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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) 2018-2020 Intel Corporation
#include "../test_precomp.hpp"
#include <ade/util/zip_range.hpp> // util::indexed
#include <opencv2/gapi/gkernel.hpp>
#include <opencv2/gapi/gcommon.hpp>
#include "compiler/gmodelbuilder.hpp"
#include "compiler/gmodel.hpp" // RcDesc, GModel::init
namespace opencv_test
{
namespace test
{
namespace
{
namespace D = cv::detail;
cv::GMat unaryOp(cv::GMat m)
{
return cv::GCall(cv::GKernel{ "gapi.test.unaryop"
, ""
, nullptr
, { GShape::GMAT }
, { D::OpaqueKind::CV_UNKNOWN }
, { D::HostCtor{cv::util::monostate{}} }
, { D::OpaqueKind::CV_UNKNOWN }
}).pass(m).yield(0);
}
cv::GMat binaryOp(cv::GMat m1, cv::GMat m2)
{
return cv::GCall(cv::GKernel{ "gapi.test.binaryOp"
, ""
, nullptr
, { GShape::GMAT }
, { D::OpaqueKind::CV_UNKNOWN, D::OpaqueKind::CV_UNKNOWN }
, { D::HostCtor{cv::util::monostate{}} }
, { D::OpaqueKind::CV_UNKNOWN}
}).pass(m1, m2).yield(0);
}
std::vector<ade::NodeHandle> collectOperations(const cv::gimpl::GModel::Graph& gr)
{
std::vector<ade::NodeHandle> ops;
for (const auto& nh : gr.nodes())
{
if (gr.metadata(nh).get<cv::gimpl::NodeType>().t == cv::gimpl::NodeType::OP)
ops.push_back(nh);
}
return ops;
}
ade::NodeHandle inputOf(cv::gimpl::GModel::Graph& gm, ade::NodeHandle nh, std::size_t port)
{
for (const auto& eh : nh->inEdges())
{
if (gm.metadata(eh).get<cv::gimpl::Input>().port == port)
{
return eh->srcNode();
}
}
util::throw_error(std::logic_error("port " + std::to_string(port) + " not found"));
}
}
}// namespace opencv_test::test
TEST(GModelBuilder, Unroll_TestUnary)
{
cv::GMat in;
cv::GMat out = test::unaryOp(in);
auto unrolled = cv::gimpl::unrollExpr(cv::GIn(in).m_args, cv::GOut(out).m_args);
EXPECT_EQ(1u, unrolled.all_ops.size()); // There is one operation
EXPECT_EQ(2u, unrolled.all_data.size()); // And two data objects (in, out)
// TODO check what the operation is, and so on, and so on
}
TEST(GModelBuilder, Unroll_TestUnaryOfUnary)
{
cv::GMat in;
cv::GMat out = test::unaryOp(test::unaryOp(in));
auto unrolled = cv::gimpl::unrollExpr(cv::GIn(in).m_args, cv::GOut(out).m_args);
EXPECT_EQ(2u, unrolled.all_ops.size()); // There're two operations
EXPECT_EQ(3u, unrolled.all_data.size()); // And three data objects (in, out)
// TODO check what the operation is, and so on, and so on
}
TEST(GModelBuilder, Unroll_Not_All_Protocol_Inputs_Are_Reached)
{
cv::GMat in1, in2; // in1 -> unaryOp() -> u_op1 -> unaryOp() -> out
auto u_op1 = test::unaryOp(in1); // in2 -> unaryOp() -> u_op2
auto u_op2 = test::unaryOp(in2);
auto out = test::unaryOp(u_op1);
EXPECT_THROW(cv::gimpl::unrollExpr(cv::GIn(in1, in2).m_args, cv::GOut(out).m_args), std::logic_error);
}
TEST(GModelBuilder, Unroll_Parallel_Path)
{
cv::GMat in1, in2; // in1 -> unaryOp() -> out1
auto out1 = test::unaryOp(in1); // in2 -> unaryOp() -> out2
auto out2 = test::unaryOp(in2);
auto unrolled = cv::gimpl::unrollExpr(cv::GIn(in1, in2).m_args, cv::GOut(out1, out2).m_args);
EXPECT_EQ(unrolled.all_ops.size(), 2u);
EXPECT_EQ(unrolled.all_data.size(), 4u);
}
TEST(GModelBuilder, Unroll_WithBranch)
{
// in -> unaryOp() -> tmp -->unaryOp() -> out1
// `---->unaryOp() -> out2
GMat in;
auto tmp = test::unaryOp(in);
auto out1 = test::unaryOp(tmp);
auto out2 = test::unaryOp(tmp);
auto unrolled = cv::gimpl::unrollExpr(cv::GIn(in).m_args, cv::GOut(out1, out2).m_args);
EXPECT_EQ(unrolled.all_ops.size(), 3u);
EXPECT_EQ(unrolled.all_data.size(), 4u);
}
TEST(GModelBuilder, Build_Unary)
{
cv::GMat in;
cv::GMat out = test::unaryOp(in);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(out).m_args);
EXPECT_EQ(3u, static_cast<std::size_t>(g.nodes().size())); // Generated graph should have three nodes
// TODO: Check what the nodes are
}
TEST(GModelBuilder, Constant_GScalar)
{
// in -> addC()-----(GMat)---->mulC()-----(GMat)---->unaryOp()----out
// ^ ^
// | |
// 3-------` c_s-------'
cv::GMat in;
cv::GScalar c_s = 5;
auto out = test::unaryOp((in + 3) * c_s); // 3 converted to GScalar
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
auto proto_slots = cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(out).m_args);
cv::gimpl::Protocol p;
std::tie(p.inputs, p.outputs, p.in_nhs, p.out_nhs) = proto_slots;
auto in_nh = p.in_nhs.front();
auto addC_nh = in_nh->outNodes().front();
auto mulC_nh = addC_nh->outNodes().front()->outNodes().front();
ASSERT_TRUE(gm.metadata(addC_nh).get<cv::gimpl::NodeType>().t == cv::gimpl::NodeType::OP);
ASSERT_TRUE(gm.metadata(mulC_nh).get<cv::gimpl::NodeType>().t == cv::gimpl::NodeType::OP);
auto s_3 = test::inputOf(gm, addC_nh, 1);
auto s_5 = test::inputOf(gm, mulC_nh, 1);
EXPECT_EQ(9u, static_cast<std::size_t>(g.nodes().size())); // 6 data nodes (1 -input, 1 output, 2 constant, 2 temp) and 3 op nodes
EXPECT_EQ(2u, static_cast<std::size_t>(addC_nh->inNodes().size())); // in and 3
EXPECT_EQ(2u, static_cast<std::size_t>(mulC_nh->inNodes().size())); // addC output and c_s
EXPECT_EQ(3, (util::get<cv::Scalar>(gm.metadata(s_3).get<cv::gimpl::ConstValue>().arg))[0]);
EXPECT_EQ(5, (util::get<cv::Scalar>(gm.metadata(s_5).get<cv::gimpl::ConstValue>().arg))[0]);
}
TEST(GModelBuilder, Check_Multiple_Outputs)
{
// ------------------------------> r
// '
// ' -----------> i_out1
// ' '
// in ----> split3() ---> g ---> integral()
// ' '
// ' -----------> i_out2
// '
// '---------> b ---> unaryOp() ---> u_out
cv::GMat in, r, g, b, i_out1, i_out2, u_out;
std::tie(r, g, b) = cv::gapi::split3(in);
std::tie(i_out1, i_out2) = cv::gapi::integral(g, 1, 1);
u_out = test::unaryOp(b);
ade::Graph gr;
cv::gimpl::GModel::Graph gm(gr);
cv::gimpl::GModel::init(gm);
auto proto_slots = cv::gimpl::GModelBuilder(gr).put(cv::GIn(in).m_args, cv::GOut(r, i_out1, i_out2, u_out).m_args);
cv::gimpl::Protocol p;
std::tie(p.inputs, p.outputs, p.in_nhs, p.out_nhs) = proto_slots;
EXPECT_EQ(4u, static_cast<std::size_t>(p.out_nhs.size()));
EXPECT_EQ(0u, gm.metadata(p.out_nhs[0]->inEdges().front()).get<cv::gimpl::Output>().port);
EXPECT_EQ(0u, gm.metadata(p.out_nhs[1]->inEdges().front()).get<cv::gimpl::Output>().port);
EXPECT_EQ(1u, gm.metadata(p.out_nhs[2]->inEdges().front()).get<cv::gimpl::Output>().port);
EXPECT_EQ(0u, gm.metadata(p.out_nhs[3]->inEdges().front()).get<cv::gimpl::Output>().port);
for (const auto it : ade::util::indexed(p.out_nhs))
{
const auto& out_nh = ade::util::value(it);
EXPECT_EQ(cv::gimpl::NodeType::DATA, gm.metadata(out_nh).get<cv::gimpl::NodeType>().t);
EXPECT_EQ(GShape::GMAT, gm.metadata(out_nh).get<cv::gimpl::Data>().shape);
}
}
TEST(GModelBuilder, Unused_Outputs)
{
cv::GMat in;
auto yuv_p = cv::gapi::split3(in);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(std::get<0>(yuv_p)).m_args);
EXPECT_EQ(5u, static_cast<std::size_t>(g.nodes().size())); // 1 input, 1 operation, 3 outputs
}
TEST(GModelBuilder, Work_With_One_Channel_From_Split3)
{
cv::GMat in, y, u, v;
std::tie(y, u, v) = cv::gapi::split3(in);
auto y_blur = cv::gapi::gaussianBlur(y, cv::Size(3, 3), 1);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(y_blur).m_args);
EXPECT_EQ(7u, static_cast<std::size_t>(g.nodes().size())); // 1 input, 2 operation, 3 nodes from split3, 1 output
}
TEST(GModelBuilder, Add_Nodes_To_Unused_Nodes)
{
cv::GMat in, y, u, v;
std::tie(y, u, v) = cv::gapi::split3(in);
auto y_blur = cv::gapi::gaussianBlur(y, cv::Size(3, 3), 1);
// unused nodes
auto u_blur = cv::gapi::gaussianBlur(y, cv::Size(3, 3), 1);
auto v_blur = cv::gapi::gaussianBlur(y, cv::Size(3, 3), 1);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(y_blur).m_args);
EXPECT_EQ(7u, static_cast<std::size_t>(g.nodes().size())); // 1 input, 2 operation, 3 nodes from split3, 1 output
}
TEST(GModelBuilder, Unlisted_Inputs)
{
// in1 -> binaryOp() -> out
// ^
// |
// in2 ----'
cv::GMat in1, in2;
auto out = test::binaryOp(in1, in2);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
// add required 2 inputs but pass 1
EXPECT_THROW(cv::gimpl::GModelBuilder(g).put(cv::GIn(in1).m_args, cv::GOut(out).m_args), std::logic_error);
}
TEST(GModelBuilder, Unroll_No_Link_Between_In_And_Out)
{
// in -> unaryOp() -> u_op
// other -> unaryOp() -> out
cv::GMat in, other;
auto u_op = test::unaryOp(in);
auto out = test::unaryOp(other);
EXPECT_THROW(cv::gimpl::unrollExpr(cv::GIn(in).m_args, cv::GOut(out).m_args), std::logic_error);
}
TEST(GModel_builder, Check_Binary_Op)
{
// in1 -> binaryOp() -> out
// ^
// |
// in2 -----'
cv::GMat in1, in2;
auto out = test::binaryOp(in1, in2);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
auto proto_slots = cv::gimpl::GModelBuilder(g).put(cv::GIn(in1, in2).m_args, cv::GOut(out).m_args);
cv::gimpl::Protocol p;
std::tie(p.inputs, p.outputs, p.in_nhs, p.out_nhs) = proto_slots;
auto ops = test::collectOperations(g);
EXPECT_EQ(1u, ops.size());
EXPECT_EQ("gapi.test.binaryOp", gm.metadata(ops.front()).get<cv::gimpl::Op>().k.name);
EXPECT_EQ(2u, static_cast<std::size_t>(ops.front()->inEdges().size()));
EXPECT_EQ(1u, static_cast<std::size_t>(ops.front()->outEdges().size()));
EXPECT_EQ(1u, static_cast<std::size_t>(ops.front()->outNodes().size()));
}
TEST(GModelBuilder, Add_Operation_With_Two_Out_One_Time)
{
// in -> integral() --> out_b1 -> unaryOp() -> out1
// |
// '-------> out_b2 -> unaryOp() -> out2
cv::GMat in, out_b1, out_b2;
std::tie(out_b1, out_b2) = cv::gapi::integral(in, 1, 1);
auto out1 = test::unaryOp(out_b1);
auto out2 = test::unaryOp(out_b1);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
auto proto_slots = cv::gimpl::GModelBuilder(g).put(cv::GIn(in).m_args, cv::GOut(out1, out2).m_args);
auto ops = test::collectOperations(gm);
cv::gimpl::Protocol p;
std::tie(p.inputs, p.outputs, p.in_nhs, p.out_nhs) = proto_slots;
auto integral_nh = p.in_nhs.front()->outNodes().front();
EXPECT_EQ(3u, ops.size());
EXPECT_EQ("org.opencv.core.matrixop.integral", gm.metadata(integral_nh).get<cv::gimpl::Op>().k.name);
EXPECT_EQ(1u, static_cast<std::size_t>(integral_nh->inEdges().size()));
EXPECT_EQ(2u, static_cast<std::size_t>(integral_nh->outEdges().size()));
EXPECT_EQ(2u, static_cast<std::size_t>(integral_nh->outNodes().size()));
}
TEST(GModelBuilder, Add_Operation_With_One_Out_One_Time)
{
// in1 -> binaryOp() -> b_out -> unaryOp() -> out1
// ^ |
// | |
// in2 ------- '----> unaryOp() -> out2
cv::GMat in1, in2;
auto b_out = test::binaryOp(in1, in2);
auto out1 = test::unaryOp(b_out);
auto out2 = test::unaryOp(b_out);
ade::Graph g;
cv::gimpl::GModel::Graph gm(g);
cv::gimpl::GModel::init(gm);
auto proto_slots = cv::gimpl::GModelBuilder(g).put(cv::GIn(in1, in2).m_args, cv::GOut(out1, out2).m_args);
cv::gimpl::Protocol p;
std::tie(p.inputs, p.outputs, p.in_nhs, p.out_nhs) = proto_slots;
cv::gimpl::GModel::Graph gr(g);
auto binaryOp_nh = p.in_nhs.front()->outNodes().front();
EXPECT_EQ(2u, static_cast<std::size_t>(binaryOp_nh->inEdges().size()));
EXPECT_EQ(1u, static_cast<std::size_t>(binaryOp_nh->outEdges().size()));
EXPECT_EQ(8u, static_cast<std::size_t>(g.nodes().size()));
}
} // namespace opencv_test
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