priv.hpp"
static cv::GTypesInfo collectInfo(const cv::gimpl::GModel::ConstGraph& g,
const std::vector<ade::NodeHandle>& nhs) {
cv::GTypesInfo info;
info.reserve(nhs.size());
ade::util::transform(nhs, std::back_inserter(info), [&g](const ade::NodeHandle& nh) {
const auto& data = g.metadata(nh).get<cv::gimpl::Data>();
return cv::GTypeInfo{data.shape, data.kind, data.ctor};
});
return info;
}
// NB: This function is used to collect graph input/output info.
// Needed for python bridge to unpack inputs and constructs outputs properly.
static cv::GraphInfo::Ptr collectGraphInfo(const cv::GComputation::Priv& priv)
{
auto g = cv::gimpl::GCompiler::makeGraph(priv);
cv::gimpl::GModel::ConstGraph cgr(*g);
auto in_info = collectInfo(cgr, cgr.metadata().get<cv::gimpl::Protocol>().in_nhs);
auto out_info = collectInfo(cgr, cgr.metadata().get<cv::gimpl::Protocol>().out_nhs);
return cv::GraphInfo::Ptr(new cv::GraphInfo{std::move(in_info), std::move(out_info)});
}
// cv::GComputation private implementation /////////////////////////////////////
// <none>
// cv::GComputation public implementation //////////////////////////////////////
cv::GComputation::GComputation(const Generator& gen)
: m_priv(gen().m_priv)
{
}
cv::GComputation::GComputation(GMat in, GMat out)
: cv::GComputation(cv::GIn(in), cv::GOut(out))
{
}
cv::GComputation::GComputation(GMat in, GScalar out)
: cv::GComputation(cv::GIn(in), cv::GOut(out))
{
}
cv::GComputation::GComputation(GMat in1, GMat in2, GMat out)
: cv::GComputation(cv::GIn(in1, in2), cv::GOut(out))
{
}
cv::GComputation::GComputation(GMat in1, GMat in2, GScalar out)
: cv::GComputation(cv::GIn(in1, in2), cv::GOut(out))
{
}
cv::GComputation::GComputation(const std::vector<GMat> &ins,
const std::vector<GMat> &outs)
: m_priv(new Priv())
{
Priv::Expr e;
const auto wrap = [](cv::GMat m) { return GProtoArg(m); };
ade::util::transform(ins, std::back_inserter(e.m_ins), wrap);
ade::util::transform(outs, std::back_inserter(e.m_outs), wrap);
m_priv->m_shape = std::move(e);
}
cv::GComputation::GComputation(cv::GProtoInputArgs &&ins,
cv::GProtoOutputArgs &&outs)
: m_priv(new Priv())
{
m_priv->m_shape = Priv::Expr{
std::move(ins.m_args)
, std::move(outs.m_args)
};
}
cv::GComputation::GComputation(cv::gapi::s11n::IIStream &is)
: m_priv(new Priv())
{
m_priv->m_shape = gapi::s11n::deserialize(is);
}
void cv::GComputation::serialize(cv::gapi::s11n::IOStream &os) const
{
// Build a basic GModel and write the whole thing to the stream
auto pG = cv::gimpl::GCompiler::makeGraph(*m_priv);
std::vector<ade::NodeHandle> nhs(pG->nodes().begin(), pG->nodes().end());
gapi::s11n::serialize(os, *pG, nhs);
}
cv::GCompiled cv::GComputation::compile(GMetaArgs &&metas, GCompileArgs &&args)
{
// FIXME: Cache gcompiled per parameters here?
cv::gimpl::GCompiler comp(*this, std::move(metas), std::move(args));
return comp.compile();
}
cv::GStreamingCompiled cv::GComputation::compileStreaming(GMetaArgs &&metas, GCompileArgs &&args)
{
cv::gimpl::GCompiler comp(*this, std::move(metas), std::move(args));
return comp.compileStreaming();
}
cv::GStreamingCompiled cv::GComputation::compileStreaming(GCompileArgs &&args)
{
// NB: Used by python bridge
if (!m_priv->m_info)
{
m_priv->m_info = collectGraphInfo(*m_priv);
}
cv::gimpl::GCompiler comp(*this, {}, std::move(args));
auto compiled = comp.compileStreaming();
compiled.priv().setInInfo(m_priv->m_info->inputs);
compiled.priv().setOutInfo(m_priv->m_info->outputs);
return compiled;
}
cv::GStreamingCompiled cv::GComputation::compileStreaming(const cv::detail::ExtractMetaCallback &callback,
GCompileArgs &&args)
{
// NB: Used by python bridge
if (!m_priv->m_info)
{
m_priv->m_info = collectGraphInfo(*m_priv);
}
auto ins = callback(m_priv->m_info->inputs);
cv::gimpl::GCompiler comp(*this, std::move(ins), std::move(args));
auto compiled = comp.compileStreaming();
compiled.priv().setInInfo(m_priv->m_info->inputs);
compiled.priv().setOutInfo(m_priv->m_info->outputs);
return compiled;
}
// FIXME: Introduce similar query/test method for GMetaArgs as a building block
// for functions like this?
static bool formats_are_same(const cv::GMetaArgs& metas1, const cv::GMetaArgs& metas2)
{
return std::equal(metas1.cbegin(), metas1.cend(), metas2.cbegin(),
[](const cv::GMetaArg& meta1, const cv::GMetaArg& meta2) {
if (meta1.index() == meta2.index() && meta1.index() == cv::GMetaArg::index_of<cv::GMatDesc>())
{
const auto& desc1 = cv::util::get<cv::GMatDesc>(meta1);
const auto& desc2 = cv::util::get<cv::GMatDesc>(meta2);
// comparison by size is omitted
return (desc1.chan == desc2.chan &&
desc1.depth == desc2.depth);
}
else
{
return meta1 == meta2;
}
});
}
void cv::GComputation::recompile(GMetaArgs&& in_metas, GCompileArgs &&args)
{
// FIXME Graph should be recompiled when GCompileArgs have changed
if (m_priv->m_lastMetas != in_metas)
{
if (m_priv->m_lastCompiled &&
m_priv->m_lastCompiled.canReshape() &&
formats_are_same(m_priv->m_lastMetas, in_metas))
{
m_priv->m_lastCompiled.reshape(in_metas, args);
}
else
{
// FIXME: Had to construct temporary object as compile() takes && (r-value)
m_priv->m_lastCompiled = compile(GMetaArgs(in_metas), std::move(args));
}
m_priv->m_lastMetas = in_metas;
}
else if (in_metas.size() == 0) {
// Happens when the graph is head-less (e.g. starts with const-vals only)
// always compile ad-hoc
m_priv->m_lastCompiled = compile(GMetaArgs(in_metas), std::move(args));
}
}
void cv::GComputation::apply(GRunArgs &&ins, GRunArgsP &&outs, GCompileArgs &&args)
{
recompile(descr_of(ins), std::move(args));
m_priv->m_lastCompiled(std::move(ins), std::move(outs));
}
void cv::GComputation::apply(const std::vector<cv::Mat> &ins,
const std::vector<cv::Mat> &outs,
GCompileArgs &&args)
{
GRunArgs call_ins;
GRunArgsP call_outs;
auto tmp = outs;
for (const cv::Mat &m : ins) { call_ins.emplace_back(m); }
for ( cv::Mat &m : tmp) { call_outs.emplace_back(&m); }
apply(std::move(call_ins), std::move(call_outs), std::move(args));
}
// NB: This overload is called from python code
cv::GRunArgs cv::GComputation::apply(const cv::detail::ExtractArgsCallback &callback,
GCompileArgs &&args)
{
// NB: Used by python bridge
if (!m_priv->m_info)
{
m_priv->m_info = collectGraphInfo(*m_priv);
}
auto ins = callback(m_priv->m_info->inputs);
recompile(descr_of(ins), std::move(args));
GRunArgs run_args;
GRunArgsP outs;
run_args.reserve(m_priv->m_info->outputs.size());
outs.reserve(m_priv->m_info->outputs.size());
cv::detail::constructGraphOutputs(m_priv->m_info->outputs, run_args, outs);
m_priv->m_lastCompiled(std::move(ins), std::move(outs));
return run_args;
}
#if !defined(GAPI_STANDALONE)
void cv::GComputation::apply(cv::Mat in, cv::Mat &out, GCompileArgs &&args)
{
apply(cv::gin(in), cv::gout(out), std::move(args));
// FIXME: The following doesn't work!
// Operation result is not replicated into user's object
// apply({GRunArg(in)}, {GRunArg(out)});
}
void cv::GComputation::apply(cv::Mat in, cv::Scalar &out, GCompileArgs &&args)
{
apply(cv::gin(in), cv::gout(out), std::move(args));
}
void cv::GComputation::apply(cv::Mat in1, cv::Mat in2, cv::Mat &out, GCompileArgs &&args)
{
apply(cv::gin(in1, in2), cv::gout(out), std::move(args));
}
void cv::GComputation::apply(cv::Mat in1, cv::Mat in2, cv::Scalar &out, GCompileArgs &&args)
{
apply(cv::gin(in1, in2), cv::gout(out), std::move(args));
}
void cv::GComputation::apply(const std::vector<cv::Mat> &ins,
std::vector<cv::Mat> &outs,
GCompileArgs &&args)
{
GRunArgs call_ins;
GRunArgsP call_outs;
for (const cv::Mat &m : ins) { call_ins.emplace_back(m); }
for ( cv::Mat &m : outs) { call_outs.emplace_back(&m); }
apply(std::move(call_ins), std::move(call_outs), std::move(args));
}
#endif // !defined(GAPI_STANDALONE)
cv::GComputation::Priv& cv::GComputation::priv()
{
return *m_priv;
}
const cv::GComputation::Priv& cv::GComputation::priv() const
{
return *m_priv;
}
// Islands /////////////////////////////////////////////////////////////////////
void cv::gapi::island(const std::string &name,
GProtoInputArgs &&ins,
GProtoOutputArgs &&outs)
{
{
// Island must have a printable name.
// Forbid names which contain only spaces.
GAPI_Assert(!name.empty());
const auto first_printable_it = std::find_if_not(name.begin(), name.end(), isspace);
const bool likely_printable = first_printable_it != name.end();
GAPI_Assert(likely_printable);
}
// Even if the name contains spaces, keep it unmodified as user will
// then use this string to assign affinity, etc.
// First, set island tags on all operations from `ins` to `outs`
auto island = cv::gimpl::unrollExpr(ins.m_args, outs.m_args);
if (island.all_ops.empty())
{
util::throw_error(std::logic_error("Operation range is empty"));
}
for (auto &op_expr_node : island.all_ops)
{
auto &op_expr_node_p = op_expr_node.priv();
GAPI_Assert(op_expr_node.shape() == GNode::NodeShape::CALL);
const GCall& call = op_expr_node.call();
const GCall::Priv& call_p = call.priv();
if (!op_expr_node_p.m_island.empty())
{
util::throw_error(std::logic_error
( "Operation " + call_p.m_k.name
+ " is already assigned to island \""
+ op_expr_node_p.m_island + "\""));
}
else
{
op_expr_node_p.m_island = name;
GAPI_LOG_INFO(NULL,
"Assigned " << call_p.m_k.name << "_" << &call_p <<
" to island \"" << name << "\"");
}
}
// Note - this function only sets islands to all operations in
// expression tree, it is just a first step.
// The second step is assigning intermediate data objects to Islands,
// see passes::initIslands for details.
}