ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
nntrainer::Tensor m =
ranged(1, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
- _Float16 answer_data[] = {static_cast<_Float16>(0), static_cast<_Float16>(1),
- static_cast<_Float16>(4), static_cast<_Float16>(9),
- static_cast<_Float16>(16), static_cast<_Float16>(0),
- static_cast<_Float16>(6), static_cast<_Float16>(14),
- static_cast<_Float16>(24), static_cast<_Float16>(36),
- static_cast<_Float16>(0), static_cast<_Float16>(11),
- static_cast<_Float16>(24), static_cast<_Float16>(39),
- static_cast<_Float16>(56), static_cast<_Float16>(0),
- static_cast<_Float16>(16), static_cast<_Float16>(34),
- static_cast<_Float16>(54), static_cast<_Float16>(76),
- static_cast<_Float16>(0), static_cast<_Float16>(21),
- static_cast<_Float16>(44), static_cast<_Float16>(69),
- static_cast<_Float16>(96), static_cast<_Float16>(0),
- static_cast<_Float16>(26), static_cast<_Float16>(54),
- static_cast<_Float16>(84), static_cast<_Float16>(116),
- static_cast<_Float16>(0), static_cast<_Float16>(31),
- static_cast<_Float16>(64), static_cast<_Float16>(99),
- static_cast<_Float16>(136), static_cast<_Float16>(0),
- static_cast<_Float16>(36), static_cast<_Float16>(74),
- static_cast<_Float16>(114), static_cast<_Float16>(156),
- static_cast<_Float16>(0), static_cast<_Float16>(41),
- static_cast<_Float16>(84), static_cast<_Float16>(129),
- static_cast<_Float16>(176), static_cast<_Float16>(0),
- static_cast<_Float16>(46), static_cast<_Float16>(94),
- static_cast<_Float16>(144), static_cast<_Float16>(196),
- static_cast<_Float16>(0), static_cast<_Float16>(51),
- static_cast<_Float16>(104), static_cast<_Float16>(159),
- static_cast<_Float16>(216), static_cast<_Float16>(0),
- static_cast<_Float16>(56), static_cast<_Float16>(114),
- static_cast<_Float16>(174), static_cast<_Float16>(236),
- static_cast<_Float16>(0), static_cast<_Float16>(61),
- static_cast<_Float16>(124), static_cast<_Float16>(189),
- static_cast<_Float16>(256), static_cast<_Float16>(0),
- static_cast<_Float16>(66), static_cast<_Float16>(134),
- static_cast<_Float16>(204), static_cast<_Float16>(276),
- static_cast<_Float16>(0), static_cast<_Float16>(71),
- static_cast<_Float16>(144), static_cast<_Float16>(219),
- static_cast<_Float16>(296), static_cast<_Float16>(0),
- static_cast<_Float16>(76), static_cast<_Float16>(154),
- static_cast<_Float16>(234), static_cast<_Float16>(316),
- static_cast<_Float16>(0), static_cast<_Float16>(81),
- static_cast<_Float16>(164), static_cast<_Float16>(249),
- static_cast<_Float16>(336), static_cast<_Float16>(0),
- static_cast<_Float16>(86), static_cast<_Float16>(174),
- static_cast<_Float16>(264), static_cast<_Float16>(356),
- static_cast<_Float16>(0), static_cast<_Float16>(91),
- static_cast<_Float16>(184), static_cast<_Float16>(279),
- static_cast<_Float16>(376), static_cast<_Float16>(0),
- static_cast<_Float16>(96), static_cast<_Float16>(194),
- static_cast<_Float16>(294), static_cast<_Float16>(396),
- static_cast<_Float16>(0), static_cast<_Float16>(101),
- static_cast<_Float16>(204), static_cast<_Float16>(309),
- static_cast<_Float16>(416), static_cast<_Float16>(0),
- static_cast<_Float16>(106), static_cast<_Float16>(214),
- static_cast<_Float16>(324), static_cast<_Float16>(436),
- static_cast<_Float16>(0), static_cast<_Float16>(111),
- static_cast<_Float16>(224), static_cast<_Float16>(339),
- static_cast<_Float16>(456), static_cast<_Float16>(0),
- static_cast<_Float16>(116), static_cast<_Float16>(234),
- static_cast<_Float16>(354), static_cast<_Float16>(476)};
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(4), static_cast<_Float16>(9),
+ static_cast<_Float16>(16), static_cast<_Float16>(0),
+ static_cast<_Float16>(6), static_cast<_Float16>(14),
+ static_cast<_Float16>(24), static_cast<_Float16>(36),
+ static_cast<_Float16>(0), static_cast<_Float16>(11),
+ static_cast<_Float16>(24), static_cast<_Float16>(39),
+ static_cast<_Float16>(56), static_cast<_Float16>(0),
+ static_cast<_Float16>(16), static_cast<_Float16>(34),
+ static_cast<_Float16>(54), static_cast<_Float16>(76),
+ static_cast<_Float16>(0), static_cast<_Float16>(21),
+ static_cast<_Float16>(44), static_cast<_Float16>(69),
+ static_cast<_Float16>(96), static_cast<_Float16>(0),
+ static_cast<_Float16>(26), static_cast<_Float16>(54),
+ static_cast<_Float16>(84), static_cast<_Float16>(116),
+ static_cast<_Float16>(0), static_cast<_Float16>(31),
+ static_cast<_Float16>(64), static_cast<_Float16>(99),
+ static_cast<_Float16>(136), static_cast<_Float16>(0),
+ static_cast<_Float16>(36), static_cast<_Float16>(74),
+ static_cast<_Float16>(114), static_cast<_Float16>(156),
+ static_cast<_Float16>(0), static_cast<_Float16>(41),
+ static_cast<_Float16>(84), static_cast<_Float16>(129),
+ static_cast<_Float16>(176), static_cast<_Float16>(0),
+ static_cast<_Float16>(46), static_cast<_Float16>(94),
+ static_cast<_Float16>(144), static_cast<_Float16>(196),
+ static_cast<_Float16>(0), static_cast<_Float16>(51),
+ static_cast<_Float16>(104), static_cast<_Float16>(159),
+ static_cast<_Float16>(216), static_cast<_Float16>(0),
+ static_cast<_Float16>(56), static_cast<_Float16>(114),
+ static_cast<_Float16>(174), static_cast<_Float16>(236),
+ static_cast<_Float16>(0), static_cast<_Float16>(61),
+ static_cast<_Float16>(124), static_cast<_Float16>(189),
+ static_cast<_Float16>(256), static_cast<_Float16>(0),
+ static_cast<_Float16>(66), static_cast<_Float16>(134),
+ static_cast<_Float16>(204), static_cast<_Float16>(276),
+ static_cast<_Float16>(0), static_cast<_Float16>(71),
+ static_cast<_Float16>(144), static_cast<_Float16>(219),
+ static_cast<_Float16>(296), static_cast<_Float16>(0),
+ static_cast<_Float16>(76), static_cast<_Float16>(154),
+ static_cast<_Float16>(234), static_cast<_Float16>(316),
+ static_cast<_Float16>(0), static_cast<_Float16>(81),
+ static_cast<_Float16>(164), static_cast<_Float16>(249),
+ static_cast<_Float16>(336), static_cast<_Float16>(0),
+ static_cast<_Float16>(86), static_cast<_Float16>(174),
+ static_cast<_Float16>(264), static_cast<_Float16>(356),
+ static_cast<_Float16>(0), static_cast<_Float16>(91),
+ static_cast<_Float16>(184), static_cast<_Float16>(279),
+ static_cast<_Float16>(376), static_cast<_Float16>(0),
+ static_cast<_Float16>(96), static_cast<_Float16>(194),
+ static_cast<_Float16>(294), static_cast<_Float16>(396),
+ static_cast<_Float16>(0), static_cast<_Float16>(101),
+ static_cast<_Float16>(204), static_cast<_Float16>(309),
+ static_cast<_Float16>(416), static_cast<_Float16>(0),
+ static_cast<_Float16>(106), static_cast<_Float16>(214),
+ static_cast<_Float16>(324), static_cast<_Float16>(436),
+ static_cast<_Float16>(0), static_cast<_Float16>(111),
+ static_cast<_Float16>(224), static_cast<_Float16>(339),
+ static_cast<_Float16>(456), static_cast<_Float16>(0),
+ static_cast<_Float16>(116), static_cast<_Float16>(234),
+ static_cast<_Float16>(354), static_cast<_Float16>(476)};
nntrainer::Tensor answer(ref_dim, answer_data);
int status = t.multiply_i(m);
EXPECT_EQ(status, ML_ERROR_NONE);
ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
nntrainer::Tensor m =
ranged(1, 2, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
- _Float16 answer_data[] = {static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(20), static_cast<_Float16>(21),
- static_cast<_Float16>(22), static_cast<_Float16>(23),
- static_cast<_Float16>(24), static_cast<_Float16>(25),
- static_cast<_Float16>(26), static_cast<_Float16>(27),
- static_cast<_Float16>(28), static_cast<_Float16>(29),
- static_cast<_Float16>(30), static_cast<_Float16>(31),
- static_cast<_Float16>(32), static_cast<_Float16>(33),
- static_cast<_Float16>(34), static_cast<_Float16>(35),
- static_cast<_Float16>(36), static_cast<_Float16>(37),
- static_cast<_Float16>(38), static_cast<_Float16>(39),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(60), static_cast<_Float16>(61),
- static_cast<_Float16>(62), static_cast<_Float16>(63),
- static_cast<_Float16>(64), static_cast<_Float16>(65),
- static_cast<_Float16>(66), static_cast<_Float16>(67),
- static_cast<_Float16>(68), static_cast<_Float16>(69),
- static_cast<_Float16>(70), static_cast<_Float16>(71),
- static_cast<_Float16>(72), static_cast<_Float16>(73),
- static_cast<_Float16>(74), static_cast<_Float16>(75),
- static_cast<_Float16>(76), static_cast<_Float16>(77),
- static_cast<_Float16>(78), static_cast<_Float16>(79),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(100), static_cast<_Float16>(101),
- static_cast<_Float16>(102), static_cast<_Float16>(103),
- static_cast<_Float16>(104), static_cast<_Float16>(105),
- static_cast<_Float16>(106), static_cast<_Float16>(107),
- static_cast<_Float16>(108), static_cast<_Float16>(109),
- static_cast<_Float16>(110), static_cast<_Float16>(111),
- static_cast<_Float16>(112), static_cast<_Float16>(113),
- static_cast<_Float16>(114), static_cast<_Float16>(115),
- static_cast<_Float16>(116), static_cast<_Float16>(117),
- static_cast<_Float16>(118), static_cast<_Float16>(119)};
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(20), static_cast<_Float16>(21),
+ static_cast<_Float16>(22), static_cast<_Float16>(23),
+ static_cast<_Float16>(24), static_cast<_Float16>(25),
+ static_cast<_Float16>(26), static_cast<_Float16>(27),
+ static_cast<_Float16>(28), static_cast<_Float16>(29),
+ static_cast<_Float16>(30), static_cast<_Float16>(31),
+ static_cast<_Float16>(32), static_cast<_Float16>(33),
+ static_cast<_Float16>(34), static_cast<_Float16>(35),
+ static_cast<_Float16>(36), static_cast<_Float16>(37),
+ static_cast<_Float16>(38), static_cast<_Float16>(39),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(60), static_cast<_Float16>(61),
+ static_cast<_Float16>(62), static_cast<_Float16>(63),
+ static_cast<_Float16>(64), static_cast<_Float16>(65),
+ static_cast<_Float16>(66), static_cast<_Float16>(67),
+ static_cast<_Float16>(68), static_cast<_Float16>(69),
+ static_cast<_Float16>(70), static_cast<_Float16>(71),
+ static_cast<_Float16>(72), static_cast<_Float16>(73),
+ static_cast<_Float16>(74), static_cast<_Float16>(75),
+ static_cast<_Float16>(76), static_cast<_Float16>(77),
+ static_cast<_Float16>(78), static_cast<_Float16>(79),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(100), static_cast<_Float16>(101),
+ static_cast<_Float16>(102), static_cast<_Float16>(103),
+ static_cast<_Float16>(104), static_cast<_Float16>(105),
+ static_cast<_Float16>(106), static_cast<_Float16>(107),
+ static_cast<_Float16>(108), static_cast<_Float16>(109),
+ static_cast<_Float16>(110), static_cast<_Float16>(111),
+ static_cast<_Float16>(112), static_cast<_Float16>(113),
+ static_cast<_Float16>(114), static_cast<_Float16>(115),
+ static_cast<_Float16>(116), static_cast<_Float16>(117),
+ static_cast<_Float16>(118), static_cast<_Float16>(119)};
nntrainer::Tensor answer(ref_dim, answer_data);
int status = t.multiply_i(m);
EXPECT_EQ(status, ML_ERROR_NONE);
ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
nntrainer::Tensor m =
ranged(3, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
- _Float16 answer_data[] = {static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(0), static_cast<_Float16>(0),
- static_cast<_Float16>(40), static_cast<_Float16>(41),
- static_cast<_Float16>(42), static_cast<_Float16>(43),
- static_cast<_Float16>(44), static_cast<_Float16>(45),
- static_cast<_Float16>(46), static_cast<_Float16>(47),
- static_cast<_Float16>(48), static_cast<_Float16>(49),
- static_cast<_Float16>(50), static_cast<_Float16>(51),
- static_cast<_Float16>(52), static_cast<_Float16>(53),
- static_cast<_Float16>(54), static_cast<_Float16>(55),
- static_cast<_Float16>(56), static_cast<_Float16>(57),
- static_cast<_Float16>(58), static_cast<_Float16>(59),
- static_cast<_Float16>(60), static_cast<_Float16>(61),
- static_cast<_Float16>(62), static_cast<_Float16>(63),
- static_cast<_Float16>(64), static_cast<_Float16>(65),
- static_cast<_Float16>(66), static_cast<_Float16>(67),
- static_cast<_Float16>(68), static_cast<_Float16>(69),
- static_cast<_Float16>(70), static_cast<_Float16>(71),
- static_cast<_Float16>(72), static_cast<_Float16>(73),
- static_cast<_Float16>(74), static_cast<_Float16>(75),
- static_cast<_Float16>(76), static_cast<_Float16>(77),
- static_cast<_Float16>(78), static_cast<_Float16>(79),
- static_cast<_Float16>(160), static_cast<_Float16>(162),
- static_cast<_Float16>(164), static_cast<_Float16>(166),
- static_cast<_Float16>(168), static_cast<_Float16>(170),
- static_cast<_Float16>(172), static_cast<_Float16>(174),
- static_cast<_Float16>(176), static_cast<_Float16>(178),
- static_cast<_Float16>(180), static_cast<_Float16>(182),
- static_cast<_Float16>(184), static_cast<_Float16>(186),
- static_cast<_Float16>(188), static_cast<_Float16>(190),
- static_cast<_Float16>(192), static_cast<_Float16>(194),
- static_cast<_Float16>(196), static_cast<_Float16>(198),
- static_cast<_Float16>(200), static_cast<_Float16>(202),
- static_cast<_Float16>(204), static_cast<_Float16>(206),
- static_cast<_Float16>(208), static_cast<_Float16>(210),
- static_cast<_Float16>(212), static_cast<_Float16>(214),
- static_cast<_Float16>(216), static_cast<_Float16>(218),
- static_cast<_Float16>(220), static_cast<_Float16>(222),
- static_cast<_Float16>(224), static_cast<_Float16>(226),
- static_cast<_Float16>(228), static_cast<_Float16>(230),
- static_cast<_Float16>(232), static_cast<_Float16>(234),
- static_cast<_Float16>(236), static_cast<_Float16>(238)};
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(0), static_cast<_Float16>(0),
+ static_cast<_Float16>(40), static_cast<_Float16>(41),
+ static_cast<_Float16>(42), static_cast<_Float16>(43),
+ static_cast<_Float16>(44), static_cast<_Float16>(45),
+ static_cast<_Float16>(46), static_cast<_Float16>(47),
+ static_cast<_Float16>(48), static_cast<_Float16>(49),
+ static_cast<_Float16>(50), static_cast<_Float16>(51),
+ static_cast<_Float16>(52), static_cast<_Float16>(53),
+ static_cast<_Float16>(54), static_cast<_Float16>(55),
+ static_cast<_Float16>(56), static_cast<_Float16>(57),
+ static_cast<_Float16>(58), static_cast<_Float16>(59),
+ static_cast<_Float16>(60), static_cast<_Float16>(61),
+ static_cast<_Float16>(62), static_cast<_Float16>(63),
+ static_cast<_Float16>(64), static_cast<_Float16>(65),
+ static_cast<_Float16>(66), static_cast<_Float16>(67),
+ static_cast<_Float16>(68), static_cast<_Float16>(69),
+ static_cast<_Float16>(70), static_cast<_Float16>(71),
+ static_cast<_Float16>(72), static_cast<_Float16>(73),
+ static_cast<_Float16>(74), static_cast<_Float16>(75),
+ static_cast<_Float16>(76), static_cast<_Float16>(77),
+ static_cast<_Float16>(78), static_cast<_Float16>(79),
+ static_cast<_Float16>(160), static_cast<_Float16>(162),
+ static_cast<_Float16>(164), static_cast<_Float16>(166),
+ static_cast<_Float16>(168), static_cast<_Float16>(170),
+ static_cast<_Float16>(172), static_cast<_Float16>(174),
+ static_cast<_Float16>(176), static_cast<_Float16>(178),
+ static_cast<_Float16>(180), static_cast<_Float16>(182),
+ static_cast<_Float16>(184), static_cast<_Float16>(186),
+ static_cast<_Float16>(188), static_cast<_Float16>(190),
+ static_cast<_Float16>(192), static_cast<_Float16>(194),
+ static_cast<_Float16>(196), static_cast<_Float16>(198),
+ static_cast<_Float16>(200), static_cast<_Float16>(202),
+ static_cast<_Float16>(204), static_cast<_Float16>(206),
+ static_cast<_Float16>(208), static_cast<_Float16>(210),
+ static_cast<_Float16>(212), static_cast<_Float16>(214),
+ static_cast<_Float16>(216), static_cast<_Float16>(218),
+ static_cast<_Float16>(220), static_cast<_Float16>(222),
+ static_cast<_Float16>(224), static_cast<_Float16>(226),
+ static_cast<_Float16>(228), static_cast<_Float16>(230),
+ static_cast<_Float16>(232), static_cast<_Float16>(234),
+ static_cast<_Float16>(236), static_cast<_Float16>(238)};
nntrainer::Tensor answer(ref_dim, answer_data);
int status = t.multiply_i(m);
EXPECT_EQ(status, ML_ERROR_NONE);
ranged(3, 5, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
nntrainer::Tensor m =
ranged(3, 1, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
- _Float16 answer_data[] = {static_cast<_Float16>(0), static_cast<_Float16>(1),
- static_cast<_Float16>(4), static_cast<_Float16>(9),
- static_cast<_Float16>(0), static_cast<_Float16>(5),
- static_cast<_Float16>(12), static_cast<_Float16>(21),
- static_cast<_Float16>(0), static_cast<_Float16>(9),
- static_cast<_Float16>(20), static_cast<_Float16>(33),
- static_cast<_Float16>(0), static_cast<_Float16>(13),
- static_cast<_Float16>(28), static_cast<_Float16>(45),
- static_cast<_Float16>(0), static_cast<_Float16>(17),
- static_cast<_Float16>(36), static_cast<_Float16>(57),
- static_cast<_Float16>(80), static_cast<_Float16>(105),
- static_cast<_Float16>(132), static_cast<_Float16>(161),
- static_cast<_Float16>(96), static_cast<_Float16>(125),
- static_cast<_Float16>(156), static_cast<_Float16>(189),
- static_cast<_Float16>(112), static_cast<_Float16>(145),
- static_cast<_Float16>(180), static_cast<_Float16>(217),
- static_cast<_Float16>(128), static_cast<_Float16>(165),
- static_cast<_Float16>(204), static_cast<_Float16>(245),
- static_cast<_Float16>(144), static_cast<_Float16>(185),
- static_cast<_Float16>(228), static_cast<_Float16>(273),
- static_cast<_Float16>(320), static_cast<_Float16>(369),
- static_cast<_Float16>(420), static_cast<_Float16>(473),
- static_cast<_Float16>(352), static_cast<_Float16>(405),
- static_cast<_Float16>(460), static_cast<_Float16>(517),
- static_cast<_Float16>(384), static_cast<_Float16>(441),
- static_cast<_Float16>(500), static_cast<_Float16>(561),
- static_cast<_Float16>(416), static_cast<_Float16>(477),
- static_cast<_Float16>(540), static_cast<_Float16>(605),
- static_cast<_Float16>(448), static_cast<_Float16>(513),
- static_cast<_Float16>(580), static_cast<_Float16>(649)};
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(4), static_cast<_Float16>(9),
+ static_cast<_Float16>(0), static_cast<_Float16>(5),
+ static_cast<_Float16>(12), static_cast<_Float16>(21),
+ static_cast<_Float16>(0), static_cast<_Float16>(9),
+ static_cast<_Float16>(20), static_cast<_Float16>(33),
+ static_cast<_Float16>(0), static_cast<_Float16>(13),
+ static_cast<_Float16>(28), static_cast<_Float16>(45),
+ static_cast<_Float16>(0), static_cast<_Float16>(17),
+ static_cast<_Float16>(36), static_cast<_Float16>(57),
+ static_cast<_Float16>(80), static_cast<_Float16>(105),
+ static_cast<_Float16>(132), static_cast<_Float16>(161),
+ static_cast<_Float16>(96), static_cast<_Float16>(125),
+ static_cast<_Float16>(156), static_cast<_Float16>(189),
+ static_cast<_Float16>(112), static_cast<_Float16>(145),
+ static_cast<_Float16>(180), static_cast<_Float16>(217),
+ static_cast<_Float16>(128), static_cast<_Float16>(165),
+ static_cast<_Float16>(204), static_cast<_Float16>(245),
+ static_cast<_Float16>(144), static_cast<_Float16>(185),
+ static_cast<_Float16>(228), static_cast<_Float16>(273),
+ static_cast<_Float16>(320), static_cast<_Float16>(369),
+ static_cast<_Float16>(420), static_cast<_Float16>(473),
+ static_cast<_Float16>(352), static_cast<_Float16>(405),
+ static_cast<_Float16>(460), static_cast<_Float16>(517),
+ static_cast<_Float16>(384), static_cast<_Float16>(441),
+ static_cast<_Float16>(500), static_cast<_Float16>(561),
+ static_cast<_Float16>(416), static_cast<_Float16>(477),
+ static_cast<_Float16>(540), static_cast<_Float16>(605),
+ static_cast<_Float16>(448), static_cast<_Float16>(513),
+ static_cast<_Float16>(580), static_cast<_Float16>(649)};
nntrainer::Tensor answer(ref_dim, answer_data);
int status = t.multiply_i(m);
EXPECT_EQ(status, ML_ERROR_NONE);
}
}
-// TEST(nntrainer_Tensor, multiply_i_broadcast_not_supported_01_n) {
-
-// nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-
-// EXPECT_EQ(target.multiply_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+TEST(nntrainer_Tensor, multiply_i_broadcast_not_supported_01_n) {
-// TEST(nntrainer_Tensor, multiply_i_broadcast_not_broadcastable_02_n) {
-// nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(target.multiply_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+ EXPECT_EQ(target.multiply_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// TEST(nntrainer_Tensor, multiply_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, multiply_i_broadcast_not_broadcastable_02_n) {
+ nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+ EXPECT_EQ(target.multiply_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// nntrainer::Tensor result = input.multiply(0.0);
-// if (result.getValue<_Float16>(0, 0, 1, 1) != 0.0)
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+TEST(nntrainer_Tensor, multiply_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// TEST(nntrainer_Tensor, multiply_02_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor result = input.multiply(0.0);
+ if (result.getValue<_Float16>(0, 0, 1, 1) != 0.0)
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// nntrainer::Tensor result = input.multiply(input);
+TEST(nntrainer_Tensor, multiply_02_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// _Float16 *data = result.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// for (int i = 0; i < batch * height * width; ++i) {
-// if (data[i] != indata[i] * indata[i]) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// break;
-// }
-// }
+ nntrainer::Tensor result = input.multiply(input);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+ _Float16 *data = result.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// TEST(nntrainer_Tensor, multiply_03_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ for (int i = 0; i < batch * height * width; ++i) {
+ if (data[i] != indata[i] * indata[i]) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ break;
+ }
+ }
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// nntrainer::Tensor test(batch - 1, height - 1, width - 1,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, multiply_03_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW({ input.multiply(test); }, std::invalid_argument);
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, multiply_04_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor test(batch - 1, height - 1, width - 1,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW({ input.multiply(test); }, std::invalid_argument);
+}
-// nntrainer::Tensor input(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
-// nntrainer::Tensor test(dim);
+TEST(nntrainer_Tensor, multiply_04_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(shared_input.multiply(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, multiply_05_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
+ nntrainer::Tensor test(dim);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(shared_input.multiply(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// nntrainer::Tensor test(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
+TEST(nntrainer_Tensor, multiply_05_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.multiply(shared_test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, multiply_06_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ nntrainer::Tensor test(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(input.multiply(shared_test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim, false);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
+TEST(nntrainer_Tensor, multiply_06_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.multiply(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, multiply_07_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim, false);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(input.multiply(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim, false);
+TEST(nntrainer_Tensor, multiply_07_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.multiply(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, multiply_08_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim, false);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(input.multiply(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
-// nntrainer::Tensor output(dim, false);
+TEST(nntrainer_Tensor, multiply_08_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.multiply(test, output), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, multiply__Float16_01_p) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
+ nntrainer::Tensor output(dim, false);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ EXPECT_THROW(input.multiply(test, output), std::invalid_argument);
+}
-// nntrainer::Tensor expected(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(expected, (i * (batch * height) + j * (width) + k + 1) * 2);
+TEST(nntrainer_Tensor, multiply__Float16_01_p) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor result = input.multiply(2.0);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// EXPECT_EQ(result, expected);
-// }
+ nntrainer::Tensor expected(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(expected, (i * (batch * height) + j * (width) + k + 1) * 2);
-// TEST(nntrainer_Tensor, divide_i_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor result = input.multiply(2.0);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+ EXPECT_EQ(result, expected);
+}
-// nntrainer::Tensor original;
-// original.copy(input);
+TEST(nntrainer_Tensor, divide_i_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// status = input.divide_i((_Float16)2.0);
-// EXPECT_EQ(status, ML_ERROR_NONE);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// _Float16 *data = original.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+ nntrainer::Tensor original;
+ original.copy(input);
-// for (int i = 0; i < batch * height * width * channel; ++i) {
-// EXPECT_FLOAT_EQ(data[i], indata[i] + indata[i]);
-// }
-// }
+ status = input.divide_i(2.0f);
+ EXPECT_EQ(status, ML_ERROR_NONE);
-// TEST(nntrainer_Tensor, divide_i_02_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ _Float16 *data = original.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ for (int i = 0; i < batch * height * width * channel; ++i) {
+ EXPECT_FLOAT_EQ(data[i], indata[i] + indata[i]);
+ }
+}
-// status = input.divide_i(input);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+TEST(nntrainer_Tensor, divide_i_02_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// for (int i = 0; i < batch * height * width * channel; ++i) {
-// EXPECT_FLOAT_EQ(indata[i], _Float16(1.0));
-// }
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, divide_i_01_n) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ status = input.divide_i(input);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+ for (int i = 0; i < batch * height * width * channel; ++i) {
+ EXPECT_FLOAT_EQ(indata[i], _Float16(1.0));
+ }
+}
-// status = input.divide_i((_Float16)0);
-// EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-// }
+TEST(nntrainer_Tensor, divide_i_01_n) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// TEST(nntrainer_Tensor, divide_i_02_n) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+ status = input.divide_i((_Float16)0);
+ EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
-// nntrainer::Tensor original(batch, channel, height - 2, width - 1,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, divide_i_02_n) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// status = input.divide_i(original);
-// EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// TEST(nntrainer_Tensor, divide_01_p) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor original(batch, channel, height - 2, width - 1,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ status = input.divide_i(original);
+ EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
-// nntrainer::Tensor result = input.divide(1.0);
+TEST(nntrainer_Tensor, divide_01_p) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// _Float16 *previous = input.getData<_Float16>();
-// ASSERT_NE(nullptr, previous);
-// _Float16 *data = result.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// for (int i = 0; i < batch * height * width * channel; ++i) {
-// EXPECT_FLOAT_EQ(data[i], previous[i]);
-// }
-// }
+ nntrainer::Tensor result = input.divide(1.0);
-// TEST(nntrainer_Tensor, divide_02_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ _Float16 *previous = input.getData<_Float16>();
+ ASSERT_NE(nullptr, previous);
+ _Float16 *data = result.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ for (int i = 0; i < batch * height * width * channel; ++i) {
+ EXPECT_FLOAT_EQ(data[i], previous[i]);
+ }
+}
-// EXPECT_THROW({ input.divide(0.0); }, std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, divide_02_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// TEST(nntrainer_Tensor, divide_03_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ EXPECT_THROW({ input.divide(0.0); }, std::invalid_argument);
+}
-// nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, divide_03_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW({ input.divide(test); }, std::invalid_argument);
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, divide_04_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW({ input.divide(test); }, std::invalid_argument);
+}
-// nntrainer::Tensor input(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
-// nntrainer::Tensor test(dim);
+TEST(nntrainer_Tensor, divide_04_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(shared_input.divide(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, divide_05_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
+ nntrainer::Tensor test(dim);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(shared_input.divide(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// nntrainer::Tensor test(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
+TEST(nntrainer_Tensor, divide_05_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.divide(shared_test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, divide_06_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ nntrainer::Tensor test(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(input.divide(shared_test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim, false);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
+TEST(nntrainer_Tensor, divide_06_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.divide(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, divide_07_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim, false);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::TensorDim dim(batch, channel, height, width);
+ EXPECT_THROW(input.divide(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim, false);
+TEST(nntrainer_Tensor, divide_07_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.divide(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width);
-// TEST(nntrainer_Tensor, divide_08_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim, false);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(input.divide(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
-// nntrainer::Tensor output(dim, false);
+TEST(nntrainer_Tensor, divide_08_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.divide(test, output), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, divide_i_broadcast_01_p) {
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(1, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 41.0, 21.0,
-// 14.333333, 11.0, 9.0, 7.6666665, 6.714286, 6.0,
-// 5.4444447, 5.0, 4.6363635, 4.3333335, 4.076923, 3.857143,
-// 3.6666667, 3.5, 3.3529413, 3.2222223, 3.1052632, 3.0,
-// 2.9047618, 2.8181818, 2.7391305, 2.6666667, 2.6, 2.5384614,
-// 2.4814816, 2.4285715, 2.3793104, 2.3333333, 2.2903225, 2.25,
-// 2.2121212, 2.1764705, 2.142857, 2.1111112, 2.0810812, 2.0526316,
-// 2.025641, 2.0, 81.0, 41.0, 27.666666, 21.0,
-// 17.0, 14.333333, 12.428572, 11.0, 9.888889, 9.0,
-// 8.272727, 7.6666665, 7.1538463, 6.714286, 6.3333335, 6.0,
-// 5.7058825, 5.4444447, 5.2105265, 5.0, 4.8095236, 4.6363635,
-// 4.478261, 4.3333335, 4.2, 4.076923, 3.9629629, 3.857143,
-// 3.7586207, 3.6666667, 3.580645, 3.5, 3.4242425, 3.3529413,
-// 3.2857144, 3.2222223, 3.162162, 3.1052632, 3.0512822, 3.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 1, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
-// 1.0, 1.0, 21.0, 11.0, 7.6666665, 6.0,
-// 5.0, 4.3333335, 3.857143, 3.5, 3.2222223, 3.0,
-// 2.8181818, 2.6666667, 2.5384614, 2.4285715, 2.3333333, 2.25,
-// 2.1764705, 2.1111112, 2.0526316, 2.0, 1.9523809, 1.9090909,
-// 1.8695652, 1.8333334, 1.8, 1.7692307, 1.7407408, 1.7142857,
-// 1.6896552, 1.6666666, 1.6451613, 1.625, 1.6060606, 1.5882353,
-// 1.5714285, 1.5555556, 1.5405406, 1.5263158, 1.5128205, 1.5,
-// 2.9047618, 2.8181818, 2.7391305, 2.6666667, 2.6, 2.5384614,
-// 2.4814816, 2.4285715, 2.3793104, 2.3333333, 2.2903225, 2.25,
-// 2.2121212, 2.1764705, 2.142857, 2.1111112, 2.0810812, 2.0526316,
-// 2.025641, 2.0, 1.9756098, 1.9523809, 1.9302325, 1.9090909,
-// 1.8888888, 1.8695652, 1.8510638, 1.8333334, 1.8163265, 1.8,
-// 1.7843137, 1.7692307, 1.754717, 1.7407408, 1.7272727, 1.7142857,
-// 1.7017543, 1.6896552, 1.6779661, 1.6666666, 2.4634147, 2.4285715,
-// 2.3953488, 2.3636363, 2.3333333, 2.3043478, 2.2765958, 2.25,
-// 2.2244897, 2.2, 2.1764705, 2.1538463, 2.1320755, 2.1111112,
-// 2.090909, 2.0714285, 2.0526316, 2.0344827, 2.0169492, 2.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 2.0, 3.0, 4.0, 5.0, 3.0,
-// 3.5, 4.0, 4.5, 5.0, 3.6666667, 4.0,
-// 4.3333335, 4.6666665, 5.0, 4.0, 4.25, 4.5,
-// 4.75, 5.0, 4.2, 4.4, 4.6, 4.8,
-// 5.0, 4.3333335, 4.5, 4.6666665, 4.8333335, 5.0,
-// 4.428571, 4.571429, 4.714286, 4.857143, 5.0, 4.5,
-// 4.625, 4.75, 4.875, 5.0, 4.5555553, 4.6666665,
-// 4.7777777, 4.888889, 5.0, 4.6, 4.7, 4.8,
-// 4.9, 5.0, 4.6363635, 4.7272725, 4.818182, 4.909091,
-// 5.0, 4.6666665, 4.75, 4.8333335, 4.9166665, 5.0,
-// 4.6923075, 4.769231, 4.8461537, 4.923077, 5.0, 4.714286,
-// 4.785714, 4.857143, 4.928571, 5.0, 4.733333, 4.8,
-// 4.866667, 4.9333334, 5.0, 4.75, 4.8125, 4.875,
-// 4.9375, 5.0, 4.7647057, 4.8235292, 4.882353, 4.9411764,
-// 5.0, 4.7777777, 4.8333335, 4.888889, 4.9444447, 5.0,
-// 4.7894735, 4.8421054, 4.894737, 4.9473686, 5.0, 4.8,
-// 4.85, 4.9, 4.95, 5.0, 4.8095236, 4.857143,
-// 4.904762, 4.952381, 5.0, 4.818182, 4.8636365, 4.909091,
-// 4.9545455, 5.0, 4.826087, 4.869565, 4.9130435, 4.9565215,
-// 5.0, 4.8333335, 4.875, 4.9166665, 4.9583335, 5.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 1.0, 6.0,
-// 3.5, 2.6666667, 2.25, 2.0, 11.0, 6.0,
-// 4.3333335, 3.5, 3.0, 16.0, 8.5, 6.0,
-// 4.75, 4.0, 21.0, 11.0, 7.6666665, 6.0,
-// 5.0, 26.0, 13.5, 9.333333, 7.25, 6.0,
-// 31.0, 16.0, 11.0, 8.5, 7.0, 36.0,
-// 18.5, 12.666667, 9.75, 8.0, 6.8333335, 6.0,
-// 5.375, 4.888889, 4.5, 7.6666665, 6.714286, 6.0,
-// 5.4444447, 5.0, 8.5, 7.428571, 6.625, 6.0,
-// 5.5, 9.333333, 8.142858, 7.25, 6.5555553, 6.0,
-// 10.166667, 8.857142, 7.875, 7.111111, 6.5, 11.0,
-// 9.571428, 8.5, 7.6666665, 7.0, 11.833333, 10.285714,
-// 9.125, 8.222222, 7.5, 12.666667, 11.0, 9.75,
-// 8.777778, 8.0, 7.3636365, 6.8333335, 6.3846154, 6.0,
-// 5.6666665, 7.818182, 7.25, 6.769231, 6.357143, 6.0,
-// 8.272727, 7.6666665, 7.1538463, 6.714286, 6.3333335, 8.727273,
-// 8.083333, 7.5384617, 7.071429, 6.6666665, 9.181818, 8.5,
-// 7.923077, 7.428571, 7.0, 9.636364, 8.916667, 8.307693,
-// 7.785714, 7.3333335, 10.090909, 9.333333, 8.692307, 8.142858,
-// 7.6666665, 10.545455, 9.75, 9.076923, 8.5, 8.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(1, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 1.0, 6.0,
-// 3.5, 2.6666667, 2.25, 2.0, 11.0, 6.0,
-// 4.3333335, 3.5, 3.0, 16.0, 8.5, 6.0,
-// 4.75, 4.0, 3.5, 3.142857, 2.875, 2.6666667,
-// 2.5, 4.3333335, 3.857143, 3.5, 3.2222223, 3.0,
-// 5.1666665, 4.571429, 4.125, 3.7777777, 3.5, 6.0,
-// 5.285714, 4.75, 4.3333335, 4.0, 41.0, 21.0,
-// 14.333333, 11.0, 9.0, 46.0, 23.5, 16.0,
-// 12.25, 10.0, 51.0, 26.0, 17.666666, 13.5,
-// 11.0, 56.0, 28.5, 19.333334, 14.75, 12.0,
-// 10.166667, 8.857142, 7.875, 7.111111, 6.5, 11.0,
-// 9.571428, 8.5, 7.6666665, 7.0, 11.833333, 10.285714,
-// 9.125, 8.222222, 7.5, 12.666667, 11.0, 9.75,
-// 8.777778, 8.0, 81.0, 41.0, 27.666666, 21.0,
-// 17.0, 86.0, 43.5, 29.333334, 22.25, 18.0,
-// 91.0, 46.0, 31.0, 23.5, 19.0, 96.0,
-// 48.5, 32.666668, 24.75, 20.0, 16.833334, 14.571428,
-// 12.875, 11.555555, 10.5, 17.666666, 15.285714, 13.5,
-// 12.111111, 11.0, 18.5, 16.0, 14.125, 12.666667,
-// 11.5, 19.333334, 16.714285, 14.75, 13.222222, 12.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 1, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 2.0, 3.0, 4.0, 5.0, 3.0,
-// 3.5, 4.0, 4.5, 5.0, 3.6666667, 4.0,
-// 4.3333335, 4.6666665, 5.0, 4.0, 4.25, 4.5,
-// 4.75, 5.0, 21.0, 22.0, 23.0, 24.0,
-// 25.0, 13.0, 13.5, 14.0, 14.5, 15.0,
-// 10.333333, 10.666667, 11.0, 11.333333, 11.666667, 9.0,
-// 9.25, 9.5, 9.75, 10.0, 8.2, 8.4,
-// 8.6, 8.8, 9.0, 7.6666665, 7.8333335, 8.0,
-// 8.166667, 8.333333, 7.285714, 7.428571, 7.571429, 7.714286,
-// 7.857143, 7.0, 7.125, 7.25, 7.375, 7.5,
-// 12.2, 12.4, 12.6, 12.8, 13.0, 11.0,
-// 11.166667, 11.333333, 11.5, 11.666667, 10.142858, 10.285714,
-// 10.428572, 10.571428, 10.714286, 9.5, 9.625, 9.75,
-// 9.875, 10.0, 9.0, 9.111111, 9.222222, 9.333333,
-// 9.444445, 8.6, 8.7, 8.8, 8.9, 9.0,
-// 8.272727, 8.363636, 8.454545, 8.545455, 8.636364, 8.0,
-// 8.083333, 8.166667, 8.25, 8.333333, 11.222222, 11.333333,
-// 11.444445, 11.555555, 11.666667, 10.6, 10.7, 10.8,
-// 10.9, 11.0, 10.090909, 10.181818, 10.272727, 10.363636,
-// 10.454545, 9.666667, 9.75, 9.833333, 9.916667, 10.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(1, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 1.0, 6.0, 3.5, 2.6666667, 2.25, 2.0,
-// 11.0, 6.0, 4.3333335, 3.5, 3.0, 16.0, 8.5, 6.0, 4.75, 4.0,
-// 21.0, 11.0, 7.6666665, 6.0, 5.0, 26.0, 13.5, 9.333333, 7.25, 6.0,
-// 31.0, 16.0, 11.0, 8.5, 7.0, 36.0, 18.5, 12.666667, 9.75, 8.0,
-// 41.0, 21.0, 14.333333, 11.0, 9.0, 46.0, 23.5, 16.0, 12.25, 10.0,
-// 51.0, 26.0, 17.666666, 13.5, 11.0, 56.0, 28.5, 19.333334, 14.75, 12.0,
-// 61.0, 31.0, 21.0, 16.0, 13.0, 66.0, 33.5, 22.666666, 17.25, 14.0,
-// 71.0, 36.0, 24.333334, 18.5, 15.0, 76.0, 38.5, 26.0, 19.75, 16.0,
-// 81.0, 41.0, 27.666666, 21.0, 17.0, 86.0, 43.5, 29.333334, 22.25, 18.0,
-// 91.0, 46.0, 31.0, 23.5, 19.0, 96.0, 48.5, 32.666668, 24.75, 20.0,
-// 101.0, 51.0, 34.333332, 26.0, 21.0, 106.0, 53.5, 36.0, 27.25, 22.0,
-// 111.0, 56.0, 37.666668, 28.5, 23.0, 116.0, 58.5, 39.333332, 29.75, 24.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(1, 2, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
-// 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 10.5, 11.0, 11.5, 12.0,
-// 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0,
-// 18.5, 19.0, 19.5, 20.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0,
-// 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0,
-// 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0,
-// 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 81.0, 82.0, 83.0, 84.0,
-// 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0,
-// 97.0, 98.0, 99.0, 100.0, 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0,
-// 54.5, 55.0, 55.5, 56.0, 56.5, 57.0, 57.5, 58.0, 58.5, 59.0, 59.5, 60.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
-// 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
-// 13.0, 14.0, 15.0, 16.0, 17.0, 18.0,
-// 19.0, 20.0, 21.0, 22.0, 23.0, 24.0,
-// 25.0, 26.0, 27.0, 28.0, 29.0, 30.0,
-// 31.0, 32.0, 33.0, 34.0, 35.0, 36.0,
-// 37.0, 38.0, 39.0, 40.0, 20.5, 21.0,
-// 21.5, 22.0, 22.5, 23.0, 23.5, 24.0,
-// 24.5, 25.0, 25.5, 26.0, 26.5, 27.0,
-// 27.5, 28.0, 28.5, 29.0, 29.5, 30.0,
-// 30.5, 31.0, 31.5, 32.0, 32.5, 33.0,
-// 33.5, 34.0, 34.5, 35.0, 35.5, 36.0,
-// 36.5, 37.0, 37.5, 38.0, 38.5, 39.0,
-// 39.5, 40.0, 27.0, 27.333334, 27.666666, 28.0,
-// 28.333334, 28.666666, 29.0, 29.333334, 29.666666, 30.0,
-// 30.333334, 30.666666, 31.0, 31.333334, 31.666666, 32.0,
-// 32.333332, 32.666668, 33.0, 33.333332, 33.666668, 34.0,
-// 34.333332, 34.666668, 35.0, 35.333332, 35.666668, 36.0,
-// 36.333332, 36.666668, 37.0, 37.333332, 37.666668, 38.0,
-// 38.333332, 38.666668, 39.0, 39.333332, 39.666668, 40.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 5, 1, 4, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 5, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// t.add_i(1);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1);
-// _Float16 answer_data[] = {
-// 1.0, 1.0, 1.0, 1.0, 5.0, 3.0,
-// 2.3333333, 2.0, 9.0, 5.0, 3.6666667, 3.0,
-// 13.0, 7.0, 5.0, 4.0, 17.0, 9.0,
-// 6.3333335, 5.0, 4.2, 3.6666667, 3.2857144, 3.0,
-// 5.0, 4.3333335, 3.857143, 3.5, 5.8, 5.0,
-// 4.428571, 4.0, 6.6, 5.6666665, 5.0, 4.5,
-// 7.4, 6.3333335, 5.571429, 5.0, 4.5555553, 4.2,
-// 3.909091, 3.6666667, 5.0, 4.6, 4.2727275, 4.0,
-// 5.4444447, 5.0, 4.6363635, 4.3333335, 5.888889, 5.4,
-// 5.0, 4.6666665, 6.3333335, 5.8, 5.3636365, 5.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.divide_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// }
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
+ nntrainer::Tensor output(dim, false);
-// TEST(nntrainer_Tensor, divide_i_broadcast_not_supported_01_n) {
-// nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(input.divide(test, output), std::invalid_argument);
+}
-// EXPECT_EQ(target.divide_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+TEST(nntrainer_Tensor, divide_i_broadcast_01_p) {
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(1, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(41.0), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(14.333333), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(7.6666665),
+ static_cast<_Float16>(6.714286), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.4444447), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.6363635), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(4.076923), static_cast<_Float16>(3.857143),
+ static_cast<_Float16>(3.6666667), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.3529413), static_cast<_Float16>(3.2222223),
+ static_cast<_Float16>(3.1052632), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(2.9047618), static_cast<_Float16>(2.8181818),
+ static_cast<_Float16>(2.7391305), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.6), static_cast<_Float16>(2.5384614),
+ static_cast<_Float16>(2.4814816), static_cast<_Float16>(2.4285715),
+ static_cast<_Float16>(2.3793104), static_cast<_Float16>(2.3333333),
+ static_cast<_Float16>(2.2903225), static_cast<_Float16>(2.25),
+ static_cast<_Float16>(2.2121212), static_cast<_Float16>(2.1764705),
+ static_cast<_Float16>(2.142857), static_cast<_Float16>(2.1111112),
+ static_cast<_Float16>(2.0810812), static_cast<_Float16>(2.0526316),
+ static_cast<_Float16>(2.025641), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(81.0), static_cast<_Float16>(41.0),
+ static_cast<_Float16>(27.666666), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(14.333333),
+ static_cast<_Float16>(12.428572), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.888889), static_cast<_Float16>(9.0),
+ static_cast<_Float16>(8.272727), static_cast<_Float16>(7.6666665),
+ static_cast<_Float16>(7.1538463), static_cast<_Float16>(6.714286),
+ static_cast<_Float16>(6.3333335), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.7058825), static_cast<_Float16>(5.4444447),
+ static_cast<_Float16>(5.2105265), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.8095236), static_cast<_Float16>(4.6363635),
+ static_cast<_Float16>(4.478261), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(4.2), static_cast<_Float16>(4.076923),
+ static_cast<_Float16>(3.9629629), static_cast<_Float16>(3.857143),
+ static_cast<_Float16>(3.7586207), static_cast<_Float16>(3.6666667),
+ static_cast<_Float16>(3.580645), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.4242425), static_cast<_Float16>(3.3529413),
+ static_cast<_Float16>(3.2857144), static_cast<_Float16>(3.2222223),
+ static_cast<_Float16>(3.162162), static_cast<_Float16>(3.1052632),
+ static_cast<_Float16>(3.0512822), static_cast<_Float16>(3.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 1, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(3.857143), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.2222223), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(2.8181818), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.5384614), static_cast<_Float16>(2.4285715),
+ static_cast<_Float16>(2.3333333), static_cast<_Float16>(2.25),
+ static_cast<_Float16>(2.1764705), static_cast<_Float16>(2.1111112),
+ static_cast<_Float16>(2.0526316), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(1.9523809), static_cast<_Float16>(1.9090909),
+ static_cast<_Float16>(1.8695652), static_cast<_Float16>(1.8333334),
+ static_cast<_Float16>(1.8), static_cast<_Float16>(1.7692307),
+ static_cast<_Float16>(1.7407408), static_cast<_Float16>(1.7142857),
+ static_cast<_Float16>(1.6896552), static_cast<_Float16>(1.6666666),
+ static_cast<_Float16>(1.6451613), static_cast<_Float16>(1.625),
+ static_cast<_Float16>(1.6060606), static_cast<_Float16>(1.5882353),
+ static_cast<_Float16>(1.5714285), static_cast<_Float16>(1.5555556),
+ static_cast<_Float16>(1.5405406), static_cast<_Float16>(1.5263158),
+ static_cast<_Float16>(1.5128205), static_cast<_Float16>(1.5),
+ static_cast<_Float16>(2.9047618), static_cast<_Float16>(2.8181818),
+ static_cast<_Float16>(2.7391305), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.6), static_cast<_Float16>(2.5384614),
+ static_cast<_Float16>(2.4814816), static_cast<_Float16>(2.4285715),
+ static_cast<_Float16>(2.3793104), static_cast<_Float16>(2.3333333),
+ static_cast<_Float16>(2.2903225), static_cast<_Float16>(2.25),
+ static_cast<_Float16>(2.2121212), static_cast<_Float16>(2.1764705),
+ static_cast<_Float16>(2.142857), static_cast<_Float16>(2.1111112),
+ static_cast<_Float16>(2.0810812), static_cast<_Float16>(2.0526316),
+ static_cast<_Float16>(2.025641), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(1.9756098), static_cast<_Float16>(1.9523809),
+ static_cast<_Float16>(1.9302325), static_cast<_Float16>(1.9090909),
+ static_cast<_Float16>(1.8888888), static_cast<_Float16>(1.8695652),
+ static_cast<_Float16>(1.8510638), static_cast<_Float16>(1.8333334),
+ static_cast<_Float16>(1.8163265), static_cast<_Float16>(1.8),
+ static_cast<_Float16>(1.7843137), static_cast<_Float16>(1.7692307),
+ static_cast<_Float16>(1.754717), static_cast<_Float16>(1.7407408),
+ static_cast<_Float16>(1.7272727), static_cast<_Float16>(1.7142857),
+ static_cast<_Float16>(1.7017543), static_cast<_Float16>(1.6896552),
+ static_cast<_Float16>(1.6779661), static_cast<_Float16>(1.6666666),
+ static_cast<_Float16>(2.4634147), static_cast<_Float16>(2.4285715),
+ static_cast<_Float16>(2.3953488), static_cast<_Float16>(2.3636363),
+ static_cast<_Float16>(2.3333333), static_cast<_Float16>(2.3043478),
+ static_cast<_Float16>(2.2765958), static_cast<_Float16>(2.25),
+ static_cast<_Float16>(2.2244897), static_cast<_Float16>(2.2),
+ static_cast<_Float16>(2.1764705), static_cast<_Float16>(2.1538463),
+ static_cast<_Float16>(2.1320755), static_cast<_Float16>(2.1111112),
+ static_cast<_Float16>(2.090909), static_cast<_Float16>(2.0714285),
+ static_cast<_Float16>(2.0526316), static_cast<_Float16>(2.0344827),
+ static_cast<_Float16>(2.0169492), static_cast<_Float16>(2.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.5), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(3.6666667), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.25), static_cast<_Float16>(4.5),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.2), static_cast<_Float16>(4.4),
+ static_cast<_Float16>(4.6), static_cast<_Float16>(4.8),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(4.5), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(4.8333335), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.428571), static_cast<_Float16>(4.571429),
+ static_cast<_Float16>(4.714286), static_cast<_Float16>(4.857143),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.5),
+ static_cast<_Float16>(4.625), static_cast<_Float16>(4.75),
+ static_cast<_Float16>(4.875), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.5555553), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(4.7777777), static_cast<_Float16>(4.888889),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.6),
+ static_cast<_Float16>(4.7), static_cast<_Float16>(4.8),
+ static_cast<_Float16>(4.9), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.6363635), static_cast<_Float16>(4.7272725),
+ static_cast<_Float16>(4.818182), static_cast<_Float16>(4.909091),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(4.8333335),
+ static_cast<_Float16>(4.9166665), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.6923075), static_cast<_Float16>(4.769231),
+ static_cast<_Float16>(4.8461537), static_cast<_Float16>(4.923077),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.714286),
+ static_cast<_Float16>(4.785714), static_cast<_Float16>(4.857143),
+ static_cast<_Float16>(4.928571), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.733333), static_cast<_Float16>(4.8),
+ static_cast<_Float16>(4.866667), static_cast<_Float16>(4.9333334),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.75),
+ static_cast<_Float16>(4.8125), static_cast<_Float16>(4.875),
+ static_cast<_Float16>(4.9375), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.7647057), static_cast<_Float16>(4.8235292),
+ static_cast<_Float16>(4.882353), static_cast<_Float16>(4.9411764),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.7777777),
+ static_cast<_Float16>(4.8333335), static_cast<_Float16>(4.888889),
+ static_cast<_Float16>(4.9444447), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.7894735), static_cast<_Float16>(4.8421054),
+ static_cast<_Float16>(4.894737), static_cast<_Float16>(4.9473686),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.8),
+ static_cast<_Float16>(4.85), static_cast<_Float16>(4.9),
+ static_cast<_Float16>(4.95), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.8095236), static_cast<_Float16>(4.857143),
+ static_cast<_Float16>(4.904762), static_cast<_Float16>(4.952381),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.818182),
+ static_cast<_Float16>(4.8636365), static_cast<_Float16>(4.909091),
+ static_cast<_Float16>(4.9545455), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.826087), static_cast<_Float16>(4.869565),
+ static_cast<_Float16>(4.9130435), static_cast<_Float16>(4.9565215),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.8333335),
+ static_cast<_Float16>(4.875), static_cast<_Float16>(4.9166665),
+ static_cast<_Float16>(4.9583335), static_cast<_Float16>(5.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.25), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(8.5), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(13.5), static_cast<_Float16>(9.333333),
+ static_cast<_Float16>(7.25), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(8.5),
+ static_cast<_Float16>(7.0), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(18.5), static_cast<_Float16>(12.666667),
+ static_cast<_Float16>(9.75), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(6.8333335), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.375), static_cast<_Float16>(4.888889),
+ static_cast<_Float16>(4.5), static_cast<_Float16>(7.6666665),
+ static_cast<_Float16>(6.714286), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.4444447), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(8.5), static_cast<_Float16>(7.428571),
+ static_cast<_Float16>(6.625), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.5), static_cast<_Float16>(9.333333),
+ static_cast<_Float16>(8.142858), static_cast<_Float16>(7.25),
+ static_cast<_Float16>(6.5555553), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(10.166667), static_cast<_Float16>(8.857142),
+ static_cast<_Float16>(7.875), static_cast<_Float16>(7.111111),
+ static_cast<_Float16>(6.5), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.571428), static_cast<_Float16>(8.5),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(7.0),
+ static_cast<_Float16>(11.833333), static_cast<_Float16>(10.285714),
+ static_cast<_Float16>(9.125), static_cast<_Float16>(8.222222),
+ static_cast<_Float16>(7.5), static_cast<_Float16>(12.666667),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(9.75),
+ static_cast<_Float16>(8.777778), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(7.3636365), static_cast<_Float16>(6.8333335),
+ static_cast<_Float16>(6.3846154), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.6666665), static_cast<_Float16>(7.818182),
+ static_cast<_Float16>(7.25), static_cast<_Float16>(6.769231),
+ static_cast<_Float16>(6.357143), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(8.272727), static_cast<_Float16>(7.6666665),
+ static_cast<_Float16>(7.1538463), static_cast<_Float16>(6.714286),
+ static_cast<_Float16>(6.3333335), static_cast<_Float16>(8.727273),
+ static_cast<_Float16>(8.083333), static_cast<_Float16>(7.5384617),
+ static_cast<_Float16>(7.071429), static_cast<_Float16>(6.6666665),
+ static_cast<_Float16>(9.181818), static_cast<_Float16>(8.5),
+ static_cast<_Float16>(7.923077), static_cast<_Float16>(7.428571),
+ static_cast<_Float16>(7.0), static_cast<_Float16>(9.636364),
+ static_cast<_Float16>(8.916667), static_cast<_Float16>(8.307693),
+ static_cast<_Float16>(7.785714), static_cast<_Float16>(7.3333335),
+ static_cast<_Float16>(10.090909), static_cast<_Float16>(9.333333),
+ static_cast<_Float16>(8.692307), static_cast<_Float16>(8.142858),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(10.545455),
+ static_cast<_Float16>(9.75), static_cast<_Float16>(9.076923),
+ static_cast<_Float16>(8.5), static_cast<_Float16>(8.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(1, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.25), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(8.5), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(3.142857),
+ static_cast<_Float16>(2.875), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.5), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(3.857143), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.2222223), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(5.1666665), static_cast<_Float16>(4.571429),
+ static_cast<_Float16>(4.125), static_cast<_Float16>(3.7777777),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.285714), static_cast<_Float16>(4.75),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(41.0), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(14.333333), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(46.0),
+ static_cast<_Float16>(23.5), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(12.25), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(51.0), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(17.666666), static_cast<_Float16>(13.5),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(56.0),
+ static_cast<_Float16>(28.5), static_cast<_Float16>(19.333334),
+ static_cast<_Float16>(14.75), static_cast<_Float16>(12.0),
+ static_cast<_Float16>(10.166667), static_cast<_Float16>(8.857142),
+ static_cast<_Float16>(7.875), static_cast<_Float16>(7.111111),
+ static_cast<_Float16>(6.5), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.571428), static_cast<_Float16>(8.5),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(7.0),
+ static_cast<_Float16>(11.833333), static_cast<_Float16>(10.285714),
+ static_cast<_Float16>(9.125), static_cast<_Float16>(8.222222),
+ static_cast<_Float16>(7.5), static_cast<_Float16>(12.666667),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(9.75),
+ static_cast<_Float16>(8.777778), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(81.0), static_cast<_Float16>(41.0),
+ static_cast<_Float16>(27.666666), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(86.0),
+ static_cast<_Float16>(43.5), static_cast<_Float16>(29.333334),
+ static_cast<_Float16>(22.25), static_cast<_Float16>(18.0),
+ static_cast<_Float16>(91.0), static_cast<_Float16>(46.0),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(23.5),
+ static_cast<_Float16>(19.0), static_cast<_Float16>(96.0),
+ static_cast<_Float16>(48.5), static_cast<_Float16>(32.666668),
+ static_cast<_Float16>(24.75), static_cast<_Float16>(20.0),
+ static_cast<_Float16>(16.833334), static_cast<_Float16>(14.571428),
+ static_cast<_Float16>(12.875), static_cast<_Float16>(11.555555),
+ static_cast<_Float16>(10.5), static_cast<_Float16>(17.666666),
+ static_cast<_Float16>(15.285714), static_cast<_Float16>(13.5),
+ static_cast<_Float16>(12.111111), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(18.5), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(14.125), static_cast<_Float16>(12.666667),
+ static_cast<_Float16>(11.5), static_cast<_Float16>(19.333334),
+ static_cast<_Float16>(16.714285), static_cast<_Float16>(14.75),
+ static_cast<_Float16>(13.222222), static_cast<_Float16>(12.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 1, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.5), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(3.6666667), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(4.25), static_cast<_Float16>(4.5),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(22.0),
+ static_cast<_Float16>(23.0), static_cast<_Float16>(24.0),
+ static_cast<_Float16>(25.0), static_cast<_Float16>(13.0),
+ static_cast<_Float16>(13.5), static_cast<_Float16>(14.0),
+ static_cast<_Float16>(14.5), static_cast<_Float16>(15.0),
+ static_cast<_Float16>(10.333333), static_cast<_Float16>(10.666667),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(11.333333),
+ static_cast<_Float16>(11.666667), static_cast<_Float16>(9.0),
+ static_cast<_Float16>(9.25), static_cast<_Float16>(9.5),
+ static_cast<_Float16>(9.75), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(8.2), static_cast<_Float16>(8.4),
+ static_cast<_Float16>(8.6), static_cast<_Float16>(8.8),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(7.6666665),
+ static_cast<_Float16>(7.8333335), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(8.166667), static_cast<_Float16>(8.333333),
+ static_cast<_Float16>(7.285714), static_cast<_Float16>(7.428571),
+ static_cast<_Float16>(7.571429), static_cast<_Float16>(7.714286),
+ static_cast<_Float16>(7.857143), static_cast<_Float16>(7.0),
+ static_cast<_Float16>(7.125), static_cast<_Float16>(7.25),
+ static_cast<_Float16>(7.375), static_cast<_Float16>(7.5),
+ static_cast<_Float16>(12.2), static_cast<_Float16>(12.4),
+ static_cast<_Float16>(12.6), static_cast<_Float16>(12.8),
+ static_cast<_Float16>(13.0), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(11.166667), static_cast<_Float16>(11.333333),
+ static_cast<_Float16>(11.5), static_cast<_Float16>(11.666667),
+ static_cast<_Float16>(10.142858), static_cast<_Float16>(10.285714),
+ static_cast<_Float16>(10.428572), static_cast<_Float16>(10.571428),
+ static_cast<_Float16>(10.714286), static_cast<_Float16>(9.5),
+ static_cast<_Float16>(9.625), static_cast<_Float16>(9.75),
+ static_cast<_Float16>(9.875), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(9.111111),
+ static_cast<_Float16>(9.222222), static_cast<_Float16>(9.333333),
+ static_cast<_Float16>(9.444445), static_cast<_Float16>(8.6),
+ static_cast<_Float16>(8.7), static_cast<_Float16>(8.8),
+ static_cast<_Float16>(8.9), static_cast<_Float16>(9.0),
+ static_cast<_Float16>(8.272727), static_cast<_Float16>(8.363636),
+ static_cast<_Float16>(8.454545), static_cast<_Float16>(8.545455),
+ static_cast<_Float16>(8.636364), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(8.083333), static_cast<_Float16>(8.166667),
+ static_cast<_Float16>(8.25), static_cast<_Float16>(8.333333),
+ static_cast<_Float16>(11.222222), static_cast<_Float16>(11.333333),
+ static_cast<_Float16>(11.444445), static_cast<_Float16>(11.555555),
+ static_cast<_Float16>(11.666667), static_cast<_Float16>(10.6),
+ static_cast<_Float16>(10.7), static_cast<_Float16>(10.8),
+ static_cast<_Float16>(10.9), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(10.090909), static_cast<_Float16>(10.181818),
+ static_cast<_Float16>(10.272727), static_cast<_Float16>(10.363636),
+ static_cast<_Float16>(10.454545), static_cast<_Float16>(9.666667),
+ static_cast<_Float16>(9.75), static_cast<_Float16>(9.833333),
+ static_cast<_Float16>(9.916667), static_cast<_Float16>(10.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(1, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(3.5), static_cast<_Float16>(2.6666667),
+ static_cast<_Float16>(2.25), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.3333335), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(8.5), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(4.75), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(7.6666665), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(13.5), static_cast<_Float16>(9.333333),
+ static_cast<_Float16>(7.25), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(8.5),
+ static_cast<_Float16>(7.0), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(18.5), static_cast<_Float16>(12.666667),
+ static_cast<_Float16>(9.75), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(41.0), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(14.333333), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(46.0),
+ static_cast<_Float16>(23.5), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(12.25), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(51.0), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(17.666666), static_cast<_Float16>(13.5),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(56.0),
+ static_cast<_Float16>(28.5), static_cast<_Float16>(19.333334),
+ static_cast<_Float16>(14.75), static_cast<_Float16>(12.0),
+ static_cast<_Float16>(61.0), static_cast<_Float16>(31.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(13.0), static_cast<_Float16>(66.0),
+ static_cast<_Float16>(33.5), static_cast<_Float16>(22.666666),
+ static_cast<_Float16>(17.25), static_cast<_Float16>(14.0),
+ static_cast<_Float16>(71.0), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(24.333334), static_cast<_Float16>(18.5),
+ static_cast<_Float16>(15.0), static_cast<_Float16>(76.0),
+ static_cast<_Float16>(38.5), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(19.75), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(81.0), static_cast<_Float16>(41.0),
+ static_cast<_Float16>(27.666666), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(86.0),
+ static_cast<_Float16>(43.5), static_cast<_Float16>(29.333334),
+ static_cast<_Float16>(22.25), static_cast<_Float16>(18.0),
+ static_cast<_Float16>(91.0), static_cast<_Float16>(46.0),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(23.5),
+ static_cast<_Float16>(19.0), static_cast<_Float16>(96.0),
+ static_cast<_Float16>(48.5), static_cast<_Float16>(32.666668),
+ static_cast<_Float16>(24.75), static_cast<_Float16>(20.0),
+ static_cast<_Float16>(101.0), static_cast<_Float16>(51.0),
+ static_cast<_Float16>(34.333332), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(106.0),
+ static_cast<_Float16>(53.5), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(27.25), static_cast<_Float16>(22.0),
+ static_cast<_Float16>(111.0), static_cast<_Float16>(56.0),
+ static_cast<_Float16>(37.666668), static_cast<_Float16>(28.5),
+ static_cast<_Float16>(23.0), static_cast<_Float16>(116.0),
+ static_cast<_Float16>(58.5), static_cast<_Float16>(39.333332),
+ static_cast<_Float16>(29.75), static_cast<_Float16>(24.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(1, 2, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(7.0), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(12.0),
+ static_cast<_Float16>(13.0), static_cast<_Float16>(14.0),
+ static_cast<_Float16>(15.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(18.0),
+ static_cast<_Float16>(19.0), static_cast<_Float16>(20.0),
+ static_cast<_Float16>(10.5), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(11.5), static_cast<_Float16>(12.0),
+ static_cast<_Float16>(12.5), static_cast<_Float16>(13.0),
+ static_cast<_Float16>(13.5), static_cast<_Float16>(14.0),
+ static_cast<_Float16>(14.5), static_cast<_Float16>(15.0),
+ static_cast<_Float16>(15.5), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(16.5), static_cast<_Float16>(17.0),
+ static_cast<_Float16>(17.5), static_cast<_Float16>(18.0),
+ static_cast<_Float16>(18.5), static_cast<_Float16>(19.0),
+ static_cast<_Float16>(19.5), static_cast<_Float16>(20.0),
+ static_cast<_Float16>(41.0), static_cast<_Float16>(42.0),
+ static_cast<_Float16>(43.0), static_cast<_Float16>(44.0),
+ static_cast<_Float16>(45.0), static_cast<_Float16>(46.0),
+ static_cast<_Float16>(47.0), static_cast<_Float16>(48.0),
+ static_cast<_Float16>(49.0), static_cast<_Float16>(50.0),
+ static_cast<_Float16>(51.0), static_cast<_Float16>(52.0),
+ static_cast<_Float16>(53.0), static_cast<_Float16>(54.0),
+ static_cast<_Float16>(55.0), static_cast<_Float16>(56.0),
+ static_cast<_Float16>(57.0), static_cast<_Float16>(58.0),
+ static_cast<_Float16>(59.0), static_cast<_Float16>(60.0),
+ static_cast<_Float16>(30.5), static_cast<_Float16>(31.0),
+ static_cast<_Float16>(31.5), static_cast<_Float16>(32.0),
+ static_cast<_Float16>(32.5), static_cast<_Float16>(33.0),
+ static_cast<_Float16>(33.5), static_cast<_Float16>(34.0),
+ static_cast<_Float16>(34.5), static_cast<_Float16>(35.0),
+ static_cast<_Float16>(35.5), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(36.5), static_cast<_Float16>(37.0),
+ static_cast<_Float16>(37.5), static_cast<_Float16>(38.0),
+ static_cast<_Float16>(38.5), static_cast<_Float16>(39.0),
+ static_cast<_Float16>(39.5), static_cast<_Float16>(40.0),
+ static_cast<_Float16>(81.0), static_cast<_Float16>(82.0),
+ static_cast<_Float16>(83.0), static_cast<_Float16>(84.0),
+ static_cast<_Float16>(85.0), static_cast<_Float16>(86.0),
+ static_cast<_Float16>(87.0), static_cast<_Float16>(88.0),
+ static_cast<_Float16>(89.0), static_cast<_Float16>(90.0),
+ static_cast<_Float16>(91.0), static_cast<_Float16>(92.0),
+ static_cast<_Float16>(93.0), static_cast<_Float16>(94.0),
+ static_cast<_Float16>(95.0), static_cast<_Float16>(96.0),
+ static_cast<_Float16>(97.0), static_cast<_Float16>(98.0),
+ static_cast<_Float16>(99.0), static_cast<_Float16>(100.0),
+ static_cast<_Float16>(50.5), static_cast<_Float16>(51.0),
+ static_cast<_Float16>(51.5), static_cast<_Float16>(52.0),
+ static_cast<_Float16>(52.5), static_cast<_Float16>(53.0),
+ static_cast<_Float16>(53.5), static_cast<_Float16>(54.0),
+ static_cast<_Float16>(54.5), static_cast<_Float16>(55.0),
+ static_cast<_Float16>(55.5), static_cast<_Float16>(56.0),
+ static_cast<_Float16>(56.5), static_cast<_Float16>(57.0),
+ static_cast<_Float16>(57.5), static_cast<_Float16>(58.0),
+ static_cast<_Float16>(58.5), static_cast<_Float16>(59.0),
+ static_cast<_Float16>(59.5), static_cast<_Float16>(60.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(3.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(6.0),
+ static_cast<_Float16>(7.0), static_cast<_Float16>(8.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(10.0),
+ static_cast<_Float16>(11.0), static_cast<_Float16>(12.0),
+ static_cast<_Float16>(13.0), static_cast<_Float16>(14.0),
+ static_cast<_Float16>(15.0), static_cast<_Float16>(16.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(18.0),
+ static_cast<_Float16>(19.0), static_cast<_Float16>(20.0),
+ static_cast<_Float16>(21.0), static_cast<_Float16>(22.0),
+ static_cast<_Float16>(23.0), static_cast<_Float16>(24.0),
+ static_cast<_Float16>(25.0), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(27.0), static_cast<_Float16>(28.0),
+ static_cast<_Float16>(29.0), static_cast<_Float16>(30.0),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(32.0),
+ static_cast<_Float16>(33.0), static_cast<_Float16>(34.0),
+ static_cast<_Float16>(35.0), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(37.0), static_cast<_Float16>(38.0),
+ static_cast<_Float16>(39.0), static_cast<_Float16>(40.0),
+ static_cast<_Float16>(20.5), static_cast<_Float16>(21.0),
+ static_cast<_Float16>(21.5), static_cast<_Float16>(22.0),
+ static_cast<_Float16>(22.5), static_cast<_Float16>(23.0),
+ static_cast<_Float16>(23.5), static_cast<_Float16>(24.0),
+ static_cast<_Float16>(24.5), static_cast<_Float16>(25.0),
+ static_cast<_Float16>(25.5), static_cast<_Float16>(26.0),
+ static_cast<_Float16>(26.5), static_cast<_Float16>(27.0),
+ static_cast<_Float16>(27.5), static_cast<_Float16>(28.0),
+ static_cast<_Float16>(28.5), static_cast<_Float16>(29.0),
+ static_cast<_Float16>(29.5), static_cast<_Float16>(30.0),
+ static_cast<_Float16>(30.5), static_cast<_Float16>(31.0),
+ static_cast<_Float16>(31.5), static_cast<_Float16>(32.0),
+ static_cast<_Float16>(32.5), static_cast<_Float16>(33.0),
+ static_cast<_Float16>(33.5), static_cast<_Float16>(34.0),
+ static_cast<_Float16>(34.5), static_cast<_Float16>(35.0),
+ static_cast<_Float16>(35.5), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(36.5), static_cast<_Float16>(37.0),
+ static_cast<_Float16>(37.5), static_cast<_Float16>(38.0),
+ static_cast<_Float16>(38.5), static_cast<_Float16>(39.0),
+ static_cast<_Float16>(39.5), static_cast<_Float16>(40.0),
+ static_cast<_Float16>(27.0), static_cast<_Float16>(27.333334),
+ static_cast<_Float16>(27.666666), static_cast<_Float16>(28.0),
+ static_cast<_Float16>(28.333334), static_cast<_Float16>(28.666666),
+ static_cast<_Float16>(29.0), static_cast<_Float16>(29.333334),
+ static_cast<_Float16>(29.666666), static_cast<_Float16>(30.0),
+ static_cast<_Float16>(30.333334), static_cast<_Float16>(30.666666),
+ static_cast<_Float16>(31.0), static_cast<_Float16>(31.333334),
+ static_cast<_Float16>(31.666666), static_cast<_Float16>(32.0),
+ static_cast<_Float16>(32.333332), static_cast<_Float16>(32.666668),
+ static_cast<_Float16>(33.0), static_cast<_Float16>(33.333332),
+ static_cast<_Float16>(33.666668), static_cast<_Float16>(34.0),
+ static_cast<_Float16>(34.333332), static_cast<_Float16>(34.666668),
+ static_cast<_Float16>(35.0), static_cast<_Float16>(35.333332),
+ static_cast<_Float16>(35.666668), static_cast<_Float16>(36.0),
+ static_cast<_Float16>(36.333332), static_cast<_Float16>(36.666668),
+ static_cast<_Float16>(37.0), static_cast<_Float16>(37.333332),
+ static_cast<_Float16>(37.666668), static_cast<_Float16>(38.0),
+ static_cast<_Float16>(38.333332), static_cast<_Float16>(38.666668),
+ static_cast<_Float16>(39.0), static_cast<_Float16>(39.333332),
+ static_cast<_Float16>(39.666668), static_cast<_Float16>(40.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 5, 1, 4, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 5, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ t.add_i(1);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(1.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(2.3333333), static_cast<_Float16>(2.0),
+ static_cast<_Float16>(9.0), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(3.6666667), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(13.0), static_cast<_Float16>(7.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(17.0), static_cast<_Float16>(9.0),
+ static_cast<_Float16>(6.3333335), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.2), static_cast<_Float16>(3.6666667),
+ static_cast<_Float16>(3.2857144), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(3.857143), static_cast<_Float16>(3.5),
+ static_cast<_Float16>(5.8), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.428571), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(6.6), static_cast<_Float16>(5.6666665),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.5),
+ static_cast<_Float16>(7.4), static_cast<_Float16>(6.3333335),
+ static_cast<_Float16>(5.571429), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.5555553), static_cast<_Float16>(4.2),
+ static_cast<_Float16>(3.909091), static_cast<_Float16>(3.6666667),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.6),
+ static_cast<_Float16>(4.2727275), static_cast<_Float16>(4.0),
+ static_cast<_Float16>(5.4444447), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(4.6363635), static_cast<_Float16>(4.3333335),
+ static_cast<_Float16>(5.888889), static_cast<_Float16>(5.4),
+ static_cast<_Float16>(5.0), static_cast<_Float16>(4.6666665),
+ static_cast<_Float16>(6.3333335), static_cast<_Float16>(5.8),
+ static_cast<_Float16>(5.3636365), static_cast<_Float16>(5.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.divide_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+}
-// TEST(nntrainer_Tensor, divide_i_broadcast_not_broadcastable_02_n) {
-// nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, divide_i_broadcast_not_supported_01_n) {
+ nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(target.divide_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+ EXPECT_EQ(target.divide_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// TEST(nntrainer_Tensor, add_i_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
-// int channel = 1;
+TEST(nntrainer_Tensor, divide_i_broadcast_not_broadcastable_02_n) {
+ nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
+ EXPECT_EQ(target.divide_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// nntrainer::Tensor original(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// original.copy(target);
+TEST(nntrainer_Tensor, add_i_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// status = target.add_i((_Float16)2.1);
-// EXPECT_EQ(status, ML_ERROR_NONE);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
-// _Float16 *previous = original.getData<_Float16>();
-// ASSERT_NE(nullptr, previous);
-// _Float16 *data = target.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
+ nntrainer::Tensor original(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ original.copy(target);
-// for (int i = 0; i < batch * height * width; ++i) {
-// EXPECT_FLOAT_EQ(data[i], (_Float16)(previous[i] + (_Float16)2.1));
-// }
-// }
+ status = target.add_i((_Float16)2.1);
+ EXPECT_EQ(status, ML_ERROR_NONE);
-// TEST(nntrainer_Tensor, add_i_02_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
-// int channel = 1;
+ _Float16 *previous = original.getData<_Float16>();
+ ASSERT_NE(nullptr, previous);
+ _Float16 *data = target.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
-// nntrainer::Tensor target(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1);
+ for (int i = 0; i < batch * height * width; ++i) {
+ EXPECT_FLOAT_EQ(data[i], (_Float16)(previous[i] + (_Float16)2.1));
+ }
+}
+
+TEST(nntrainer_Tensor, add_i_02_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// nntrainer::Tensor original(batch, height, width, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// original.copy(target);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1);
-// status = target.add_i(target, 3.0);
-// EXPECT_EQ(status, ML_ERROR_NONE);
+ nntrainer::Tensor original(batch, height, width, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ original.copy(target);
-// _Float16 *previous = original.getData<_Float16>();
-// ASSERT_NE(nullptr, previous);
-// _Float16 *data = target.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
+ status = target.add_i(target, 3.0);
+ EXPECT_EQ(status, ML_ERROR_NONE);
-// for (int i = 0; i < batch * height * width; ++i) {
-// EXPECT_FLOAT_EQ(data[i], previous[i] * 4.0);
-// }
-// }
+ _Float16 *previous = original.getData<_Float16>();
+ ASSERT_NE(nullptr, previous);
+ _Float16 *data = target.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
-// // /**
-// // * @brief operand dimension is not right
-// // */
-// TEST(nntrainer_Tensor, add_i_01_n) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
-// int channel = 1;
+ for (int i = 0; i < batch * height * width; ++i) {
+ EXPECT_FLOAT_EQ(data[i], previous[i] * 4.0);
+ }
+}
-// nntrainer::Tensor target(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1);
+// /**
+// * @brief operand dimension is not right
+// */
+TEST(nntrainer_Tensor, add_i_01_n) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// nntrainer::Tensor target2(batch, height - 2, width - 3,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1);
-// status = target.add_i(target2);
-// EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-// }
+ nntrainer::Tensor target2(batch, height - 2, width - 3,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, add_i_broadcast_01_p) {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26,
-// 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54,
-// 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 40, 42,
-// 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70,
-// 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98,
-// 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 80, 82, 84, 86,
-// 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114,
-// 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142,
-// 144, 146, 148, 150, 152, 154, 156, 158};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 1, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26,
-// 28, 30, 32, 34, 36, 38, 20, 22, 24, 26, 28, 30, 32, 34,
-// 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62,
-// 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90,
-// 92, 94, 96, 98, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98,
-// 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126,
-// 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154,
-// 156, 158, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162,
-// 164, 166, 168, 170, 172, 174, 176, 178};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15,
-// 16, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 30, 31, 32,
-// 33, 34, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 48, 49,
-// 50, 51, 52, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66,
-// 67, 68, 69, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82,
-// 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 96, 97, 98, 99,
-// 100, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 114, 115, 116,
-// 117, 118, 120, 121, 122, 123, 124, 126, 127, 128, 129, 130, 132, 133,
-// 134, 135, 136, 138, 139, 140, 141, 142};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 2, 4, 6, 8, 5, 7, 9, 11, 13, 10, 12, 14, 16,
-// 18, 15, 17, 19, 21, 23, 20, 22, 24, 26, 28, 25, 27, 29,
-// 31, 33, 30, 32, 34, 36, 38, 35, 37, 39, 41, 43, 45, 47,
-// 49, 51, 53, 50, 52, 54, 56, 58, 55, 57, 59, 61, 63, 60,
-// 62, 64, 66, 68, 65, 67, 69, 71, 73, 70, 72, 74, 76, 78,
-// 75, 77, 79, 81, 83, 80, 82, 84, 86, 88, 90, 92, 94, 96,
-// 98, 95, 97, 99, 101, 103, 100, 102, 104, 106, 108, 105, 107, 109,
-// 111, 113, 110, 112, 114, 116, 118, 115, 117, 119, 121, 123, 120, 122,
-// 124, 126, 128, 125, 127, 129, 131, 133};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 2, 4, 6, 8, 5, 7, 9, 11, 13, 10, 12, 14, 16,
-// 18, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 30, 32, 34,
-// 36, 38, 35, 37, 39, 41, 43, 40, 42, 44, 46, 48, 40, 42,
-// 44, 46, 48, 45, 47, 49, 51, 53, 50, 52, 54, 56, 58, 55,
-// 57, 59, 61, 63, 65, 67, 69, 71, 73, 70, 72, 74, 76, 78,
-// 75, 77, 79, 81, 83, 80, 82, 84, 86, 88, 80, 82, 84, 86,
-// 88, 85, 87, 89, 91, 93, 90, 92, 94, 96, 98, 95, 97, 99,
-// 101, 103, 105, 107, 109, 111, 113, 110, 112, 114, 116, 118, 115, 117,
-// 119, 121, 123, 120, 122, 124, 126, 128};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 1, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15,
-// 16, 18, 19, 20, 21, 22, 20, 21, 22, 23, 24, 26, 27, 28,
-// 29, 30, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45,
-// 46, 47, 48, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 62,
-// 63, 64, 65, 66, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74,
-// 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 91,
-// 92, 94, 95, 96, 97, 98, 100, 101, 102, 103, 104, 106, 107, 108,
-// 109, 110, 108, 109, 110, 111, 112, 114, 115, 116, 117, 118, 120, 121,
-// 122, 123, 124, 126, 127, 128, 129, 130};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 2, 4, 6, 8, 5, 7, 9, 11, 13, 10, 12, 14, 16,
-// 18, 15, 17, 19, 21, 23, 20, 22, 24, 26, 28, 25, 27, 29,
-// 31, 33, 30, 32, 34, 36, 38, 35, 37, 39, 41, 43, 40, 42,
-// 44, 46, 48, 45, 47, 49, 51, 53, 50, 52, 54, 56, 58, 55,
-// 57, 59, 61, 63, 60, 62, 64, 66, 68, 65, 67, 69, 71, 73,
-// 70, 72, 74, 76, 78, 75, 77, 79, 81, 83, 80, 82, 84, 86,
-// 88, 85, 87, 89, 91, 93, 90, 92, 94, 96, 98, 95, 97, 99,
-// 101, 103, 100, 102, 104, 106, 108, 105, 107, 109, 111, 113, 110, 112,
-// 114, 116, 118, 115, 117, 119, 121, 123};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 2, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-// 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28,
-// 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 40, 41,
-// 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-// 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 80, 81, 82, 83,
-// 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
-// 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
-// 113, 114, 115, 116, 117, 118, 119, 120};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-// 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
-// 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42,
-// 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
-// 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-// 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85,
-// 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-// 114, 115, 116, 117, 118, 119, 120, 121};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// m.add_i(1.0);
-// _Float16 answer_data[] = {
-// 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
-// 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
-// 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
-// 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
-// 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-// 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
-// 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98,
-// 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
-// 113, 114, 115, 116, 117, 118, 119, 120};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 5, 1, 4, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(3, 5, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 1, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {0, 2, 4, 6, 4, 6, 8, 10, 8, 10, 12, 14,
-// 12, 14, 16, 18, 16, 18, 20, 22, 24, 26, 28, 30,
-// 28, 30, 32, 34, 32, 34, 36, 38, 36, 38, 40, 42,
-// 40, 42, 44, 46, 48, 50, 52, 54, 52, 54, 56, 58,
-// 56, 58, 60, 62, 60, 62, 64, 66, 64, 66, 68, 70};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(1, 1, 2, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(1, 1, 2, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 1, 2, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {0.0, 2.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(16, 1, 1, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor t =
-// ranged(16, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(1, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0,
-// 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0};
-// nntrainer::Tensor answer(ref_dim, answer_data);
-// int status = t.add_i(m);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// EXPECT_EQ(t, answer);
-// }
-// }
+ status = target.add_i(target2);
+ EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
+
+TEST(nntrainer_Tensor, add_i_broadcast_01_p) {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(10),
+ static_cast<_Float16>(12), static_cast<_Float16>(14),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(50),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(68), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(50),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(68), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(90),
+ static_cast<_Float16>(92), static_cast<_Float16>(94),
+ static_cast<_Float16>(96), static_cast<_Float16>(98),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(110),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(116), static_cast<_Float16>(118),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(90),
+ static_cast<_Float16>(92), static_cast<_Float16>(94),
+ static_cast<_Float16>(96), static_cast<_Float16>(98),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(110),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(116), static_cast<_Float16>(118),
+ static_cast<_Float16>(120), static_cast<_Float16>(122),
+ static_cast<_Float16>(124), static_cast<_Float16>(126),
+ static_cast<_Float16>(128), static_cast<_Float16>(130),
+ static_cast<_Float16>(132), static_cast<_Float16>(134),
+ static_cast<_Float16>(136), static_cast<_Float16>(138),
+ static_cast<_Float16>(140), static_cast<_Float16>(142),
+ static_cast<_Float16>(144), static_cast<_Float16>(146),
+ static_cast<_Float16>(148), static_cast<_Float16>(150),
+ static_cast<_Float16>(152), static_cast<_Float16>(154),
+ static_cast<_Float16>(156), static_cast<_Float16>(158)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 1, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(10),
+ static_cast<_Float16>(12), static_cast<_Float16>(14),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(50),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(68), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(90),
+ static_cast<_Float16>(92), static_cast<_Float16>(94),
+ static_cast<_Float16>(96), static_cast<_Float16>(98),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(90),
+ static_cast<_Float16>(92), static_cast<_Float16>(94),
+ static_cast<_Float16>(96), static_cast<_Float16>(98),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(110),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(116), static_cast<_Float16>(118),
+ static_cast<_Float16>(120), static_cast<_Float16>(122),
+ static_cast<_Float16>(124), static_cast<_Float16>(126),
+ static_cast<_Float16>(128), static_cast<_Float16>(130),
+ static_cast<_Float16>(132), static_cast<_Float16>(134),
+ static_cast<_Float16>(136), static_cast<_Float16>(138),
+ static_cast<_Float16>(140), static_cast<_Float16>(142),
+ static_cast<_Float16>(144), static_cast<_Float16>(146),
+ static_cast<_Float16>(148), static_cast<_Float16>(150),
+ static_cast<_Float16>(152), static_cast<_Float16>(154),
+ static_cast<_Float16>(156), static_cast<_Float16>(158),
+ static_cast<_Float16>(140), static_cast<_Float16>(142),
+ static_cast<_Float16>(144), static_cast<_Float16>(146),
+ static_cast<_Float16>(148), static_cast<_Float16>(150),
+ static_cast<_Float16>(152), static_cast<_Float16>(154),
+ static_cast<_Float16>(156), static_cast<_Float16>(158),
+ static_cast<_Float16>(160), static_cast<_Float16>(162),
+ static_cast<_Float16>(164), static_cast<_Float16>(166),
+ static_cast<_Float16>(168), static_cast<_Float16>(170),
+ static_cast<_Float16>(172), static_cast<_Float16>(174),
+ static_cast<_Float16>(176), static_cast<_Float16>(178)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(2), static_cast<_Float16>(3),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(7), static_cast<_Float16>(8),
+ static_cast<_Float16>(9), static_cast<_Float16>(10),
+ static_cast<_Float16>(12), static_cast<_Float16>(13),
+ static_cast<_Float16>(14), static_cast<_Float16>(15),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(19), static_cast<_Float16>(20),
+ static_cast<_Float16>(21), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(25),
+ static_cast<_Float16>(26), static_cast<_Float16>(27),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(31), static_cast<_Float16>(32),
+ static_cast<_Float16>(33), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(37),
+ static_cast<_Float16>(38), static_cast<_Float16>(39),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(43), static_cast<_Float16>(44),
+ static_cast<_Float16>(45), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(49),
+ static_cast<_Float16>(50), static_cast<_Float16>(51),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(55), static_cast<_Float16>(56),
+ static_cast<_Float16>(57), static_cast<_Float16>(58),
+ static_cast<_Float16>(60), static_cast<_Float16>(61),
+ static_cast<_Float16>(62), static_cast<_Float16>(63),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(67), static_cast<_Float16>(68),
+ static_cast<_Float16>(69), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(73),
+ static_cast<_Float16>(74), static_cast<_Float16>(75),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(79), static_cast<_Float16>(80),
+ static_cast<_Float16>(81), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(85),
+ static_cast<_Float16>(86), static_cast<_Float16>(87),
+ static_cast<_Float16>(88), static_cast<_Float16>(90),
+ static_cast<_Float16>(91), static_cast<_Float16>(92),
+ static_cast<_Float16>(93), static_cast<_Float16>(94),
+ static_cast<_Float16>(96), static_cast<_Float16>(97),
+ static_cast<_Float16>(98), static_cast<_Float16>(99),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(103), static_cast<_Float16>(104),
+ static_cast<_Float16>(105), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(109),
+ static_cast<_Float16>(110), static_cast<_Float16>(111),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(115), static_cast<_Float16>(116),
+ static_cast<_Float16>(117), static_cast<_Float16>(118),
+ static_cast<_Float16>(120), static_cast<_Float16>(121),
+ static_cast<_Float16>(122), static_cast<_Float16>(123),
+ static_cast<_Float16>(124), static_cast<_Float16>(126),
+ static_cast<_Float16>(127), static_cast<_Float16>(128),
+ static_cast<_Float16>(129), static_cast<_Float16>(130),
+ static_cast<_Float16>(132), static_cast<_Float16>(133),
+ static_cast<_Float16>(134), static_cast<_Float16>(135),
+ static_cast<_Float16>(136), static_cast<_Float16>(138),
+ static_cast<_Float16>(139), static_cast<_Float16>(140),
+ static_cast<_Float16>(141), static_cast<_Float16>(142)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(5),
+ static_cast<_Float16>(7), static_cast<_Float16>(9),
+ static_cast<_Float16>(11), static_cast<_Float16>(13),
+ static_cast<_Float16>(10), static_cast<_Float16>(12),
+ static_cast<_Float16>(14), static_cast<_Float16>(16),
+ static_cast<_Float16>(18), static_cast<_Float16>(15),
+ static_cast<_Float16>(17), static_cast<_Float16>(19),
+ static_cast<_Float16>(21), static_cast<_Float16>(23),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(25),
+ static_cast<_Float16>(27), static_cast<_Float16>(29),
+ static_cast<_Float16>(31), static_cast<_Float16>(33),
+ static_cast<_Float16>(30), static_cast<_Float16>(32),
+ static_cast<_Float16>(34), static_cast<_Float16>(36),
+ static_cast<_Float16>(38), static_cast<_Float16>(35),
+ static_cast<_Float16>(37), static_cast<_Float16>(39),
+ static_cast<_Float16>(41), static_cast<_Float16>(43),
+ static_cast<_Float16>(45), static_cast<_Float16>(47),
+ static_cast<_Float16>(49), static_cast<_Float16>(51),
+ static_cast<_Float16>(53), static_cast<_Float16>(50),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(55), static_cast<_Float16>(57),
+ static_cast<_Float16>(59), static_cast<_Float16>(61),
+ static_cast<_Float16>(63), static_cast<_Float16>(60),
+ static_cast<_Float16>(62), static_cast<_Float16>(64),
+ static_cast<_Float16>(66), static_cast<_Float16>(68),
+ static_cast<_Float16>(65), static_cast<_Float16>(67),
+ static_cast<_Float16>(69), static_cast<_Float16>(71),
+ static_cast<_Float16>(73), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(75), static_cast<_Float16>(77),
+ static_cast<_Float16>(79), static_cast<_Float16>(81),
+ static_cast<_Float16>(83), static_cast<_Float16>(80),
+ static_cast<_Float16>(82), static_cast<_Float16>(84),
+ static_cast<_Float16>(86), static_cast<_Float16>(88),
+ static_cast<_Float16>(90), static_cast<_Float16>(92),
+ static_cast<_Float16>(94), static_cast<_Float16>(96),
+ static_cast<_Float16>(98), static_cast<_Float16>(95),
+ static_cast<_Float16>(97), static_cast<_Float16>(99),
+ static_cast<_Float16>(101), static_cast<_Float16>(103),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(105),
+ static_cast<_Float16>(107), static_cast<_Float16>(109),
+ static_cast<_Float16>(111), static_cast<_Float16>(113),
+ static_cast<_Float16>(110), static_cast<_Float16>(112),
+ static_cast<_Float16>(114), static_cast<_Float16>(116),
+ static_cast<_Float16>(118), static_cast<_Float16>(115),
+ static_cast<_Float16>(117), static_cast<_Float16>(119),
+ static_cast<_Float16>(121), static_cast<_Float16>(123),
+ static_cast<_Float16>(120), static_cast<_Float16>(122),
+ static_cast<_Float16>(124), static_cast<_Float16>(126),
+ static_cast<_Float16>(128), static_cast<_Float16>(125),
+ static_cast<_Float16>(127), static_cast<_Float16>(129),
+ static_cast<_Float16>(131), static_cast<_Float16>(133)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(5),
+ static_cast<_Float16>(7), static_cast<_Float16>(9),
+ static_cast<_Float16>(11), static_cast<_Float16>(13),
+ static_cast<_Float16>(10), static_cast<_Float16>(12),
+ static_cast<_Float16>(14), static_cast<_Float16>(16),
+ static_cast<_Float16>(18), static_cast<_Float16>(15),
+ static_cast<_Float16>(17), static_cast<_Float16>(19),
+ static_cast<_Float16>(21), static_cast<_Float16>(23),
+ static_cast<_Float16>(25), static_cast<_Float16>(27),
+ static_cast<_Float16>(29), static_cast<_Float16>(31),
+ static_cast<_Float16>(33), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(35), static_cast<_Float16>(37),
+ static_cast<_Float16>(39), static_cast<_Float16>(41),
+ static_cast<_Float16>(43), static_cast<_Float16>(40),
+ static_cast<_Float16>(42), static_cast<_Float16>(44),
+ static_cast<_Float16>(46), static_cast<_Float16>(48),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(45),
+ static_cast<_Float16>(47), static_cast<_Float16>(49),
+ static_cast<_Float16>(51), static_cast<_Float16>(53),
+ static_cast<_Float16>(50), static_cast<_Float16>(52),
+ static_cast<_Float16>(54), static_cast<_Float16>(56),
+ static_cast<_Float16>(58), static_cast<_Float16>(55),
+ static_cast<_Float16>(57), static_cast<_Float16>(59),
+ static_cast<_Float16>(61), static_cast<_Float16>(63),
+ static_cast<_Float16>(65), static_cast<_Float16>(67),
+ static_cast<_Float16>(69), static_cast<_Float16>(71),
+ static_cast<_Float16>(73), static_cast<_Float16>(70),
+ static_cast<_Float16>(72), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(78),
+ static_cast<_Float16>(75), static_cast<_Float16>(77),
+ static_cast<_Float16>(79), static_cast<_Float16>(81),
+ static_cast<_Float16>(83), static_cast<_Float16>(80),
+ static_cast<_Float16>(82), static_cast<_Float16>(84),
+ static_cast<_Float16>(86), static_cast<_Float16>(88),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(85),
+ static_cast<_Float16>(87), static_cast<_Float16>(89),
+ static_cast<_Float16>(91), static_cast<_Float16>(93),
+ static_cast<_Float16>(90), static_cast<_Float16>(92),
+ static_cast<_Float16>(94), static_cast<_Float16>(96),
+ static_cast<_Float16>(98), static_cast<_Float16>(95),
+ static_cast<_Float16>(97), static_cast<_Float16>(99),
+ static_cast<_Float16>(101), static_cast<_Float16>(103),
+ static_cast<_Float16>(105), static_cast<_Float16>(107),
+ static_cast<_Float16>(109), static_cast<_Float16>(111),
+ static_cast<_Float16>(113), static_cast<_Float16>(110),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(116), static_cast<_Float16>(118),
+ static_cast<_Float16>(115), static_cast<_Float16>(117),
+ static_cast<_Float16>(119), static_cast<_Float16>(121),
+ static_cast<_Float16>(123), static_cast<_Float16>(120),
+ static_cast<_Float16>(122), static_cast<_Float16>(124),
+ static_cast<_Float16>(126), static_cast<_Float16>(128)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 1, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(2), static_cast<_Float16>(3),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(7), static_cast<_Float16>(8),
+ static_cast<_Float16>(9), static_cast<_Float16>(10),
+ static_cast<_Float16>(12), static_cast<_Float16>(13),
+ static_cast<_Float16>(14), static_cast<_Float16>(15),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(19), static_cast<_Float16>(20),
+ static_cast<_Float16>(21), static_cast<_Float16>(22),
+ static_cast<_Float16>(20), static_cast<_Float16>(21),
+ static_cast<_Float16>(22), static_cast<_Float16>(23),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(27), static_cast<_Float16>(28),
+ static_cast<_Float16>(29), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(33),
+ static_cast<_Float16>(34), static_cast<_Float16>(35),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(39), static_cast<_Float16>(40),
+ static_cast<_Float16>(41), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(45),
+ static_cast<_Float16>(46), static_cast<_Float16>(47),
+ static_cast<_Float16>(48), static_cast<_Float16>(50),
+ static_cast<_Float16>(51), static_cast<_Float16>(52),
+ static_cast<_Float16>(53), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(57),
+ static_cast<_Float16>(58), static_cast<_Float16>(59),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(63), static_cast<_Float16>(64),
+ static_cast<_Float16>(65), static_cast<_Float16>(66),
+ static_cast<_Float16>(64), static_cast<_Float16>(65),
+ static_cast<_Float16>(66), static_cast<_Float16>(67),
+ static_cast<_Float16>(68), static_cast<_Float16>(70),
+ static_cast<_Float16>(71), static_cast<_Float16>(72),
+ static_cast<_Float16>(73), static_cast<_Float16>(74),
+ static_cast<_Float16>(76), static_cast<_Float16>(77),
+ static_cast<_Float16>(78), static_cast<_Float16>(79),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(83), static_cast<_Float16>(84),
+ static_cast<_Float16>(85), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(89),
+ static_cast<_Float16>(90), static_cast<_Float16>(91),
+ static_cast<_Float16>(92), static_cast<_Float16>(94),
+ static_cast<_Float16>(95), static_cast<_Float16>(96),
+ static_cast<_Float16>(97), static_cast<_Float16>(98),
+ static_cast<_Float16>(100), static_cast<_Float16>(101),
+ static_cast<_Float16>(102), static_cast<_Float16>(103),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(107), static_cast<_Float16>(108),
+ static_cast<_Float16>(109), static_cast<_Float16>(110),
+ static_cast<_Float16>(108), static_cast<_Float16>(109),
+ static_cast<_Float16>(110), static_cast<_Float16>(111),
+ static_cast<_Float16>(112), static_cast<_Float16>(114),
+ static_cast<_Float16>(115), static_cast<_Float16>(116),
+ static_cast<_Float16>(117), static_cast<_Float16>(118),
+ static_cast<_Float16>(120), static_cast<_Float16>(121),
+ static_cast<_Float16>(122), static_cast<_Float16>(123),
+ static_cast<_Float16>(124), static_cast<_Float16>(126),
+ static_cast<_Float16>(127), static_cast<_Float16>(128),
+ static_cast<_Float16>(129), static_cast<_Float16>(130)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 1, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(5),
+ static_cast<_Float16>(7), static_cast<_Float16>(9),
+ static_cast<_Float16>(11), static_cast<_Float16>(13),
+ static_cast<_Float16>(10), static_cast<_Float16>(12),
+ static_cast<_Float16>(14), static_cast<_Float16>(16),
+ static_cast<_Float16>(18), static_cast<_Float16>(15),
+ static_cast<_Float16>(17), static_cast<_Float16>(19),
+ static_cast<_Float16>(21), static_cast<_Float16>(23),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(25),
+ static_cast<_Float16>(27), static_cast<_Float16>(29),
+ static_cast<_Float16>(31), static_cast<_Float16>(33),
+ static_cast<_Float16>(30), static_cast<_Float16>(32),
+ static_cast<_Float16>(34), static_cast<_Float16>(36),
+ static_cast<_Float16>(38), static_cast<_Float16>(35),
+ static_cast<_Float16>(37), static_cast<_Float16>(39),
+ static_cast<_Float16>(41), static_cast<_Float16>(43),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(45),
+ static_cast<_Float16>(47), static_cast<_Float16>(49),
+ static_cast<_Float16>(51), static_cast<_Float16>(53),
+ static_cast<_Float16>(50), static_cast<_Float16>(52),
+ static_cast<_Float16>(54), static_cast<_Float16>(56),
+ static_cast<_Float16>(58), static_cast<_Float16>(55),
+ static_cast<_Float16>(57), static_cast<_Float16>(59),
+ static_cast<_Float16>(61), static_cast<_Float16>(63),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(68), static_cast<_Float16>(65),
+ static_cast<_Float16>(67), static_cast<_Float16>(69),
+ static_cast<_Float16>(71), static_cast<_Float16>(73),
+ static_cast<_Float16>(70), static_cast<_Float16>(72),
+ static_cast<_Float16>(74), static_cast<_Float16>(76),
+ static_cast<_Float16>(78), static_cast<_Float16>(75),
+ static_cast<_Float16>(77), static_cast<_Float16>(79),
+ static_cast<_Float16>(81), static_cast<_Float16>(83),
+ static_cast<_Float16>(80), static_cast<_Float16>(82),
+ static_cast<_Float16>(84), static_cast<_Float16>(86),
+ static_cast<_Float16>(88), static_cast<_Float16>(85),
+ static_cast<_Float16>(87), static_cast<_Float16>(89),
+ static_cast<_Float16>(91), static_cast<_Float16>(93),
+ static_cast<_Float16>(90), static_cast<_Float16>(92),
+ static_cast<_Float16>(94), static_cast<_Float16>(96),
+ static_cast<_Float16>(98), static_cast<_Float16>(95),
+ static_cast<_Float16>(97), static_cast<_Float16>(99),
+ static_cast<_Float16>(101), static_cast<_Float16>(103),
+ static_cast<_Float16>(100), static_cast<_Float16>(102),
+ static_cast<_Float16>(104), static_cast<_Float16>(106),
+ static_cast<_Float16>(108), static_cast<_Float16>(105),
+ static_cast<_Float16>(107), static_cast<_Float16>(109),
+ static_cast<_Float16>(111), static_cast<_Float16>(113),
+ static_cast<_Float16>(110), static_cast<_Float16>(112),
+ static_cast<_Float16>(114), static_cast<_Float16>(116),
+ static_cast<_Float16>(118), static_cast<_Float16>(115),
+ static_cast<_Float16>(117), static_cast<_Float16>(119),
+ static_cast<_Float16>(121), static_cast<_Float16>(123)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 2, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(2), static_cast<_Float16>(3),
+ static_cast<_Float16>(4), static_cast<_Float16>(5),
+ static_cast<_Float16>(6), static_cast<_Float16>(7),
+ static_cast<_Float16>(8), static_cast<_Float16>(9),
+ static_cast<_Float16>(10), static_cast<_Float16>(11),
+ static_cast<_Float16>(12), static_cast<_Float16>(13),
+ static_cast<_Float16>(14), static_cast<_Float16>(15),
+ static_cast<_Float16>(16), static_cast<_Float16>(17),
+ static_cast<_Float16>(18), static_cast<_Float16>(19),
+ static_cast<_Float16>(21), static_cast<_Float16>(22),
+ static_cast<_Float16>(23), static_cast<_Float16>(24),
+ static_cast<_Float16>(25), static_cast<_Float16>(26),
+ static_cast<_Float16>(27), static_cast<_Float16>(28),
+ static_cast<_Float16>(29), static_cast<_Float16>(30),
+ static_cast<_Float16>(31), static_cast<_Float16>(32),
+ static_cast<_Float16>(33), static_cast<_Float16>(34),
+ static_cast<_Float16>(35), static_cast<_Float16>(36),
+ static_cast<_Float16>(37), static_cast<_Float16>(38),
+ static_cast<_Float16>(39), static_cast<_Float16>(40),
+ static_cast<_Float16>(40), static_cast<_Float16>(41),
+ static_cast<_Float16>(42), static_cast<_Float16>(43),
+ static_cast<_Float16>(44), static_cast<_Float16>(45),
+ static_cast<_Float16>(46), static_cast<_Float16>(47),
+ static_cast<_Float16>(48), static_cast<_Float16>(49),
+ static_cast<_Float16>(50), static_cast<_Float16>(51),
+ static_cast<_Float16>(52), static_cast<_Float16>(53),
+ static_cast<_Float16>(54), static_cast<_Float16>(55),
+ static_cast<_Float16>(56), static_cast<_Float16>(57),
+ static_cast<_Float16>(58), static_cast<_Float16>(59),
+ static_cast<_Float16>(61), static_cast<_Float16>(62),
+ static_cast<_Float16>(63), static_cast<_Float16>(64),
+ static_cast<_Float16>(65), static_cast<_Float16>(66),
+ static_cast<_Float16>(67), static_cast<_Float16>(68),
+ static_cast<_Float16>(69), static_cast<_Float16>(70),
+ static_cast<_Float16>(71), static_cast<_Float16>(72),
+ static_cast<_Float16>(73), static_cast<_Float16>(74),
+ static_cast<_Float16>(75), static_cast<_Float16>(76),
+ static_cast<_Float16>(77), static_cast<_Float16>(78),
+ static_cast<_Float16>(79), static_cast<_Float16>(80),
+ static_cast<_Float16>(80), static_cast<_Float16>(81),
+ static_cast<_Float16>(82), static_cast<_Float16>(83),
+ static_cast<_Float16>(84), static_cast<_Float16>(85),
+ static_cast<_Float16>(86), static_cast<_Float16>(87),
+ static_cast<_Float16>(88), static_cast<_Float16>(89),
+ static_cast<_Float16>(90), static_cast<_Float16>(91),
+ static_cast<_Float16>(92), static_cast<_Float16>(93),
+ static_cast<_Float16>(94), static_cast<_Float16>(95),
+ static_cast<_Float16>(96), static_cast<_Float16>(97),
+ static_cast<_Float16>(98), static_cast<_Float16>(99),
+ static_cast<_Float16>(101), static_cast<_Float16>(102),
+ static_cast<_Float16>(103), static_cast<_Float16>(104),
+ static_cast<_Float16>(105), static_cast<_Float16>(106),
+ static_cast<_Float16>(107), static_cast<_Float16>(108),
+ static_cast<_Float16>(109), static_cast<_Float16>(110),
+ static_cast<_Float16>(111), static_cast<_Float16>(112),
+ static_cast<_Float16>(113), static_cast<_Float16>(114),
+ static_cast<_Float16>(115), static_cast<_Float16>(116),
+ static_cast<_Float16>(117), static_cast<_Float16>(118),
+ static_cast<_Float16>(119), static_cast<_Float16>(120)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(1),
+ static_cast<_Float16>(2), static_cast<_Float16>(3),
+ static_cast<_Float16>(4), static_cast<_Float16>(5),
+ static_cast<_Float16>(6), static_cast<_Float16>(7),
+ static_cast<_Float16>(8), static_cast<_Float16>(9),
+ static_cast<_Float16>(10), static_cast<_Float16>(11),
+ static_cast<_Float16>(12), static_cast<_Float16>(13),
+ static_cast<_Float16>(14), static_cast<_Float16>(15),
+ static_cast<_Float16>(16), static_cast<_Float16>(17),
+ static_cast<_Float16>(18), static_cast<_Float16>(19),
+ static_cast<_Float16>(20), static_cast<_Float16>(21),
+ static_cast<_Float16>(22), static_cast<_Float16>(23),
+ static_cast<_Float16>(24), static_cast<_Float16>(25),
+ static_cast<_Float16>(26), static_cast<_Float16>(27),
+ static_cast<_Float16>(28), static_cast<_Float16>(29),
+ static_cast<_Float16>(30), static_cast<_Float16>(31),
+ static_cast<_Float16>(32), static_cast<_Float16>(33),
+ static_cast<_Float16>(34), static_cast<_Float16>(35),
+ static_cast<_Float16>(36), static_cast<_Float16>(37),
+ static_cast<_Float16>(38), static_cast<_Float16>(39),
+ static_cast<_Float16>(41), static_cast<_Float16>(42),
+ static_cast<_Float16>(43), static_cast<_Float16>(44),
+ static_cast<_Float16>(45), static_cast<_Float16>(46),
+ static_cast<_Float16>(47), static_cast<_Float16>(48),
+ static_cast<_Float16>(49), static_cast<_Float16>(50),
+ static_cast<_Float16>(51), static_cast<_Float16>(52),
+ static_cast<_Float16>(53), static_cast<_Float16>(54),
+ static_cast<_Float16>(55), static_cast<_Float16>(56),
+ static_cast<_Float16>(57), static_cast<_Float16>(58),
+ static_cast<_Float16>(59), static_cast<_Float16>(60),
+ static_cast<_Float16>(61), static_cast<_Float16>(62),
+ static_cast<_Float16>(63), static_cast<_Float16>(64),
+ static_cast<_Float16>(65), static_cast<_Float16>(66),
+ static_cast<_Float16>(67), static_cast<_Float16>(68),
+ static_cast<_Float16>(69), static_cast<_Float16>(70),
+ static_cast<_Float16>(71), static_cast<_Float16>(72),
+ static_cast<_Float16>(73), static_cast<_Float16>(74),
+ static_cast<_Float16>(75), static_cast<_Float16>(76),
+ static_cast<_Float16>(77), static_cast<_Float16>(78),
+ static_cast<_Float16>(79), static_cast<_Float16>(80),
+ static_cast<_Float16>(82), static_cast<_Float16>(83),
+ static_cast<_Float16>(84), static_cast<_Float16>(85),
+ static_cast<_Float16>(86), static_cast<_Float16>(87),
+ static_cast<_Float16>(88), static_cast<_Float16>(89),
+ static_cast<_Float16>(90), static_cast<_Float16>(91),
+ static_cast<_Float16>(92), static_cast<_Float16>(93),
+ static_cast<_Float16>(94), static_cast<_Float16>(95),
+ static_cast<_Float16>(96), static_cast<_Float16>(97),
+ static_cast<_Float16>(98), static_cast<_Float16>(99),
+ static_cast<_Float16>(100), static_cast<_Float16>(101),
+ static_cast<_Float16>(102), static_cast<_Float16>(103),
+ static_cast<_Float16>(104), static_cast<_Float16>(105),
+ static_cast<_Float16>(106), static_cast<_Float16>(107),
+ static_cast<_Float16>(108), static_cast<_Float16>(109),
+ static_cast<_Float16>(110), static_cast<_Float16>(111),
+ static_cast<_Float16>(112), static_cast<_Float16>(113),
+ static_cast<_Float16>(114), static_cast<_Float16>(115),
+ static_cast<_Float16>(116), static_cast<_Float16>(117),
+ static_cast<_Float16>(118), static_cast<_Float16>(119),
+ static_cast<_Float16>(120), static_cast<_Float16>(121)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ m.add_i(1.0);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(1), static_cast<_Float16>(2),
+ static_cast<_Float16>(3), static_cast<_Float16>(4),
+ static_cast<_Float16>(5), static_cast<_Float16>(6),
+ static_cast<_Float16>(7), static_cast<_Float16>(8),
+ static_cast<_Float16>(9), static_cast<_Float16>(10),
+ static_cast<_Float16>(11), static_cast<_Float16>(12),
+ static_cast<_Float16>(13), static_cast<_Float16>(14),
+ static_cast<_Float16>(15), static_cast<_Float16>(16),
+ static_cast<_Float16>(17), static_cast<_Float16>(18),
+ static_cast<_Float16>(19), static_cast<_Float16>(20),
+ static_cast<_Float16>(21), static_cast<_Float16>(22),
+ static_cast<_Float16>(23), static_cast<_Float16>(24),
+ static_cast<_Float16>(25), static_cast<_Float16>(26),
+ static_cast<_Float16>(27), static_cast<_Float16>(28),
+ static_cast<_Float16>(29), static_cast<_Float16>(30),
+ static_cast<_Float16>(31), static_cast<_Float16>(32),
+ static_cast<_Float16>(33), static_cast<_Float16>(34),
+ static_cast<_Float16>(35), static_cast<_Float16>(36),
+ static_cast<_Float16>(37), static_cast<_Float16>(38),
+ static_cast<_Float16>(39), static_cast<_Float16>(40),
+ static_cast<_Float16>(41), static_cast<_Float16>(42),
+ static_cast<_Float16>(43), static_cast<_Float16>(44),
+ static_cast<_Float16>(45), static_cast<_Float16>(46),
+ static_cast<_Float16>(47), static_cast<_Float16>(48),
+ static_cast<_Float16>(49), static_cast<_Float16>(50),
+ static_cast<_Float16>(51), static_cast<_Float16>(52),
+ static_cast<_Float16>(53), static_cast<_Float16>(54),
+ static_cast<_Float16>(55), static_cast<_Float16>(56),
+ static_cast<_Float16>(57), static_cast<_Float16>(58),
+ static_cast<_Float16>(59), static_cast<_Float16>(60),
+ static_cast<_Float16>(61), static_cast<_Float16>(62),
+ static_cast<_Float16>(63), static_cast<_Float16>(64),
+ static_cast<_Float16>(65), static_cast<_Float16>(66),
+ static_cast<_Float16>(67), static_cast<_Float16>(68),
+ static_cast<_Float16>(69), static_cast<_Float16>(70),
+ static_cast<_Float16>(71), static_cast<_Float16>(72),
+ static_cast<_Float16>(73), static_cast<_Float16>(74),
+ static_cast<_Float16>(75), static_cast<_Float16>(76),
+ static_cast<_Float16>(77), static_cast<_Float16>(78),
+ static_cast<_Float16>(79), static_cast<_Float16>(80),
+ static_cast<_Float16>(81), static_cast<_Float16>(82),
+ static_cast<_Float16>(83), static_cast<_Float16>(84),
+ static_cast<_Float16>(85), static_cast<_Float16>(86),
+ static_cast<_Float16>(87), static_cast<_Float16>(88),
+ static_cast<_Float16>(89), static_cast<_Float16>(90),
+ static_cast<_Float16>(91), static_cast<_Float16>(92),
+ static_cast<_Float16>(93), static_cast<_Float16>(94),
+ static_cast<_Float16>(95), static_cast<_Float16>(96),
+ static_cast<_Float16>(97), static_cast<_Float16>(98),
+ static_cast<_Float16>(99), static_cast<_Float16>(100),
+ static_cast<_Float16>(101), static_cast<_Float16>(102),
+ static_cast<_Float16>(103), static_cast<_Float16>(104),
+ static_cast<_Float16>(105), static_cast<_Float16>(106),
+ static_cast<_Float16>(107), static_cast<_Float16>(108),
+ static_cast<_Float16>(109), static_cast<_Float16>(110),
+ static_cast<_Float16>(111), static_cast<_Float16>(112),
+ static_cast<_Float16>(113), static_cast<_Float16>(114),
+ static_cast<_Float16>(115), static_cast<_Float16>(116),
+ static_cast<_Float16>(117), static_cast<_Float16>(118),
+ static_cast<_Float16>(119), static_cast<_Float16>(120)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 5, 1, 4, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(3, 5, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 1, 1, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0), static_cast<_Float16>(2),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(4), static_cast<_Float16>(6),
+ static_cast<_Float16>(8), static_cast<_Float16>(10),
+ static_cast<_Float16>(8), static_cast<_Float16>(10),
+ static_cast<_Float16>(12), static_cast<_Float16>(14),
+ static_cast<_Float16>(12), static_cast<_Float16>(14),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(16), static_cast<_Float16>(18),
+ static_cast<_Float16>(20), static_cast<_Float16>(22),
+ static_cast<_Float16>(24), static_cast<_Float16>(26),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(28), static_cast<_Float16>(30),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(32), static_cast<_Float16>(34),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(36), static_cast<_Float16>(38),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(40), static_cast<_Float16>(42),
+ static_cast<_Float16>(44), static_cast<_Float16>(46),
+ static_cast<_Float16>(48), static_cast<_Float16>(50),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(52), static_cast<_Float16>(54),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(56), static_cast<_Float16>(58),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(60), static_cast<_Float16>(62),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(64), static_cast<_Float16>(66),
+ static_cast<_Float16>(68), static_cast<_Float16>(70)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(1, 1, 2, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(1, 1, 2, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 1, 2, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {static_cast<_Float16>(0.0),
+ static_cast<_Float16>(2.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(16, 1, 1, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor t =
+ ranged(16, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(1, 1, 1, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ static_cast<_Float16>(0.0), static_cast<_Float16>(1.0),
+ static_cast<_Float16>(2.0), static_cast<_Float16>(3.0),
+ static_cast<_Float16>(4.0), static_cast<_Float16>(5.0),
+ static_cast<_Float16>(6.0), static_cast<_Float16>(7.0),
+ static_cast<_Float16>(8.0), static_cast<_Float16>(9.0),
+ static_cast<_Float16>(10.0), static_cast<_Float16>(11.0),
+ static_cast<_Float16>(12.0), static_cast<_Float16>(13.0),
+ static_cast<_Float16>(14.0), static_cast<_Float16>(15.0)};
+ nntrainer::Tensor answer(ref_dim, answer_data);
+ int status = t.add_i(m);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+ EXPECT_EQ(t, answer);
+ }
+}
-// TEST(nntrainer_Tensor, add_i_broadcast_not_supported_01_n) {
-// nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, add_i_broadcast_not_supported_01_n) {
+ nntrainer::Tensor target(3, 1, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 1, 3, 3, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(target.add_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+ EXPECT_EQ(target.add_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// TEST(nntrainer_Tensor, add_i_broadcast_not_broadcastable_02_n) {
-// nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, add_i_broadcast_not_broadcastable_02_n) {
+ nntrainer::Tensor target(3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target2(3, 2, 3, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(target.add_i(target2), ML_ERROR_INVALID_PARAMETER);
-// }
+ EXPECT_EQ(target.add_i(target2), ML_ERROR_INVALID_PARAMETER);
+}
-// TEST(nntrainer_Tensor, add_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor result = input.add(1.0);
+ nntrainer::Tensor result = input.add(1.0);
-// _Float16 *data = result.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+ _Float16 *data = result.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// for (int i = 0; i < batch * height * width; ++i) {
-// if (data[i] != (_Float16)(indata[i] + (_Float16)1.0)) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// break;
-// }
-// }
+ for (int i = 0; i < batch * height * width; ++i) {
+ if (data[i] != (_Float16)(indata[i] + (_Float16)1.0)) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ break;
+ }
+ }
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// TEST(nntrainer_Tensor, add_02_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_02_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor result = input.add(input);
+ nntrainer::Tensor result = input.add(input);
-// _Float16 *data = result.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+ _Float16 *data = result.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// for (int i = 0; i < batch * height * width; ++i) {
-// if (data[i] != indata[i] + indata[i]) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// break;
-// }
-// }
+ for (int i = 0; i < batch * height * width; ++i) {
+ if (data[i] != indata[i] + indata[i]) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ break;
+ }
+ }
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// TEST(nntrainer_Tensor, add_03_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_03_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// EXPECT_THROW({ input.add(test); }, std::invalid_argument);
-// }
+ EXPECT_THROW({ input.add(test); }, std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, add_04_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_04_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(batch, channel, height, 2 * width);
-// nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
-// nntrainer::Tensor test(dim);
+ nntrainer::Tensor input(batch, channel, height, 2 * width);
+ nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
+ nntrainer::Tensor test(dim);
-// EXPECT_THROW(shared_input.add(test), std::invalid_argument);
-// }
+ EXPECT_THROW(shared_input.add(test), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, add_05_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_05_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(dim);
-// nntrainer::Tensor test(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
+ nntrainer::Tensor input(dim);
+ nntrainer::Tensor test(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
-// EXPECT_THROW(input.add(shared_test), std::invalid_argument);
-// }
+ EXPECT_THROW(input.add(shared_test), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, add_06_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_06_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(dim, false);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor input(dim, false);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
-// EXPECT_THROW(input.add(test), std::invalid_argument);
-// }
+ EXPECT_THROW(input.add(test), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, add_07_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_07_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim, false);
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim, false);
-// EXPECT_THROW(input.add(test), std::invalid_argument);
-// }
+ EXPECT_THROW(input.add(test), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, add_08_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, add_08_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
-// nntrainer::Tensor output(dim, false);
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
+ nntrainer::Tensor output(dim, false);
-// EXPECT_THROW(input.add(test, output), std::invalid_argument);
-// }
+ EXPECT_THROW(input.add(test, output), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, pow_01_p) {
+TEST(nntrainer_Tensor, pow_01_p) {
-// nntrainer::Tensor input = constant(4.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor input = constant(4.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor actual, expected;
+ nntrainer::Tensor actual, expected;
-// actual = input.pow(0.5f);
-// expected = constant(2.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+ actual = input.pow(0.5f);
+ expected = constant(2.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
-// actual = input.pow(2.0f);
-// expected = constant(16.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+ actual = input.pow(2.0f);
+ expected = constant(16.0, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
-// actual = input.pow(-0.5f);
-// expected = constant(0.5, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
-// }
+ actual = input.pow(-0.5f);
+ expected = constant(0.5, 3, 2, 4, 5, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+}
-// // TEST(nntrainer_Tensor, erf_01_p) {
-// // int batch = 1;
-// // int channel = 1;
-// // int height = 2;
-// // int width = 2;
-
-// // nntrainer::TensorDim dim(batch, channel, height, width,
-// // nntrainer::Tformat::NCHW,
-// // nntrainer::Tdatatype::FP16);
-
-// // nntrainer::Tensor input(dim);
-// // GEN_TEST_INPUT(input, k + l * 0.5 + 0.5);
-// // nntrainer::Tensor actual = input.erf();
-// // nntrainer::Tensor expected(
-// // std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// // {{{{0.5205, 0.8427}, {0.966105, 0.995322}}}}),
-// // dim.getTensorType());
-
-// // EXPECT_EQ(actual, expected);
-// // }
-
-// TEST(nntrainer_Tensor, subtract_i_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
+// TEST(nntrainer_Tensor, erf_01_p) {
+// int batch = 1;
// int channel = 1;
+// int height = 2;
+// int width = 2;
-// nntrainer::Tensor target(batch, channel, height, width,
+// nntrainer::TensorDim dim(batch, channel, height, width,
// nntrainer::Tformat::NCHW,
// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
-// nntrainer::Tensor original(batch, height, width, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// original.copy(target);
-
-// status = target.subtract_i(2.1);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-
-// _Float16 *previous = original.getData<_Float16>();
-// ASSERT_NE(nullptr, previous);
-// _Float16 *data = target.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
+// nntrainer::Tensor input(dim);
+// GEN_TEST_INPUT(input, k + l * 0.5 + 0.5);
+// nntrainer::Tensor actual = input.erf();
+// nntrainer::Tensor expected(
+// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+// {{{{0.5205, 0.8427}, {0.966105, 0.995322}}}}),
+// dim.getTensorType());
-// for (int i = 0; i < batch * height * width; ++i) {
-// EXPECT_FLOAT_EQ(data[i], ((_Float16)(previous[i] - (_Float16)2.1)));
-// }
+// EXPECT_EQ(actual, expected);
// }
-// TEST(nntrainer_Tensor, subtract_i_02_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
-// int channel = 1;
+TEST(nntrainer_Tensor, subtract_i_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// nntrainer::Tensor target(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
-// status = target.subtract_i(target);
-// EXPECT_EQ(status, ML_ERROR_NONE);
+ nntrainer::Tensor original(batch, height, width, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ original.copy(target);
-// _Float16 *data = target.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
+ status = target.subtract_i(2.1);
+ EXPECT_EQ(status, ML_ERROR_NONE);
-// for (int i = 0; i < batch * height * width; ++i) {
-// EXPECT_FLOAT_EQ(data[i], 0);
-// }
-// }
+ _Float16 *previous = original.getData<_Float16>();
+ ASSERT_NE(nullptr, previous);
+ _Float16 *data = target.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
-// TEST(nntrainer_Tensor, subtract_i_03_n) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int height = 3;
-// int width = 10;
-// int channel = 1;
+ for (int i = 0; i < batch * height * width; ++i) {
+ EXPECT_FLOAT_EQ(data[i], ((_Float16)(previous[i] - (_Float16)2.1)));
+ }
+}
-// nntrainer::Tensor target(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
+TEST(nntrainer_Tensor, subtract_i_02_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// nntrainer::Tensor target2(batch, channel, height - 1, width - 3,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
-// status = target.subtract_i(target2);
-// EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
-// }
+ status = target.subtract_i(target);
+ EXPECT_EQ(status, ML_ERROR_NONE);
-// TEST(nntrainer_Tensor, subtract_01_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ _Float16 *data = target.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ for (int i = 0; i < batch * height * width; ++i) {
+ EXPECT_FLOAT_EQ(data[i], 0);
+ }
+}
-// nntrainer::Tensor result = input.subtract(1.0);
+TEST(nntrainer_Tensor, subtract_i_03_n) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int height = 3;
+ int width = 10;
+ int channel = 1;
-// _Float16 *data = result.getData<_Float16>();
-// ASSERT_NE(nullptr, data);
-// _Float16 *indata = input.getData<_Float16>();
-// ASSERT_NE(nullptr, indata);
+ nntrainer::Tensor target(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(target, i * (batch * height) + j * (width) + k + 1 + channel);
-// for (int i = 0; i < batch * height * width; ++i) {
-// if (data[i] != indata[i] - 1.0) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// break;
-// }
-// }
+ nntrainer::Tensor target2(batch, channel, height - 1, width - 3,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+ status = target.subtract_i(target2);
+ EXPECT_EQ(status, ML_ERROR_INVALID_PARAMETER);
+}
-// TEST(nntrainer_Tensor, subtract_02_p) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+TEST(nntrainer_Tensor, subtract_01_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor input(batch, channel, height, width);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor result = input.subtract(input);
+ nntrainer::Tensor result = input.subtract(1.0);
-// EXPECT_EQ(constant(0.0, batch, channel, height, width), result);
-// }
+ _Float16 *data = result.getData<_Float16>();
+ ASSERT_NE(nullptr, data);
+ _Float16 *indata = input.getData<_Float16>();
+ ASSERT_NE(nullptr, indata);
-// TEST(nntrainer_Tensor, subtract_03_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ for (int i = 0; i < batch * height * width; ++i) {
+ if (data[i] != indata[i] - 1.0) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ break;
+ }
+ }
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+TEST(nntrainer_Tensor, subtract_02_p) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW({ input.subtract(test); }, std::invalid_argument);
-// }
+ nntrainer::Tensor input(batch, channel, height, width);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, subtract_04_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor result = input.subtract(input);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(constant(0.0, batch, channel, height, width), result);
+}
-// nntrainer::Tensor input(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
-// nntrainer::Tensor test(dim);
+TEST(nntrainer_Tensor, subtract_03_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(shared_input.subtract(test), std::invalid_argument);
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, subtract_05_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor test(batch - 1, channel, height - 1, width - 1,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW({ input.subtract(test); }, std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// nntrainer::Tensor test(batch, channel, height, 2 * width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
+TEST(nntrainer_Tensor, subtract_04_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.subtract(shared_test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, subtract_06_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_input = input.getSharedDataTensor(dim, 0, false);
+ nntrainer::Tensor test(dim);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(shared_input.subtract(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim, false);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
+TEST(nntrainer_Tensor, subtract_05_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.subtract(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, subtract_07_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ nntrainer::Tensor test(batch, channel, height, 2 * width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor shared_test = test.getSharedDataTensor(dim, 0, false);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(input.subtract(shared_test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim, false);
+TEST(nntrainer_Tensor, subtract_06_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.subtract(test), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, subtract_08_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim, false);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::TensorDim dim(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(input.subtract(test), std::invalid_argument);
+}
-// nntrainer::Tensor input(dim);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// nntrainer::Tensor test(dim);
-// GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
-// nntrainer::Tensor output(dim, false);
+TEST(nntrainer_Tensor, subtract_07_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW(input.subtract(test, output), std::invalid_argument);
-// }
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// TEST(nntrainer_Tensor, subtract__Float16_01_p) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim, false);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ EXPECT_THROW(input.subtract(test), std::invalid_argument);
+}
-// nntrainer::Tensor expected(batch, channel, height, width,
-// nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(expected, i * (batch * height) + j * (width) + k);
+TEST(nntrainer_Tensor, subtract_08_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::Tensor result = input.subtract(1.0);
+ nntrainer::TensorDim dim(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(result, expected);
-// }
+ nntrainer::Tensor input(dim);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
+ nntrainer::Tensor test(dim);
+ GEN_TEST_INPUT(test, i * (batch * height) + j * (width) + k + 2);
+ nntrainer::Tensor output(dim, false);
-// TEST(nntrainer_Tensor, sum_01_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ EXPECT_THROW(input.subtract(test, output), std::invalid_argument);
+}
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+TEST(nntrainer_Tensor, subtract__Float16_01_p) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// EXPECT_THROW({ input.sum(4); }, std::out_of_range);
-// }
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k + 1);
-// TEST(nntrainer_Tensor, sum_02_n) {
-// int batch = 3;
-// int channel = 1;
-// int height = 3;
-// int width = 10;
+ nntrainer::Tensor expected(batch, channel, height, width,
+ nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(expected, i * (batch * height) + j * (width) + k);
-// nntrainer::Tensor input(batch, channel, height, width,
-// nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
+ nntrainer::Tensor result = input.subtract(1.0);
-// EXPECT_THROW({ input.sum(-1); }, std::out_of_range);
-// }
+ EXPECT_EQ(result, expected);
+}
-// TEST(nntrainer_Tensor, sum_02_p) {
-// int batch = 3;
-// int channel = 2;
-// int height = 2;
-// int width = 10;
+TEST(nntrainer_Tensor, sum_01_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// nntrainer::Tensor ans0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{39, 42, 45, 48, 51, 54, 57, 60, 63, 66},
-// {69, 72, 75, 78, 81, 84, 87, 90, 93, 96}},
-// {{57, 60, 63, 66, 69, 72, 75, 78, 81, 84},
-// {87, 90, 93, 96, 99, 102, 105, 108, 111, 114}}}}),
-// t_type);
+ EXPECT_THROW({ input.sum(4); }, std::out_of_range);
+}
-// nntrainer::Tensor ans1(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{8, 10, 12, 14, 16, 18, 20, 22, 24, 26},
-// {28, 30, 32, 34, 36, 38, 40, 42, 44, 46}}},
-// {{{32, 34, 36, 38, 40, 42, 44, 46, 48, 50},
-// {52, 54, 56, 58, 60, 62, 64, 66, 68, 70}}},
-// {{{56, 58, 60, 62, 64, 66, 68, 70, 72, 74},
-// {76, 78, 80, 82, 84, 86, 88, 90, 92, 94}}}}),
-// t_type);
-
-// nntrainer::Tensor ans2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{12, 14, 16, 18, 20, 22, 24, 26, 28, 30}},
-// {{24, 26, 28, 30, 32, 34, 36, 38, 40, 42}}},
-// {{{36, 38, 40, 42, 44, 46, 48, 50, 52, 54}},
-// {{48, 50, 52, 54, 56, 58, 60, 62, 64, 66}}},
-// {{{60, 62, 64, 66, 68, 70, 72, 74, 76, 78}},
-// {{72, 74, 76, 78, 80, 82, 84, 86, 88, 90}}}}),
-// t_type);
-
-// nntrainer::Tensor ans3(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{55}, {155}}, {{115}, {215}}},
-// {{{175}, {275}}, {{235}, {335}}},
-// {{{295}, {395}}, {{355}, {455}}}}),
-// t_type);
-
-// nntrainer::Tensor input(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(input, i * (batch * height * channel) + j * (batch * height) +
-// k * (width) + l + 1);
-
-// nntrainer::Tensor result0 = input.sum(0);
-// nntrainer::Tensor result1 = input.sum(1);
-// nntrainer::Tensor result2 = input.sum(2);
-// nntrainer::Tensor result3 = input.sum(3);
-
-// EXPECT_EQ(ans0, result0);
-// EXPECT_EQ(ans1, result1);
-// EXPECT_EQ(ans2, result2);
-// EXPECT_EQ(ans3, result3);
-// }
+TEST(nntrainer_Tensor, sum_02_n) {
+ int batch = 3;
+ int channel = 1;
+ int height = 3;
+ int width = 10;
-// TEST(nntrainer_Tensor, sum_03_p) {
-// const int batch = 3;
-// const int channel = 2;
-// const int height = 1;
-// const int width = 10;
+ nntrainer::Tensor input(batch, channel, height, width,
+ nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ GEN_TEST_INPUT(input, i * (batch * height) + j * (width) + k);
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ EXPECT_THROW({ input.sum(-1); }, std::out_of_range);
+}
-// nntrainer::Tensor input(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(input, i * (height * channel * width) + j * (height * width) +
-// k * (width) + l + 1);
-// // Test for alpha == 1 and beta == 0 and dimension of reduced axis == 1
-// {
-// nntrainer::Tensor ans_0_1_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{63, 66, 69, 72, 75, 78, 81, 84, 87, 90}},
-// {{93, 96, 99, 102, 105, 108, 111, 114, 117, 120}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_1_1_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{12, 14, 16, 18, 20, 22, 24, 26, 28, 30}}},
-// {{{52, 54, 56, 58, 60, 62, 64, 66, 68, 70}}},
-// {{{92, 94, 96, 98, 100, 102, 104, 106, 108, 110}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_2_1_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}},
-// {{11, 12, 13, 14, 15, 16, 17, 18, 19, 20}}},
-// {{{21, 22, 23, 24, 25, 26, 27, 28, 29, 30}},
-// {{31, 32, 33, 34, 35, 36, 37, 38, 39, 40}}},
-// {{{41, 42, 43, 44, 45, 46, 47, 48, 49, 50}},
-// {{51, 52, 53, 54, 55, 56, 57, 58, 59, 60}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_3_1_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{55}}, {{155}}}, {{{255}}, {{355}}}, {{{455}}, {{555}}}}),
-// t_type);
-
-// nntrainer::Tensor result_0_1_0 = input.sum(0, 1);
-// nntrainer::Tensor result_1_1_0 = input.sum(1, 1);
-// nntrainer::Tensor result_2_1_0 = input.sum(2, 1);
-// nntrainer::Tensor result_3_1_0 = input.sum(3, 1);
-
-// EXPECT_EQ(ans_0_1_0, result_0_1_0);
-// EXPECT_EQ(ans_1_1_0, result_1_1_0);
-// EXPECT_EQ(ans_2_1_0, result_2_1_0);
-// EXPECT_EQ(ans_3_1_0, result_3_1_0);
-// }
+TEST(nntrainer_Tensor, sum_02_p) {
+ int batch = 3;
+ int channel = 2;
+ int height = 2;
+ int width = 10;
-// // Test for alpha == 1 and beta == 2 and dimension of reduced axis == 1
-// {
-// nntrainer::Tensor ans_0_1_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{65, 70, 75, 80, 85, 90, 95, 100, 105, 110}},
-// {{115, 120, 125, 130, 135, 140, 145, 150, 155, 160}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_1_1_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{14, 18, 22, 26, 30, 34, 38, 42, 46, 50}}},
-// {{{74, 78, 82, 86, 90, 94, 98, 102, 106, 110}}},
-// {{{134, 138, 142, 146, 150, 154, 158, 162, 166, 170}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_2_1_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{3, 6, 9, 12, 15, 18, 21, 24, 27, 30}},
-// {{33, 36, 39, 42, 45, 48, 51, 54, 57, 60}}},
-// {{{63, 66, 69, 72, 75, 78, 81, 84, 87, 90}},
-// {{93, 96, 99, 102, 105, 108, 111, 114, 117, 120}}},
-// {{{123, 126, 129, 132, 135, 138, 141, 144, 147, 150}},
-// {{153, 156, 159, 162, 165, 168, 171, 174, 177, 180}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_3_1_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{57}}, {{159}}}, {{{261}}, {{363}}}, {{{465}}, {{567}}}}),
-// t_type);
-
-// nntrainer::Tensor output_0_1_2(1, channel, height, width, t_type);
-// {
-// const int batch = 1;
-// GEN_TEST_INPUT(output_0_1_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_1_1_2(batch, 1, height, width, t_type);
-// {
-// const int channel = 1;
-// GEN_TEST_INPUT(output_1_1_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_2_1_2(batch, channel, 1, width, t_type);
-// {
-// const int height = 1;
-// GEN_TEST_INPUT(output_2_1_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_3_1_2(batch, channel, height, 1, t_type);
-// {
-// const int width = 1;
-// GEN_TEST_INPUT(output_3_1_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor result_0_1_2 = input.sum(0, output_0_1_2, 1, 2);
-// nntrainer::Tensor result_1_1_2 = input.sum(1, output_1_1_2, 1, 2);
-// nntrainer::Tensor result_2_1_2 = input.sum(2, output_2_1_2, 1, 2);
-// nntrainer::Tensor result_3_1_2 = input.sum(3, output_3_1_2, 1, 2);
-
-// EXPECT_EQ(ans_0_1_2, result_0_1_2);
-// EXPECT_EQ(ans_1_1_2, result_1_1_2);
-// EXPECT_EQ(ans_2_1_2, result_2_1_2);
-// EXPECT_EQ(ans_3_1_2, result_3_1_2);
-// }
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+
+ nntrainer::Tensor ans0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{39, 42, 45, 48, 51, 54, 57, 60, 63, 66},
+ {69, 72, 75, 78, 81, 84, 87, 90, 93, 96}},
+ {{57, 60, 63, 66, 69, 72, 75, 78, 81, 84},
+ {87, 90, 93, 96, 99, 102, 105, 108, 111, 114}}}}),
+ t_type);
+
+ nntrainer::Tensor ans1(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{8, 10, 12, 14, 16, 18, 20, 22, 24, 26},
+ {28, 30, 32, 34, 36, 38, 40, 42, 44, 46}}},
+ {{{32, 34, 36, 38, 40, 42, 44, 46, 48, 50},
+ {52, 54, 56, 58, 60, 62, 64, 66, 68, 70}}},
+ {{{56, 58, 60, 62, 64, 66, 68, 70, 72, 74},
+ {76, 78, 80, 82, 84, 86, 88, 90, 92, 94}}}}),
+ t_type);
+
+ nntrainer::Tensor ans2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{12, 14, 16, 18, 20, 22, 24, 26, 28, 30}},
+ {{24, 26, 28, 30, 32, 34, 36, 38, 40, 42}}},
+ {{{36, 38, 40, 42, 44, 46, 48, 50, 52, 54}},
+ {{48, 50, 52, 54, 56, 58, 60, 62, 64, 66}}},
+ {{{60, 62, 64, 66, 68, 70, 72, 74, 76, 78}},
+ {{72, 74, 76, 78, 80, 82, 84, 86, 88, 90}}}}),
+ t_type);
+
+ nntrainer::Tensor ans3(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{55}, {155}}, {{115}, {215}}},
+ {{{175}, {275}}, {{235}, {335}}},
+ {{{295}, {395}}, {{355}, {455}}}}),
+ t_type);
+
+ nntrainer::Tensor input(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(input, i * (batch * height * channel) + j * (batch * height) +
+ k * (width) + l + 1);
+
+ nntrainer::Tensor result0 = input.sum(0);
+ nntrainer::Tensor result1 = input.sum(1);
+ nntrainer::Tensor result2 = input.sum(2);
+ nntrainer::Tensor result3 = input.sum(3);
+
+ EXPECT_EQ(ans0, result0);
+ EXPECT_EQ(ans1, result1);
+ EXPECT_EQ(ans2, result2);
+ EXPECT_EQ(ans3, result3);
+}
-// // Test for alpha == 2 and beta == 0
-// {
-// nntrainer::Tensor ans_0_2_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{126, 132, 138, 144, 150, 156, 162, 168, 174, 180}},
-// {{186, 192, 198, 204, 210, 216, 222, 228, 234, 240}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_1_2_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{24, 28, 32, 36, 40, 44, 48, 52, 56, 60}}},
-// {{{104, 108, 112, 116, 120, 124, 128, 132, 136, 140}}},
-// {{{184, 188, 192, 196, 200, 204, 208, 212, 216, 220}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_2_2_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{2, 4, 6, 8, 10, 12, 14, 16, 18, 20}},
-// {{22, 24, 26, 28, 30, 32, 34, 36, 38, 40}}},
-// {{{42, 44, 46, 48, 50, 52, 54, 56, 58, 60}},
-// {{62, 64, 66, 68, 70, 72, 74, 76, 78, 80}}},
-// {{{82, 84, 86, 88, 90, 92, 94, 96, 98, 100}},
-// {{102, 104, 106, 108, 110, 112, 114, 116, 118, 120}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_3_2_0(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{110}}, {{310}}}, {{{510}}, {{710}}}, {{{910}}, {{1110}}}}),
-// t_type);
-
-// nntrainer::Tensor result_0_2_0 = input.sum(0, 2);
-// nntrainer::Tensor result_1_2_0 = input.sum(1, 2);
-// nntrainer::Tensor result_2_2_0 = input.sum(2, 2);
-// nntrainer::Tensor result_3_2_0 = input.sum(3, 2);
-
-// EXPECT_EQ(ans_0_2_0, result_0_2_0);
-// EXPECT_EQ(ans_1_2_0, result_1_2_0);
-// EXPECT_EQ(ans_2_2_0, result_2_2_0);
-// EXPECT_EQ(ans_3_2_0, result_3_2_0);
-// }
+TEST(nntrainer_Tensor, sum_03_p) {
+ const int batch = 3;
+ const int channel = 2;
+ const int height = 1;
+ const int width = 10;
-// // Test for alpha == 2 and beta == 2
-// {
-// nntrainer::Tensor ans_0_2_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{128, 136, 144, 152, 160, 168, 176, 184, 192, 200}},
-// {{208, 216, 224, 232, 240, 248, 256, 264, 272, 280}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_1_2_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{26, 32, 38, 44, 50, 56, 62, 68, 74, 80}}},
-// {{{126, 132, 138, 144, 150, 156, 162, 168, 174, 180}}},
-// {{{226, 232, 238, 244, 250, 256, 262, 268, 274, 280}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_2_2_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{4, 8, 12, 16, 20, 24, 28, 32, 36, 40}},
-// {{44, 48, 52, 56, 60, 64, 68, 72, 76, 80}}},
-// {{{84, 88, 92, 96, 100, 104, 108, 112, 116, 120}},
-// {{124, 128, 132, 136, 140, 144, 148, 152, 156, 160}}},
-// {{{164, 168, 172, 176, 180, 184, 188, 192, 196, 200}},
-// {{204, 208, 212, 216, 220, 224, 228, 232, 236, 240}}}}),
-// t_type);
-
-// nntrainer::Tensor ans_3_2_2(
-// std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
-// {{{{112}}, {{314}}}, {{{516}}, {{718}}}, {{{920}}, {{1122}}}}),
-// t_type);
-
-// nntrainer::Tensor output_0_2_2(1, channel, height, width, t_type);
-// {
-// const int batch = 1;
-// GEN_TEST_INPUT(output_0_2_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_1_2_2(batch, 1, height, width, t_type);
-// {
-// const int channel = 1;
-// GEN_TEST_INPUT(output_1_2_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_2_2_2(batch, channel, 1, width, t_type);
-// {
-// const int height = 1;
-// GEN_TEST_INPUT(output_2_2_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor output_3_2_2(batch, channel, height, 1, t_type);
-// {
-// const int width = 1;
-// GEN_TEST_INPUT(output_3_2_2, i * (channel * height * width) +
-// j * (height * width) + k * (width) + l +
-// 1);
-// }
-// nntrainer::Tensor result_0_2_2 = input.sum(0, output_0_2_2, 2, 2);
-// nntrainer::Tensor result_1_2_2 = input.sum(1, output_1_2_2, 2, 2);
-// nntrainer::Tensor result_2_2_2 = input.sum(2, output_2_2_2, 2, 2);
-// nntrainer::Tensor result_3_2_2 = input.sum(3, output_3_2_2, 2, 2);
-
-// EXPECT_EQ(ans_0_2_2, result_0_2_2);
-// EXPECT_EQ(ans_1_2_2, result_1_2_2);
-// EXPECT_EQ(ans_2_2_2, result_2_2_2);
-// EXPECT_EQ(ans_3_2_2, result_3_2_2);
-// }
-// }
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, sum_04_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 2;
-// int height = 2;
-// int width = 10;
+ nntrainer::Tensor input(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(input, i * (height * channel * width) + j * (height * width) +
+ k * (width) + l + 1);
+ // Test for alpha == 1 and beta == 0 and dimension of reduced axis == 1
+ {
+ nntrainer::Tensor ans_0_1_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{63, 66, 69, 72, 75, 78, 81, 84, 87, 90}},
+ {{93, 96, 99, 102, 105, 108, 111, 114, 117, 120}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_1_1_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{12, 14, 16, 18, 20, 22, 24, 26, 28, 30}}},
+ {{{52, 54, 56, 58, 60, 62, 64, 66, 68, 70}}},
+ {{{92, 94, 96, 98, 100, 102, 104, 106, 108, 110}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_2_1_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}},
+ {{11, 12, 13, 14, 15, 16, 17, 18, 19, 20}}},
+ {{{21, 22, 23, 24, 25, 26, 27, 28, 29, 30}},
+ {{31, 32, 33, 34, 35, 36, 37, 38, 39, 40}}},
+ {{{41, 42, 43, 44, 45, 46, 47, 48, 49, 50}},
+ {{51, 52, 53, 54, 55, 56, 57, 58, 59, 60}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_3_1_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{55}}, {{155}}}, {{{255}}, {{355}}}, {{{455}}, {{555}}}}),
+ t_type);
+
+ nntrainer::Tensor result_0_1_0 = input.sum(0, 1);
+ nntrainer::Tensor result_1_1_0 = input.sum(1, 1);
+ nntrainer::Tensor result_2_1_0 = input.sum(2, 1);
+ nntrainer::Tensor result_3_1_0 = input.sum(3, 1);
+
+ EXPECT_EQ(ans_0_1_0, result_0_1_0);
+ EXPECT_EQ(ans_1_1_0, result_1_1_0);
+ EXPECT_EQ(ans_2_1_0, result_2_1_0);
+ EXPECT_EQ(ans_3_1_0, result_3_1_0);
+ }
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ // Test for alpha == 1 and beta == 2 and dimension of reduced axis == 1
+ {
+ nntrainer::Tensor ans_0_1_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{65, 70, 75, 80, 85, 90, 95, 100, 105, 110}},
+ {{115, 120, 125, 130, 135, 140, 145, 150, 155, 160}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_1_1_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{14, 18, 22, 26, 30, 34, 38, 42, 46, 50}}},
+ {{{74, 78, 82, 86, 90, 94, 98, 102, 106, 110}}},
+ {{{134, 138, 142, 146, 150, 154, 158, 162, 166, 170}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_2_1_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{3, 6, 9, 12, 15, 18, 21, 24, 27, 30}},
+ {{33, 36, 39, 42, 45, 48, 51, 54, 57, 60}}},
+ {{{63, 66, 69, 72, 75, 78, 81, 84, 87, 90}},
+ {{93, 96, 99, 102, 105, 108, 111, 114, 117, 120}}},
+ {{{123, 126, 129, 132, 135, 138, 141, 144, 147, 150}},
+ {{153, 156, 159, 162, 165, 168, 171, 174, 177, 180}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_3_1_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{57}}, {{159}}}, {{{261}}, {{363}}}, {{{465}}, {{567}}}}),
+ t_type);
+
+ nntrainer::Tensor output_0_1_2(1, channel, height, width, t_type);
+ {
+ const int batch = 1;
+ GEN_TEST_INPUT(output_0_1_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_1_1_2(batch, 1, height, width, t_type);
+ {
+ const int channel = 1;
+ GEN_TEST_INPUT(output_1_1_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_2_1_2(batch, channel, 1, width, t_type);
+ {
+ const int height = 1;
+ GEN_TEST_INPUT(output_2_1_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_3_1_2(batch, channel, height, 1, t_type);
+ {
+ const int width = 1;
+ GEN_TEST_INPUT(output_3_1_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor result_0_1_2 = input.sum(0, output_0_1_2, 1, 2);
+ nntrainer::Tensor result_1_1_2 = input.sum(1, output_1_1_2, 1, 2);
+ nntrainer::Tensor result_2_1_2 = input.sum(2, output_2_1_2, 1, 2);
+ nntrainer::Tensor result_3_1_2 = input.sum(3, output_3_1_2, 1, 2);
+
+ EXPECT_EQ(ans_0_1_2, result_0_1_2);
+ EXPECT_EQ(ans_1_1_2, result_1_1_2);
+ EXPECT_EQ(ans_2_1_2, result_2_1_2);
+ EXPECT_EQ(ans_3_1_2, result_3_1_2);
+ }
-// nntrainer::Tensor input(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(input, i * (batch * height * channel) + j * (height * width) +
-// k * width + l + 1);
+ // Test for alpha == 2 and beta == 0
+ {
+ nntrainer::Tensor ans_0_2_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{126, 132, 138, 144, 150, 156, 162, 168, 174, 180}},
+ {{186, 192, 198, 204, 210, 216, 222, 228, 234, 240}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_1_2_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{24, 28, 32, 36, 40, 44, 48, 52, 56, 60}}},
+ {{{104, 108, 112, 116, 120, 124, 128, 132, 136, 140}}},
+ {{{184, 188, 192, 196, 200, 204, 208, 212, 216, 220}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_2_2_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{2, 4, 6, 8, 10, 12, 14, 16, 18, 20}},
+ {{22, 24, 26, 28, 30, 32, 34, 36, 38, 40}}},
+ {{{42, 44, 46, 48, 50, 52, 54, 56, 58, 60}},
+ {{62, 64, 66, 68, 70, 72, 74, 76, 78, 80}}},
+ {{{82, 84, 86, 88, 90, 92, 94, 96, 98, 100}},
+ {{102, 104, 106, 108, 110, 112, 114, 116, 118, 120}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_3_2_0(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{110}}, {{310}}}, {{{510}}, {{710}}}, {{{910}}, {{1110}}}}),
+ t_type);
+
+ nntrainer::Tensor result_0_2_0 = input.sum(0, 2);
+ nntrainer::Tensor result_1_2_0 = input.sum(1, 2);
+ nntrainer::Tensor result_2_2_0 = input.sum(2, 2);
+ nntrainer::Tensor result_3_2_0 = input.sum(3, 2);
+
+ EXPECT_EQ(ans_0_2_0, result_0_2_0);
+ EXPECT_EQ(ans_1_2_0, result_1_2_0);
+ EXPECT_EQ(ans_2_2_0, result_2_2_0);
+ EXPECT_EQ(ans_3_2_0, result_3_2_0);
+ }
-// nntrainer::Tensor result = input.sum_by_batch();
-// if (result.getValue<_Float16>(0, 0, 0, 0) != 820 ||
-// result.getValue<_Float16>(1, 0, 0, 0) != 1300 ||
-// result.getValue<_Float16>(2, 0, 0, 0) != 1780)
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ // Test for alpha == 2 and beta == 2
+ {
+ nntrainer::Tensor ans_0_2_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{128, 136, 144, 152, 160, 168, 176, 184, 192, 200}},
+ {{208, 216, 224, 232, 240, 248, 256, 264, 272, 280}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_1_2_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{26, 32, 38, 44, 50, 56, 62, 68, 74, 80}}},
+ {{{126, 132, 138, 144, 150, 156, 162, 168, 174, 180}}},
+ {{{226, 232, 238, 244, 250, 256, 262, 268, 274, 280}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_2_2_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{4, 8, 12, 16, 20, 24, 28, 32, 36, 40}},
+ {{44, 48, 52, 56, 60, 64, 68, 72, 76, 80}}},
+ {{{84, 88, 92, 96, 100, 104, 108, 112, 116, 120}},
+ {{124, 128, 132, 136, 140, 144, 148, 152, 156, 160}}},
+ {{{164, 168, 172, 176, 180, 184, 188, 192, 196, 200}},
+ {{204, 208, 212, 216, 220, 224, 228, 232, 236, 240}}}}),
+ t_type);
+
+ nntrainer::Tensor ans_3_2_2(
+ std::vector<std::vector<std::vector<std::vector<_Float16>>>>(
+ {{{{112}}, {{314}}}, {{{516}}, {{718}}}, {{{920}}, {{1122}}}}),
+ t_type);
+
+ nntrainer::Tensor output_0_2_2(1, channel, height, width, t_type);
+ {
+ const int batch = 1;
+ GEN_TEST_INPUT(output_0_2_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_1_2_2(batch, 1, height, width, t_type);
+ {
+ const int channel = 1;
+ GEN_TEST_INPUT(output_1_2_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_2_2_2(batch, channel, 1, width, t_type);
+ {
+ const int height = 1;
+ GEN_TEST_INPUT(output_2_2_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor output_3_2_2(batch, channel, height, 1, t_type);
+ {
+ const int width = 1;
+ GEN_TEST_INPUT(output_3_2_2, i * (channel * height * width) +
+ j * (height * width) + k * (width) + l +
+ 1);
+ }
+ nntrainer::Tensor result_0_2_2 = input.sum(0, output_0_2_2, 2, 2);
+ nntrainer::Tensor result_1_2_2 = input.sum(1, output_1_2_2, 2, 2);
+ nntrainer::Tensor result_2_2_2 = input.sum(2, output_2_2_2, 2, 2);
+ nntrainer::Tensor result_3_2_2 = input.sum(3, output_3_2_2, 2, 2);
+
+ EXPECT_EQ(ans_0_2_2, result_0_2_2);
+ EXPECT_EQ(ans_1_2_2, result_1_2_2);
+ EXPECT_EQ(ans_2_2_2, result_2_2_2);
+ EXPECT_EQ(ans_3_2_2, result_3_2_2);
+ }
+}
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+TEST(nntrainer_Tensor, sum_04_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 2;
+ int height = 2;
+ int width = 10;
-// TEST(nntrainer_Tensor, multiple_sum_invalid_args_01_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor t = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(t.sum(std::vector<unsigned int>()), std::invalid_argument);
-// }
+ nntrainer::Tensor input(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(input, i * (batch * height * channel) + j * (height * width) +
+ k * width + l + 1);
-// TEST(nntrainer_Tensor, multiple_sum_out_of_range_n) {
-// nntrainer::Tensor t = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(t.sum({7}), std::out_of_range);
-// }
+ nntrainer::Tensor result = input.sum_by_batch();
+ if (result.getValue<_Float16>(0, 0, 0, 0) != 820 ||
+ result.getValue<_Float16>(1, 0, 0, 0) != 1300 ||
+ result.getValue<_Float16>(2, 0, 0, 0) != 1780)
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// TEST(nntrainer_Tensor, multiple_sum_p) {
-// nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor actual, expected;
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
-// actual = t.sum({0, 1});
-// expected = constant(2 * 3, 1, 1, 5, 7, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, multiple_sum_invalid_args_01_n) {
-// actual = t.sum({1, 2, 3});
-// expected = constant(3 * 5 * 7, 2, 1, 1, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+ nntrainer::Tensor t = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(t.sum(std::vector<unsigned int>()), std::invalid_argument);
+}
-// actual = t.sum({3, 1});
-// expected = constant(7 * 3, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, multiple_sum_out_of_range_n) {
+ nntrainer::Tensor t = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(t.sum({7}), std::out_of_range);
+}
-// actual = t.sum({3, 1}, 0.5);
-// expected = constant(7 * 3 * 0.5, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
-// }
+TEST(nntrainer_Tensor, multiple_sum_p) {
+ nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor actual, expected;
+
+ actual = t.sum({0, 1});
+ expected = constant(2 * 3, 1, 1, 5, 7, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.sum({1, 2, 3});
+ expected = constant(3 * 5 * 7, 2, 1, 1, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.sum({3, 1});
+ expected = constant(7 * 3, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.sum({3, 1}, 0.5);
+ expected = constant(7 * 3 * 0.5, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+}
+// @todo check later
// TEST(nntrainer_Tensor, average_p) {
// nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
// nntrainer::Tdatatype::FP16);
// EXPECT_EQ(actual, expected);
// int idx = 0;
-// t = t.apply([&](_Float16 in) { return idx++ % 2; });
+// t = t.apply(
+// (std::function<_Float16(_Float16)>)[&](_Float16 in) { return idx++ % 2; });
// actual = t.average();
// expected = constant(0.5, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
// EXPECT_EQ(actual, expected);
// }
-// TEST(nntrainer_Tensor, average_axis_p) {
-// nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// int idx = 0;
-// std::function<_Float16(_Float16)> f = [&](_Float16 in) { return idx++ % 2; };
-// t = t.apply(f);
+TEST(nntrainer_Tensor, average_axis_p) {
+ nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ int idx = 0;
+ std::function<_Float16(_Float16)> f = [&](_Float16 in) { return idx++ % 2; };
+ t = t.apply(f);
+
+ nntrainer::Tensor actual, expected;
+
+ actual = t.average(0);
+ expected = constant(0, 1, 2, 2, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16)
+ .apply(f);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average(1);
+ expected = constant(0, 2, 1, 2, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16)
+ .apply(f);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average(2);
+ expected = constant(0, 2, 2, 1, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16)
+ .apply(f);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average(3);
+ expected = constant(0.5, 2, 2, 2, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+}
-// nntrainer::Tensor actual, expected;
+TEST(nntrainer_Tensor, average_axis_out_of_range_01_n) {
+ nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(t.average(-1), std::out_of_range);
+}
-// actual = t.average(0);
-// expected = constant(0, 1, 2, 2, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16)
-// .apply(f);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, average_axis_out_of_range_02_n) {
+ nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(t.average(7), std::out_of_range);
+}
-// actual = t.average(1);
-// expected = constant(0, 2, 1, 2, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16)
-// .apply(f);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, average_multiple_axes_p) {
+ nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor actual, expected;
+
+ actual = t.average({0, 1, 2});
+ expected = constant(1.0, 1, 1, 1, 7, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average({0, 1, 2, 3});
+ expected = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average({3, 1});
+ expected = constant(1.0, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+
+ actual = t.average({3, 1, 1, 1, 3});
+ expected = constant(1.0, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_EQ(actual, expected);
+}
-// actual = t.average(2);
-// expected = constant(0, 2, 2, 1, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16)
-// .apply(f);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, average_multiple_axes_01_n) {
+ nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(t.average({5, 7}), std::out_of_range);
+}
-// actual = t.average(3);
-// expected = constant(0.5, 2, 2, 2, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
-// }
+TEST(nntrainer_Tensor, dot_01_n) {
-// TEST(nntrainer_Tensor, average_axis_out_of_range_01_n) {
-// nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(t.average(-1), std::out_of_range);
-// }
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, average_axis_out_of_range_02_n) {
-// nntrainer::Tensor t = constant(1.0, 2, 2, 2, 2, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(t.average(7), std::out_of_range);
-// }
+ nntrainer::Tensor input(2, 3, 4, 5, t_type);
+ nntrainer::Tensor m(1, 3, 4, 5, t_type);
+ EXPECT_THROW(nntrainer::Tensor result = input.dot(m), std::runtime_error);
+}
-// TEST(nntrainer_Tensor, average_multiple_axes_p) {
-// nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor actual, expected;
+TEST(nntrainer_Tensor, dot_02_n) {
-// actual = t.average({0, 1, 2});
-// expected = constant(1.0, 1, 1, 1, 7, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// actual = t.average({0, 1, 2, 3});
-// expected = constant(1.0, 1, 1, 1, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+ nntrainer::Tensor input(2, 3, 4, 5, t_type);
+ nntrainer::Tensor m(1, 3, 4, 5, t_type);
+ EXPECT_THROW(nntrainer::Tensor result = input.dot(m, true),
+ std::runtime_error);
+}
-// actual = t.average({3, 1});
-// expected = constant(1.0, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
+TEST(nntrainer_Tensor, dot_02_p) {
-// actual = t.average({3, 1, 1, 1, 3});
-// expected = constant(1.0, 2, 1, 5, 1, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(actual, expected);
-// }
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, average_multiple_axes_01_n) {
-// nntrainer::Tensor t = constant(1.0, 2, 3, 5, 7, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(t.average({5, 7}), std::out_of_range);
-// }
+ nntrainer::Tensor input(2, 3, 4, 5, t_type);
+ nntrainer::Tensor m(1, 3, 4, 5, t_type);
+ EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, false, true));
+}
-// TEST(nntrainer_Tensor, dot_01_n) {
+TEST(nntrainer_Tensor, dot_03_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor input(2, 3, 4, 5, t_type);
-// nntrainer::Tensor m(1, 3, 4, 5, t_type);
-// EXPECT_THROW(nntrainer::Tensor result = input.dot(m), std::runtime_error);
-// }
+ nntrainer::Tensor input(1, 3, 4, 5, t_type);
+ nntrainer::Tensor m(1, 3, 4, 5, t_type);
+ EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, true));
+}
-// TEST(nntrainer_Tensor, dot_02_n) {
+TEST(nntrainer_Tensor, dot_04_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor input(2, 3, 4, 5, t_type);
-// nntrainer::Tensor m(1, 3, 4, 5, t_type);
-// EXPECT_THROW(nntrainer::Tensor result = input.dot(m, true),
-// std::runtime_error);
-// }
+ nntrainer::Tensor input(2, 3, 4, 5, t_type);
+ nntrainer::Tensor m(1, 1, 4, 5, t_type);
+ EXPECT_THROW(nntrainer::Tensor result = input.dot(m), std::runtime_error);
+ EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, false, true));
+}
+#include <iostream>
+TEST(nntrainer_Tensor, dot_05_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 2;
+ int channel = 3;
+ int height = 4;
+ int width = 5;
+
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+
+ _Float16 ans[2][3][4][24] = {0};
+
+ nntrainer::Tensor input(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(input, i * (channel * width * height) + j * (height * width) +
+ k * (width) + l + 1);
+ nntrainer::Tensor weight(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(weight, i * (channel * width * height) + j * (height * width) +
+ k * (width) + l + 1);
+ weight.reshape({1, 1, 24, 5, t_type});
+
+ nntrainer::Tensor result = input.dot(weight, false, true);
+
+ for (int b = 0; b < batch; b++) {
+ for (int c = 0; c < channel; c++) {
+ for (int h = 0; h < height; h++) {
+ for (int k = 0; k < batch * channel * height; k++) {
+ ans[b][c][h][k] = 0;
+ for (int w = 0; w < width; w++) {
+ _Float16 val1 = input.getValue<_Float16>(b, c, h, w);
+ _Float16 val2 = weight.getValue<_Float16>(0, 0, k, w);
+ ans[b][c][h][k] += val1 * val2;
+ }
+ }
+ }
+ }
+ }
+
+ for (unsigned int i = 0; i < result.batch(); ++i) {
+ for (unsigned int c = 0; c < result.channel(); ++c) {
+ for (unsigned int j = 0; j < result.height(); ++j) {
+ for (unsigned int k = 0; k < result.width(); ++k) {
+ _Float16 val1 = ans[i][c][j][k];
+ _Float16 val2 = result.getValue<_Float16>(i, c, j, k);
+ if (val1 != val2) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ goto end_dot_01_p;
+ }
+ }
+ }
+ }
+ }
+end_dot_01_p:
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+TEST(nntrainer_Tensor, dot_06_p) {
+ int status = ML_ERROR_NONE;
+ int batch = 3;
+ int channel = 1;
+ int height = 1;
+ int width = 3;
+
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+
+ _Float16 ans[3][1][1][3] = {
+ {{{30, 36, 42}}}, {{{66, 81, 96}}}, {{{102, 126, 150}}}};
+
+ nntrainer::Tensor input(batch, channel, height, width, t_type);
+ GEN_TEST_INPUT(input, i * (channel * width * height) + j * (height * width) +
+ k * (width) + l + 1);
+
+ nntrainer::Tensor result = input.dot(input);
+
+ for (unsigned int i = 0; i < result.batch(); ++i) {
+ for (unsigned int j = 0; j < result.height(); ++j) {
+ for (unsigned int k = 0; k < result.width(); ++k) {
+ if (ans[i][0][j][k] != result.getValue<_Float16>(i, 0, j, k)) {
+ status = ML_ERROR_RESULT_OUT_OF_RANGE;
+ goto end_dot_01_p;
+ }
+ }
+ }
+ }
+end_dot_01_p:
+ EXPECT_EQ(status, ML_ERROR_NONE);
+}
+
+TEST(nntrainer_Tensor, dot_transpose_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
+ 92, 113, 134, 155, 128, 158, 188, 218};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
+ 92, 113, 134, 155, 128, 158, 188, 218};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
+ 92, 113, 134, 155, 128, 158, 188, 218};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
+ 92, 113, 134, 155, 128, 158, 188, 218};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13, 28, 40};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+}
+
+TEST(nntrainer_Tensor, dot_shortcuts_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5, 14};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5, 14};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5, 14};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5, 14};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5, 14, 23, 32};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
+ _Float16 answer_data[] = {5, 14, 23, 32};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5, 14, 23, 32};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
+ _Float16 answer_data[] = {5, 14, 23, 32};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
+ _Float16 answer_data[] = {20, 23, 26, 29};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
+ _Float16 answer_data[] = {10, 13};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, false);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, false, true);
+ EXPECT_EQ(ret, answer);
+ }
+ {
+ _Float16 a_data[] = {0, 1, 2};
+ nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
+ _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
+ nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
+ _Float16 answer_data[] = {10, 13};
+ nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
+ answer_data);
+ nntrainer::Tensor ret = a.dot(b, true, true);
+ EXPECT_EQ(ret, answer);
+ }
+}
-// TEST(nntrainer_Tensor, dot_02_p) {
+TEST(nntrainer_Tensor, transpose_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor input(2, 3, 4, 5, t_type);
-// nntrainer::Tensor m(1, 3, 4, 5, t_type);
-// EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, false, true));
-// }
+ /// plain transpose
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
+ 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
+ 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
+ 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+ 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
+ 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
+ 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+ 112, 113, 114, 115, 116, 117, 118, 119};
+ nntrainer::Tensor answer({3, 2, 4, 5, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("0:1:2");
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 3, 8,
+ 13, 18, 4, 9, 14, 19, 20, 25, 30, 35, 21, 26, 31, 36,
+ 22, 27, 32, 37, 23, 28, 33, 38, 24, 29, 34, 39, 40, 45,
+ 50, 55, 41, 46, 51, 56, 42, 47, 52, 57, 43, 48, 53, 58,
+ 44, 49, 54, 59, 60, 65, 70, 75, 61, 66, 71, 76, 62, 67,
+ 72, 77, 63, 68, 73, 78, 64, 69, 74, 79, 80, 85, 90, 95,
+ 81, 86, 91, 96, 82, 87, 92, 97, 83, 88, 93, 98, 84, 89,
+ 94, 99, 100, 105, 110, 115, 101, 106, 111, 116, 102, 107, 112, 117,
+ 103, 108, 113, 118, 104, 109, 114, 119};
+ nntrainer::Tensor answer({3, 2, 5, 4, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("0:2:1");
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 5, 6, 7, 8,
+ 9, 25, 26, 27, 28, 29, 10, 11, 12, 13, 14, 30, 31, 32,
+ 33, 34, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 40, 41,
+ 42, 43, 44, 60, 61, 62, 63, 64, 45, 46, 47, 48, 49, 65,
+ 66, 67, 68, 69, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74,
+ 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83,
+ 84, 100, 101, 102, 103, 104, 85, 86, 87, 88, 89, 105, 106, 107,
+ 108, 109, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 95, 96,
+ 97, 98, 99, 115, 116, 117, 118, 119};
+ nntrainer::Tensor answer({3, 4, 2, 5, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("1:0:2");
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 20, 1, 21, 2, 22, 3, 23, 4, 24, 5, 25, 6, 26, 7, 27,
+ 8, 28, 9, 29, 10, 30, 11, 31, 12, 32, 13, 33, 14, 34, 15, 35,
+ 16, 36, 17, 37, 18, 38, 19, 39, 40, 60, 41, 61, 42, 62, 43, 63,
+ 44, 64, 45, 65, 46, 66, 47, 67, 48, 68, 49, 69, 50, 70, 51, 71,
+ 52, 72, 53, 73, 54, 74, 55, 75, 56, 76, 57, 77, 58, 78, 59, 79,
+ 80, 100, 81, 101, 82, 102, 83, 103, 84, 104, 85, 105, 86, 106, 87, 107,
+ 88, 108, 89, 109, 90, 110, 91, 111, 92, 112, 93, 113, 94, 114, 95, 115,
+ 96, 116, 97, 117, 98, 118, 99, 119};
+ nntrainer::Tensor answer({3, 4, 5, 2, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("1:2:0");
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 5, 10, 15, 20, 25, 30, 35, 1, 6, 11, 16, 21, 26, 31,
+ 36, 2, 7, 12, 17, 22, 27, 32, 37, 3, 8, 13, 18, 23, 28,
+ 33, 38, 4, 9, 14, 19, 24, 29, 34, 39, 40, 45, 50, 55, 60,
+ 65, 70, 75, 41, 46, 51, 56, 61, 66, 71, 76, 42, 47, 52, 57,
+ 62, 67, 72, 77, 43, 48, 53, 58, 63, 68, 73, 78, 44, 49, 54,
+ 59, 64, 69, 74, 79, 80, 85, 90, 95, 100, 105, 110, 115, 81, 86,
+ 91, 96, 101, 106, 111, 116, 82, 87, 92, 97, 102, 107, 112, 117, 83,
+ 88, 93, 98, 103, 108, 113, 118, 84, 89, 94, 99, 104, 109, 114, 119};
+ nntrainer::Tensor answer({3, 5, 2, 4, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("2:0:1");
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 20, 5, 25, 10, 30, 15, 35, 1, 21, 6, 26, 11, 31, 16, 36,
+ 2, 22, 7, 27, 12, 32, 17, 37, 3, 23, 8, 28, 13, 33, 18, 38,
+ 4, 24, 9, 29, 14, 34, 19, 39, 40, 60, 45, 65, 50, 70, 55, 75,
+ 41, 61, 46, 66, 51, 71, 56, 76, 42, 62, 47, 67, 52, 72, 57, 77,
+ 43, 63, 48, 68, 53, 73, 58, 78, 44, 64, 49, 69, 54, 74, 59, 79,
+ 80, 100, 85, 105, 90, 110, 95, 115, 81, 101, 86, 106, 91, 111, 96, 116,
+ 82, 102, 87, 107, 92, 112, 97, 117, 83, 103, 88, 108, 93, 113, 98, 118,
+ 84, 104, 89, 109, 94, 114, 99, 119};
+ nntrainer::Tensor answer({3, 5, 4, 2, t_type}, answer_data);
+ nntrainer::Tensor m = t.transpose("2:1:0");
+ EXPECT_EQ(answer, m);
+ }
-// TEST(nntrainer_Tensor, dot_03_p) {
+ /// outplace transpose
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
+ 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
+ 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
+ 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+ 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
+ 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
+ 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+ 112, 113, 114, 115, 116, 117, 118, 119};
+ nntrainer::Tensor answer({3, 2, 4, 5, t_type}, answer_data);
+ t.transpose("0:1:2", m);
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 2, 5, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 3, 8,
+ 13, 18, 4, 9, 14, 19, 20, 25, 30, 35, 21, 26, 31, 36,
+ 22, 27, 32, 37, 23, 28, 33, 38, 24, 29, 34, 39, 40, 45,
+ 50, 55, 41, 46, 51, 56, 42, 47, 52, 57, 43, 48, 53, 58,
+ 44, 49, 54, 59, 60, 65, 70, 75, 61, 66, 71, 76, 62, 67,
+ 72, 77, 63, 68, 73, 78, 64, 69, 74, 79, 80, 85, 90, 95,
+ 81, 86, 91, 96, 82, 87, 92, 97, 83, 88, 93, 98, 84, 89,
+ 94, 99, 100, 105, 110, 115, 101, 106, 111, 116, 102, 107, 112, 117,
+ 103, 108, 113, 118, 104, 109, 114, 119};
+ nntrainer::Tensor answer({3, 2, 5, 4, t_type}, answer_data);
+ t.transpose("0:2:1", m);
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 4, 2, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 5, 6, 7, 8,
+ 9, 25, 26, 27, 28, 29, 10, 11, 12, 13, 14, 30, 31, 32,
+ 33, 34, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 40, 41,
+ 42, 43, 44, 60, 61, 62, 63, 64, 45, 46, 47, 48, 49, 65,
+ 66, 67, 68, 69, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74,
+ 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83,
+ 84, 100, 101, 102, 103, 104, 85, 86, 87, 88, 89, 105, 106, 107,
+ 108, 109, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 95, 96,
+ 97, 98, 99, 115, 116, 117, 118, 119};
+ nntrainer::Tensor answer({3, 4, 2, 5, t_type}, answer_data);
+ t.transpose("1:0:2", m);
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 4, 5, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 20, 1, 21, 2, 22, 3, 23, 4, 24, 5, 25, 6, 26, 7, 27,
+ 8, 28, 9, 29, 10, 30, 11, 31, 12, 32, 13, 33, 14, 34, 15, 35,
+ 16, 36, 17, 37, 18, 38, 19, 39, 40, 60, 41, 61, 42, 62, 43, 63,
+ 44, 64, 45, 65, 46, 66, 47, 67, 48, 68, 49, 69, 50, 70, 51, 71,
+ 52, 72, 53, 73, 54, 74, 55, 75, 56, 76, 57, 77, 58, 78, 59, 79,
+ 80, 100, 81, 101, 82, 102, 83, 103, 84, 104, 85, 105, 86, 106, 87, 107,
+ 88, 108, 89, 109, 90, 110, 91, 111, 92, 112, 93, 113, 94, 114, 95, 115,
+ 96, 116, 97, 117, 98, 118, 99, 119};
+ nntrainer::Tensor answer({3, 4, 5, 2, t_type}, answer_data);
+ t.transpose("1:2:0", m);
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 5, 2, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 5, 10, 15, 20, 25, 30, 35, 1, 6, 11, 16, 21, 26, 31,
+ 36, 2, 7, 12, 17, 22, 27, 32, 37, 3, 8, 13, 18, 23, 28,
+ 33, 38, 4, 9, 14, 19, 24, 29, 34, 39, 40, 45, 50, 55, 60,
+ 65, 70, 75, 41, 46, 51, 56, 61, 66, 71, 76, 42, 47, 52, 57,
+ 62, 67, 72, 77, 43, 48, 53, 58, 63, 68, 73, 78, 44, 49, 54,
+ 59, 64, 69, 74, 79, 80, 85, 90, 95, 100, 105, 110, 115, 81, 86,
+ 91, 96, 101, 106, 111, 116, 82, 87, 92, 97, 102, 107, 112, 117, 83,
+ 88, 93, 98, 103, 108, 113, 118, 84, 89, 94, 99, 104, 109, 114, 119};
+ nntrainer::Tensor answer({3, 5, 2, 4, t_type}, answer_data);
+ t.transpose("2:0:1", m);
+ EXPECT_EQ(answer, m);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor m =
+ ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 answer_data[] = {
+ 0, 20, 5, 25, 10, 30, 15, 35, 1, 21, 6, 26, 11, 31, 16, 36,
+ 2, 22, 7, 27, 12, 32, 17, 37, 3, 23, 8, 28, 13, 33, 18, 38,
+ 4, 24, 9, 29, 14, 34, 19, 39, 40, 60, 45, 65, 50, 70, 55, 75,
+ 41, 61, 46, 66, 51, 71, 56, 76, 42, 62, 47, 67, 52, 72, 57, 77,
+ 43, 63, 48, 68, 53, 73, 58, 78, 44, 64, 49, 69, 54, 74, 59, 79,
+ 80, 100, 85, 105, 90, 110, 95, 115, 81, 101, 86, 106, 91, 111, 96, 116,
+ 82, 102, 87, 107, 92, 112, 97, 117, 83, 103, 88, 108, 93, 113, 98, 118,
+ 84, 104, 89, 109, 94, 114, 99, 119};
+ nntrainer::Tensor answer({3, 5, 4, 2, t_type}, answer_data);
+ t.transpose("2:1:0", m);
+ EXPECT_EQ(answer, m);
+ }
+}
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, tranpose_dimension_not_match_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor input(1, 3, 4, 5, t_type);
-// nntrainer::Tensor m(1, 3, 4, 5, t_type);
-// EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, true));
-// }
+ nntrainer::Tensor a(3, 2, 4, 5, t_type);
+ nntrainer::Tensor b(3, 1, 2, 3, t_type);
-// TEST(nntrainer_Tensor, dot_04_n) {
+ EXPECT_THROW(a.transpose("0:1:2", b), std::invalid_argument);
+}
+// TEST(nntrainer_Tensor, DISABLED_set_01_p) {
// nntrainer::TensorDim::TensorType t_type;
// t_type.format = nntrainer::Tformat::NCHW;
// t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor input(2, 3, 4, 5, t_type);
-// nntrainer::Tensor m(1, 1, 4, 5, t_type);
-// EXPECT_THROW(nntrainer::Tensor result = input.dot(m), std::runtime_error);
-// EXPECT_NO_THROW(nntrainer::Tensor result = input.dot(m, false, true));
-// }
-// #include <iostream>
-// TEST(nntrainer_Tensor, dot_05_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 2;
-// int channel = 3;
-// int height = 4;
-// int width = 5;
+// nntrainer::Tensor tensor = nntrainer::Tensor(1, 1, 1, 1, t_type);
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+// tensor.setZero();
+// EXPECT_EQ(tensor.getValue<_Float16>(0, 0, 0, 0), 0.0);
-// _Float16 ans[2][3][4][24] = {0};
-
-// nntrainer::Tensor input(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(input, i * (channel * width * height) + j * (height * width) +
-// k * (width) + l + 1);
-// nntrainer::Tensor weight(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(weight, i * (channel * width * height) + j * (height * width) +
-// k * (width) + l + 1);
-// weight.reshape({1, 1, 24, 5, t_type});
-
-// nntrainer::Tensor result = input.dot(weight, false, true);
-
-// for (int b = 0; b < batch; b++) {
-// for (int c = 0; c < channel; c++) {
-// for (int h = 0; h < height; h++) {
-// for (int k = 0; k < batch * channel * height; k++) {
-// ans[b][c][h][k] = 0;
-// for (int w = 0; w < width; w++) {
-// _Float16 val1 = input.getValue<_Float16>(b, c, h, w);
-// _Float16 val2 = weight.getValue<_Float16>(0, 0, k, w);
-// ans[b][c][h][k] += val1 * val2;
-// }
-// }
-// }
-// }
-// }
+// tensor.setRandUniform(-0.5, 0.0);
+// std::cout << "val : " << (float)tensor.getValue<_Float16>(0, 0, 0, 0)
+// << std::endl;
-// for (unsigned int i = 0; i < result.batch(); ++i) {
-// for (unsigned int c = 0; c < result.channel(); ++c) {
-// for (unsigned int j = 0; j < result.height(); ++j) {
-// for (unsigned int k = 0; k < result.width(); ++k) {
-// _Float16 val1 = ans[i][c][j][k];
-// _Float16 val2 = result.getValue<_Float16>(i, c, j, k);
-// if (val1 != val2) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// goto end_dot_01_p;
-// }
-// }
-// }
-// }
-// }
-// end_dot_01_p:
-// EXPECT_EQ(status, ML_ERROR_NONE);
+// _Float16 val = tensor.getValue<_Float16>(0, 0, 0, 0);
+// EXPECT_TRUE(val >= -0.5 && val < 0);
// }
-// TEST(nntrainer_Tensor, dot_06_p) {
-// int status = ML_ERROR_NONE;
-// int batch = 3;
-// int channel = 1;
-// int height = 1;
-// int width = 3;
-
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, save_read_01_p) {
+ int batch = 3;
+ int channel = 4;
+ int height = 5;
+ int width = 6;
-// _Float16 ans[3][1][1][3] = {
-// {{{30, 36, 42}}}, {{{66, 81, 96}}}, {{{102, 126, 150}}}};
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor input(batch, channel, height, width, t_type);
-// GEN_TEST_INPUT(input, i * (channel * width * height) + j * (height * width) +
-// k * (width) + l + 1);
+ nntrainer::Tensor target(3, 4, 5, 6, t_type);
+ nntrainer::Tensor readed(3, 4, 5, 6, t_type);
-// nntrainer::Tensor result = input.dot(input);
+ GEN_TEST_INPUT(target, i * (channel * width * height) + j * (height * width) +
+ k * (width) + l + 1);
-// for (unsigned int i = 0; i < result.batch(); ++i) {
-// for (unsigned int j = 0; j < result.height(); ++j) {
-// for (unsigned int k = 0; k < result.width(); ++k) {
-// if (ans[i][0][j][k] != result.getValue<_Float16>(i, 0, j, k)) {
-// status = ML_ERROR_RESULT_OUT_OF_RANGE;
-// goto end_dot_01_p;
-// }
-// }
-// }
-// }
-// end_dot_01_p:
-// EXPECT_EQ(status, ML_ERROR_NONE);
-// }
+ std::ofstream save_file("save.bin", std::ios::out | std::ios::binary);
+ target.save(save_file);
+ save_file.close();
-// TEST(nntrainer_Tensor, dot_transpose_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
-// 92, 113, 134, 155, 128, 158, 188, 218};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
-// 92, 113, 134, 155, 128, 158, 188, 218};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
-// 92, 113, 134, 155, 128, 158, 188, 218};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92,
-// 92, 113, 134, 155, 128, 158, 188, 218};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29, 56, 68, 80, 92};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40, 46, 67, 64, 94};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13, 28, 40};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// }
+ std::ifstream read_file("save.bin");
+ readed.read(read_file);
+ read_file.close();
-// TEST(nntrainer_Tensor, dot_shortcuts_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5, 14};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5, 14};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 2, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5, 14};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 1, 4, 2, 5};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 2, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5, 14};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 2, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5, 14, 23, 32};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 1, t_type), b_data);
-// _Float16 answer_data[] = {5, 14, 23, 32};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 4, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5, 14, 23, 32};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 4, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 1, 3, t_type), b_data);
-// _Float16 answer_data[] = {5, 14, 23, 32};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 4, 1, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 4, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 4, 3, t_type), b_data);
-// _Float16 answer_data[] = {20, 23, 26, 29};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 4, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 1, 2, 3, 4, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 3, 2, t_type), b_data);
-// _Float16 answer_data[] = {10, 13};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, false);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 1, 3, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, false, true);
-// EXPECT_EQ(ret, answer);
-// }
-// {
-// _Float16 a_data[] = {0, 1, 2};
-// nntrainer::Tensor a(nntrainer::TensorDim(1, 1, 3, 1, t_type), a_data);
-// _Float16 b_data[] = {0, 2, 4, 1, 3, 5};
-// nntrainer::Tensor b(nntrainer::TensorDim(1, 1, 2, 3, t_type), b_data);
-// _Float16 answer_data[] = {10, 13};
-// nntrainer::Tensor answer(nntrainer::TensorDim(1, 1, 1, 2, t_type),
-// answer_data);
-// nntrainer::Tensor ret = a.dot(b, true, true);
-// EXPECT_EQ(ret, answer);
-// }
-// }
+ EXPECT_EQ(target, readed);
-// TEST(nntrainer_Tensor, transpose_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ int status = std::remove("save.bin");
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-
-// /// plain transpose
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-// 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
-// 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
-// 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
-// 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
-// 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
-// 112, 113, 114, 115, 116, 117, 118, 119};
-// nntrainer::Tensor answer({3, 2, 4, 5, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("0:1:2");
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 3, 8,
-// 13, 18, 4, 9, 14, 19, 20, 25, 30, 35, 21, 26, 31, 36,
-// 22, 27, 32, 37, 23, 28, 33, 38, 24, 29, 34, 39, 40, 45,
-// 50, 55, 41, 46, 51, 56, 42, 47, 52, 57, 43, 48, 53, 58,
-// 44, 49, 54, 59, 60, 65, 70, 75, 61, 66, 71, 76, 62, 67,
-// 72, 77, 63, 68, 73, 78, 64, 69, 74, 79, 80, 85, 90, 95,
-// 81, 86, 91, 96, 82, 87, 92, 97, 83, 88, 93, 98, 84, 89,
-// 94, 99, 100, 105, 110, 115, 101, 106, 111, 116, 102, 107, 112, 117,
-// 103, 108, 113, 118, 104, 109, 114, 119};
-// nntrainer::Tensor answer({3, 2, 5, 4, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("0:2:1");
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 5, 6, 7, 8,
-// 9, 25, 26, 27, 28, 29, 10, 11, 12, 13, 14, 30, 31, 32,
-// 33, 34, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 40, 41,
-// 42, 43, 44, 60, 61, 62, 63, 64, 45, 46, 47, 48, 49, 65,
-// 66, 67, 68, 69, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74,
-// 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83,
-// 84, 100, 101, 102, 103, 104, 85, 86, 87, 88, 89, 105, 106, 107,
-// 108, 109, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 95, 96,
-// 97, 98, 99, 115, 116, 117, 118, 119};
-// nntrainer::Tensor answer({3, 4, 2, 5, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("1:0:2");
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 20, 1, 21, 2, 22, 3, 23, 4, 24, 5, 25, 6, 26, 7, 27,
-// 8, 28, 9, 29, 10, 30, 11, 31, 12, 32, 13, 33, 14, 34, 15, 35,
-// 16, 36, 17, 37, 18, 38, 19, 39, 40, 60, 41, 61, 42, 62, 43, 63,
-// 44, 64, 45, 65, 46, 66, 47, 67, 48, 68, 49, 69, 50, 70, 51, 71,
-// 52, 72, 53, 73, 54, 74, 55, 75, 56, 76, 57, 77, 58, 78, 59, 79,
-// 80, 100, 81, 101, 82, 102, 83, 103, 84, 104, 85, 105, 86, 106, 87, 107,
-// 88, 108, 89, 109, 90, 110, 91, 111, 92, 112, 93, 113, 94, 114, 95, 115,
-// 96, 116, 97, 117, 98, 118, 99, 119};
-// nntrainer::Tensor answer({3, 4, 5, 2, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("1:2:0");
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 5, 10, 15, 20, 25, 30, 35, 1, 6, 11, 16, 21, 26, 31,
-// 36, 2, 7, 12, 17, 22, 27, 32, 37, 3, 8, 13, 18, 23, 28,
-// 33, 38, 4, 9, 14, 19, 24, 29, 34, 39, 40, 45, 50, 55, 60,
-// 65, 70, 75, 41, 46, 51, 56, 61, 66, 71, 76, 42, 47, 52, 57,
-// 62, 67, 72, 77, 43, 48, 53, 58, 63, 68, 73, 78, 44, 49, 54,
-// 59, 64, 69, 74, 79, 80, 85, 90, 95, 100, 105, 110, 115, 81, 86,
-// 91, 96, 101, 106, 111, 116, 82, 87, 92, 97, 102, 107, 112, 117, 83,
-// 88, 93, 98, 103, 108, 113, 118, 84, 89, 94, 99, 104, 109, 114, 119};
-// nntrainer::Tensor answer({3, 5, 2, 4, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("2:0:1");
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 20, 5, 25, 10, 30, 15, 35, 1, 21, 6, 26, 11, 31, 16, 36,
-// 2, 22, 7, 27, 12, 32, 17, 37, 3, 23, 8, 28, 13, 33, 18, 38,
-// 4, 24, 9, 29, 14, 34, 19, 39, 40, 60, 45, 65, 50, 70, 55, 75,
-// 41, 61, 46, 66, 51, 71, 56, 76, 42, 62, 47, 67, 52, 72, 57, 77,
-// 43, 63, 48, 68, 53, 73, 58, 78, 44, 64, 49, 69, 54, 74, 59, 79,
-// 80, 100, 85, 105, 90, 110, 95, 115, 81, 101, 86, 106, 91, 111, 96, 116,
-// 82, 102, 87, 107, 92, 112, 97, 117, 83, 103, 88, 108, 93, 113, 98, 118,
-// 84, 104, 89, 109, 94, 114, 99, 119};
-// nntrainer::Tensor answer({3, 5, 4, 2, t_type}, answer_data);
-// nntrainer::Tensor m = t.transpose("2:1:0");
-// EXPECT_EQ(answer, m);
-// }
+ ASSERT_EQ(status, 0);
+}
-// /// outplace transpose
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-// 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
-// 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
-// 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
-// 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
-// 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
-// 112, 113, 114, 115, 116, 117, 118, 119};
-// nntrainer::Tensor answer({3, 2, 4, 5, t_type}, answer_data);
-// t.transpose("0:1:2", m);
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 2, 5, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 3, 8,
-// 13, 18, 4, 9, 14, 19, 20, 25, 30, 35, 21, 26, 31, 36,
-// 22, 27, 32, 37, 23, 28, 33, 38, 24, 29, 34, 39, 40, 45,
-// 50, 55, 41, 46, 51, 56, 42, 47, 52, 57, 43, 48, 53, 58,
-// 44, 49, 54, 59, 60, 65, 70, 75, 61, 66, 71, 76, 62, 67,
-// 72, 77, 63, 68, 73, 78, 64, 69, 74, 79, 80, 85, 90, 95,
-// 81, 86, 91, 96, 82, 87, 92, 97, 83, 88, 93, 98, 84, 89,
-// 94, 99, 100, 105, 110, 115, 101, 106, 111, 116, 102, 107, 112, 117,
-// 103, 108, 113, 118, 104, 109, 114, 119};
-// nntrainer::Tensor answer({3, 2, 5, 4, t_type}, answer_data);
-// t.transpose("0:2:1", m);
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 4, 2, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 5, 6, 7, 8,
-// 9, 25, 26, 27, 28, 29, 10, 11, 12, 13, 14, 30, 31, 32,
-// 33, 34, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 40, 41,
-// 42, 43, 44, 60, 61, 62, 63, 64, 45, 46, 47, 48, 49, 65,
-// 66, 67, 68, 69, 50, 51, 52, 53, 54, 70, 71, 72, 73, 74,
-// 55, 56, 57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83,
-// 84, 100, 101, 102, 103, 104, 85, 86, 87, 88, 89, 105, 106, 107,
-// 108, 109, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, 95, 96,
-// 97, 98, 99, 115, 116, 117, 118, 119};
-// nntrainer::Tensor answer({3, 4, 2, 5, t_type}, answer_data);
-// t.transpose("1:0:2", m);
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 4, 5, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 20, 1, 21, 2, 22, 3, 23, 4, 24, 5, 25, 6, 26, 7, 27,
-// 8, 28, 9, 29, 10, 30, 11, 31, 12, 32, 13, 33, 14, 34, 15, 35,
-// 16, 36, 17, 37, 18, 38, 19, 39, 40, 60, 41, 61, 42, 62, 43, 63,
-// 44, 64, 45, 65, 46, 66, 47, 67, 48, 68, 49, 69, 50, 70, 51, 71,
-// 52, 72, 53, 73, 54, 74, 55, 75, 56, 76, 57, 77, 58, 78, 59, 79,
-// 80, 100, 81, 101, 82, 102, 83, 103, 84, 104, 85, 105, 86, 106, 87, 107,
-// 88, 108, 89, 109, 90, 110, 91, 111, 92, 112, 93, 113, 94, 114, 95, 115,
-// 96, 116, 97, 117, 98, 118, 99, 119};
-// nntrainer::Tensor answer({3, 4, 5, 2, t_type}, answer_data);
-// t.transpose("1:2:0", m);
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 5, 2, 4, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 5, 10, 15, 20, 25, 30, 35, 1, 6, 11, 16, 21, 26, 31,
-// 36, 2, 7, 12, 17, 22, 27, 32, 37, 3, 8, 13, 18, 23, 28,
-// 33, 38, 4, 9, 14, 19, 24, 29, 34, 39, 40, 45, 50, 55, 60,
-// 65, 70, 75, 41, 46, 51, 56, 61, 66, 71, 76, 42, 47, 52, 57,
-// 62, 67, 72, 77, 43, 48, 53, 58, 63, 68, 73, 78, 44, 49, 54,
-// 59, 64, 69, 74, 79, 80, 85, 90, 95, 100, 105, 110, 115, 81, 86,
-// 91, 96, 101, 106, 111, 116, 82, 87, 92, 97, 102, 107, 112, 117, 83,
-// 88, 93, 98, 103, 108, 113, 118, 84, 89, 94, 99, 104, 109, 114, 119};
-// nntrainer::Tensor answer({3, 5, 2, 4, t_type}, answer_data);
-// t.transpose("2:0:1", m);
-// EXPECT_EQ(answer, m);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor m =
-// ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 answer_data[] = {
-// 0, 20, 5, 25, 10, 30, 15, 35, 1, 21, 6, 26, 11, 31, 16, 36,
-// 2, 22, 7, 27, 12, 32, 17, 37, 3, 23, 8, 28, 13, 33, 18, 38,
-// 4, 24, 9, 29, 14, 34, 19, 39, 40, 60, 45, 65, 50, 70, 55, 75,
-// 41, 61, 46, 66, 51, 71, 56, 76, 42, 62, 47, 67, 52, 72, 57, 77,
-// 43, 63, 48, 68, 53, 73, 58, 78, 44, 64, 49, 69, 54, 74, 59, 79,
-// 80, 100, 85, 105, 90, 110, 95, 115, 81, 101, 86, 106, 91, 111, 96, 116,
-// 82, 102, 87, 107, 92, 112, 97, 117, 83, 103, 88, 108, 93, 113, 98, 118,
-// 84, 104, 89, 109, 94, 114, 99, 119};
-// nntrainer::Tensor answer({3, 5, 4, 2, t_type}, answer_data);
-// t.transpose("2:1:0", m);
-// EXPECT_EQ(answer, m);
-// }
-// }
+TEST(nntrainer_Tensor, save_read_01_n) {
+ int batch = 3;
+ int channel = 4;
+ int height = 5;
+ int width = 6;
-// TEST(nntrainer_Tensor, tranpose_dimension_not_match_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor a(3, 2, 4, 5, t_type);
-// nntrainer::Tensor b(3, 1, 2, 3, t_type);
+ nntrainer::Tensor target(3, 4, 5, 6, t_type);
+ nntrainer::Tensor readed(3, 4, 1, 1, t_type);
-// EXPECT_THROW(a.transpose("0:1:2", b), std::invalid_argument);
-// }
+ GEN_TEST_INPUT(target, i * (channel * width * height) + j * (height * width) +
+ k * (width) + l + 1);
-// // TEST(nntrainer_Tensor, set_01_p) {
-// // nntrainer::TensorDim::TensorType t_type;
-// // t_type.format = nntrainer::Tformat::NCHW;
-// // t_type.data_type = nntrainer::Tdatatype::FP16;
+ std::ofstream save_file("save.bin", std::ios::out | std::ios::binary);
+ target.save(save_file);
+ save_file.close();
-// // nntrainer::Tensor tensor = nntrainer::Tensor(1, 1, 1, 1, t_type);
+ std::ifstream read_file("save.bin");
+ readed.read(read_file);
+ read_file.close();
-// // tensor.setZero();
-// // EXPECT_EQ(tensor.getValue<_Float16>(0, 0, 0, 0), 0.0);
+ EXPECT_NE(target, readed);
-// // tensor.setRandUniform(-0.5, 0.0);
-// // std::cout << "val : " << tensor.getValue<_Float16>(0, 0, 0, 0) << std::endl;
+ int status = std::remove("save.bin");
-// // _Float16 val = tensor.getValue<_Float16>(0, 0, 0, 0);
-// // EXPECT_TRUE(val >= -0.5 && val < 0);
-// // }
+ ASSERT_EQ(status, 0);
+}
-// TEST(nntrainer_Tensor, save_read_01_p) {
-// int batch = 3;
-// int channel = 4;
-// int height = 5;
-// int width = 6;
+TEST(nntrainer_Tensor, copy_and_shares_variable_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor target(3, 4, 5, 6, t_type);
-// nntrainer::Tensor readed(3, 4, 5, 6, t_type);
+ nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor B = A.clone();
+ nntrainer::Tensor C = A;
-// GEN_TEST_INPUT(target, i * (channel * width * height) + j * (height * width) +
-// k * (width) + l + 1);
+ C.setValue(1, 1, 1, 1, 2.0f);
-// std::ofstream save_file("save.bin", std::ios::out | std::ios::binary);
-// target.save(save_file);
-// save_file.close();
+ EXPECT_EQ(A, C);
+ EXPECT_NE(B, C);
-// std::ifstream read_file("save.bin");
-// readed.read(read_file);
-// read_file.close();
+ C.reshape(nntrainer::TensorDim(3, 4, 6, 5, t_type));
+ EXPECT_EQ(A.getDim(), B.getDim());
+ EXPECT_NE(A.getDim(), C.getDim());
+}
-// EXPECT_EQ(target, readed);
+TEST(nntrainer_Tensor, reshape_n_01) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// int status = std::remove("save.bin");
+ nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
-// ASSERT_EQ(status, 0);
-// }
+ EXPECT_THROW(A.reshape(nntrainer::TensorDim(9, 9, 9, 9, t_type)),
+ std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, save_read_01_n) {
-// int batch = 3;
-// int channel = 4;
-// int height = 5;
-// int width = 6;
+TEST(nntrainer_Tensor, reshape_n_02) {
+ nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::TensorDim A_dim = A.getDim();
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ /** Changing the dim of a tensor only affects local copy of the dim */
+ A_dim.setTensorDim(1, 100);
+ EXPECT_EQ(A_dim.getTensorDim(1), 100u);
-// nntrainer::Tensor target(3, 4, 5, 6, t_type);
-// nntrainer::Tensor readed(3, 4, 1, 1, t_type);
+ nntrainer::TensorDim A_dim_2 = A.getDim();
+ EXPECT_EQ(A_dim_2.getTensorDim(1), 4u);
+}
-// GEN_TEST_INPUT(target, i * (channel * width * height) + j * (height * width) +
-// k * (width) + l + 1);
+TEST(nntrainer_Tensor, copy_and_reshape_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// std::ofstream save_file("save.bin", std::ios::out | std::ios::binary);
-// target.save(save_file);
-// save_file.close();
+ nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+ nntrainer::Tensor B = A;
+ nntrainer::Tensor C = A.clone();
-// std::ifstream read_file("save.bin");
-// readed.read(read_file);
-// read_file.close();
+ EXPECT_THROW(B.reshape(nntrainer::TensorDim(9, 9, 9, 9, t_type)),
+ std::invalid_argument);
+}
-// EXPECT_NE(target, readed);
+/// @note this test case demonstrates it is dangerous to use sharedConstTensor
+/// to const correct the inner data.
+TEST(nntrainer_Tensor, constructor_from_shared_const_ptr_shares_variable_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// int status = std::remove("save.bin");
+ nntrainer::sharedConstTensor A = MAKE_SHARED_TENSOR(constant(
+ 1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// ASSERT_EQ(status, 0);
-// }
+ nntrainer::Tensor B = *A;
+ nntrainer::Tensor C = A->clone();
-// TEST(nntrainer_Tensor, copy_and_shares_variable_p) {
+ B.setValue(2, 3, 4, 5, 2.0f);
+ EXPECT_EQ(*A, B);
+ EXPECT_NE(*A, C);
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ C.reshape(nntrainer::TensorDim(3, 4, 6, 5, t_type));
+ EXPECT_EQ(A->getDim(), B.getDim());
+ EXPECT_NE(A->getDim(), C.getDim());
+}
-// nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor B = A.clone();
-// nntrainer::Tensor C = A;
+TEST(nntrainer_Tensor, print_small_size) {
+ nntrainer::Tensor target = constant(1.0, 3, 1, 2, 3, nntrainer::Tformat::NCHW,
+ nntrainer::Tdatatype::FP16);
+
+ std::stringstream ss, expected;
+ ss << target;
+
+ expected << '<' << typeid(target).name() << " at " << &target << ">\n"
+ << "data addr: " << target.getData<_Float16>() << '\n'
+ << "Shape: 3:1:2:3 [ FP16 : NCHW ]\n"
+ << " 1 1 1 \n"
+ << " 1 1 1 \n"
+ << "\n"
+ << "-------\n"
+ << " 1 1 1 \n"
+ << " 1 1 1 \n"
+ << "\n"
+ << "-------\n"
+ << " 1 1 1 \n"
+ << " 1 1 1 \n"
+ << "\n"
+ << "-------\n";
+
+ EXPECT_EQ(ss.str(), expected.str());
+}
-// C.setValue(1, 1, 1, 1, 2.0f);
+TEST(nntrainer_Tensor, print_large_size) {
+ nntrainer::Tensor target = constant(
+ 1.2, 3, 10, 10, 10, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// EXPECT_EQ(A, C);
-// EXPECT_NE(B, C);
+ std::stringstream ss, expected;
-// C.reshape(nntrainer::TensorDim(3, 4, 6, 5, t_type));
-// EXPECT_EQ(A.getDim(), B.getDim());
-// EXPECT_NE(A.getDim(), C.getDim());
-// }
+ expected << '<' << typeid(target).name() << " at " << &target << ">\n"
+ << "data addr: " << target.getData<_Float16>() << '\n'
+ << "Shape: 3:10:10:10 [ FP16 : NCHW ]\n"
+ << "[1.2002 1.2002 1.2002 ... 1.2002 1.2002 1.2002]\n";
+ ss << target;
-// TEST(nntrainer_Tensor, reshape_n_01) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ EXPECT_EQ(ss.str(), expected.str());
+}
-// nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
+// TEST(nntrainer_Tensor, DISABLED_equation_test_01_p) {
+// nntrainer::Tensor a, b, c;
+// nntrainer::Tensor ret1, ret2;
-// EXPECT_THROW(A.reshape(nntrainer::TensorDim(9, 9, 9, 9, t_type)),
-// std::invalid_argument);
-// }
+// a = randUniform(4, 6, 7, 3, -100, 100);
+// b = randUniform(4, 6, 7, 3, -100, 100);
+// c = randUniform(4, 6, 7, 3, -100, 100);
-// TEST(nntrainer_Tensor, reshape_n_02) {
-// nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::TensorDim A_dim = A.getDim();
+// ret1 = a.subtract(b).multiply(c);
+// ret2 = a.multiply(c).subtract(b.multiply(c));
-// /** Changing the dim of a tensor only affects local copy of the dim */
-// A_dim.setTensorDim(1, 100);
-// EXPECT_EQ(A_dim.getTensorDim(1), 100u);
+// _Float16 *data1 = ret1.getData<_Float16>();
+// _Float16 *data2 = ret2.getData<_Float16>();
+// EXPECT_EQ(ret1, ret2);
-// nntrainer::TensorDim A_dim_2 = A.getDim();
-// EXPECT_EQ(A_dim_2.getTensorDim(1), 4u);
+// for (unsigned int i = 0; i < ret1.size(); ++i) {
+// EXPECT_FLOAT_EQ(data1[i], data2[i]);
+// }
// }
-// TEST(nntrainer_Tensor, copy_and_reshape_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, fill_p) {
+ /// same dimension, buffer size
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ nntrainer::Tensor target(3, 2, 4, 5, t_type);
+ // nntrainer::Tensor original = randUniform(3, 2, 4, 5, -1.0f, 1.0f);
+ nntrainer::Tensor original =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ target.fill(original, false);
-// nntrainer::Tensor A = constant(1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-// nntrainer::Tensor B = A;
-// nntrainer::Tensor C = A.clone();
+ EXPECT_EQ(target, original);
+ }
-// EXPECT_THROW(B.reshape(nntrainer::TensorDim(9, 9, 9, 9, t_type)),
-// std::invalid_argument);
-// }
+ /// same dimension, buffer size is different (not tested)
+ {
+ /// there is no way to make non contiguous tensor publicily yet
+ EXPECT_TRUE(true);
+ }
-// /// @note this test case demonstrates it is dangerous to use sharedConstTensor
-// /// to const correct the inner data.
-// TEST(nntrainer_Tensor, constructor_from_shared_const_ptr_shares_variable_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ /// uninitialized with initialized flag is true
+ {
+ nntrainer::Tensor target;
+ // nntrainer::Tensor original = randUniform(3, 2, 4, 5, -1.0f, 1.0f);
+ nntrainer::Tensor original =
+ ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ target.fill(original, true);
-// nntrainer::sharedConstTensor A = MAKE_SHARED_TENSOR(constant(
-// 1.0f, 3, 4, 5, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ EXPECT_EQ(target, original);
+ }
+}
-// nntrainer::Tensor B = *A;
-// nntrainer::Tensor C = A->clone();
+TEST(nntrainer_Tensor, fill_uninitialized_n) {
+ nntrainer::Tensor target;
+ // nntrainer::Tensor original = randUniform(3, 1, 2, 3, -1.0f, 1.0f);
+ nntrainer::Tensor original =
+ ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(target.fill(original, false), std::invalid_argument);
+}
-// B.setValue(2, 3, 4, 5, 2.0f);
-// EXPECT_EQ(*A, B);
-// EXPECT_NE(*A, C);
+TEST(nntrainer_Tensor, fill_different_dimension_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// C.reshape(nntrainer::TensorDim(3, 4, 6, 5, t_type));
-// EXPECT_EQ(A->getDim(), B.getDim());
-// EXPECT_NE(A->getDim(), C.getDim());
-// }
+ nntrainer::Tensor target(3, 1, 3, 2, t_type);
+ // nntrainer::Tensor original = randUniform(3, 1, 2, 3, -1.0f, 1.0f);
+ nntrainer::Tensor original =
+ ranged(3, 1, 2, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ EXPECT_THROW(target.fill(original, false), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, print_small_size) {
-// nntrainer::Tensor target = constant(1.0, 3, 1, 2, 3, nntrainer::Tformat::NCHW,
-// nntrainer::Tdatatype::FP16);
-
-// std::stringstream ss, expected;
-// ss << target;
-
-// expected << '<' << typeid(target).name() << " at " << &target << ">\n"
-// << "data addr: " << target.getData<_Float16>() << '\n'
-// << "Shape: 3:1:2:3 [ FP16 : NCHW ]\n"
-// << " 1 1 1 \n"
-// << " 1 1 1 \n"
-// << "\n"
-// << "-------\n"
-// << " 1 1 1 \n"
-// << " 1 1 1 \n"
-// << "\n"
-// << "-------\n"
-// << " 1 1 1 \n"
-// << " 1 1 1 \n"
-// << "\n"
-// << "-------\n";
-
-// EXPECT_EQ(ss.str(), expected.str());
+// TEST(nntrainer_Tensor, DISABLED_fill_non_contiguous_n) {
+// /// there is no way to make non contiguous tensor publicily yet
+// EXPECT_TRUE(false);
// }
-// TEST(nntrainer_Tensor, print_large_size) {
-// nntrainer::Tensor target = constant(
-// 1.2, 3, 10, 10, 10, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-
-// std::stringstream ss, expected;
-
-// expected << '<' << typeid(target).name() << " at " << &target << ">\n"
-// << "data addr: " << target.getData<_Float16>() << '\n'
-// << "Shape: 3:10:10:10 [ FP16 : NCHW ]\n"
-// << "[1.2002 1.2002 1.2002 ... 1.2002 1.2002 1.2002]\n";
-// ss << target;
-
-// EXPECT_EQ(ss.str(), expected.str());
+// TEST(nntrainer_Tensor, DISABLED_fill_different_buffer_size_n) {
+// /// there is no way to make same dimension, diffrent buffersized tensor
+// /// publicily yet
+// EXPECT_TRUE(false);
// }
-// // TEST(nntrainer_Tensor, DISABLED_equation_test_01_p) {
-// // nntrainer::Tensor a, b, c;
-// // nntrainer::Tensor ret1, ret2;
-
-// // a = randUniform(4, 6, 7, 3, -100, 100);
-// // b = randUniform(4, 6, 7, 3, -100, 100);
-// // c = randUniform(4, 6, 7, 3, -100, 100);
+TEST(nntrainer_Tensor, empty_01) {
+ nntrainer::Tensor t;
-// // ret1 = a.subtract(b).multiply(c);
-// // ret2 = a.multiply(c).subtract(b.multiply(c));
-
-// // _Float16 *data1 = ret1.getData<_Float16>();
-// // _Float16 *data2 = ret2.getData<_Float16>();
-// // EXPECT_EQ(ret1, ret2);
-
-// // for (unsigned int i = 0; i < ret1.size(); ++i) {
-// // EXPECT_FLOAT_EQ(data1[i], data2[i]);
-// // }
-// // }
-
-// TEST(nntrainer_Tensor, fill_p) {
-// /// same dimension, buffer size
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// nntrainer::Tensor target(3, 2, 4, 5, t_type);
-// // nntrainer::Tensor original = randUniform(3, 2, 4, 5, -1.0f, 1.0f);
-// nntrainer::Tensor original =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// target.fill(original, false);
-
-// EXPECT_EQ(target, original);
-// }
+ EXPECT_TRUE(t.empty());
+}
-// /// same dimension, buffer size is different (not tested)
-// {
-// /// there is no way to make non contiguous tensor publicily yet
-// EXPECT_TRUE(true);
-// }
+TEST(nntrainer_Tensor, empty_02) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// /// uninitialized with initialized flag is true
-// {
-// nntrainer::Tensor target;
-// // nntrainer::Tensor original = randUniform(3, 2, 4, 5, -1.0f, 1.0f);
-// nntrainer::Tensor original =
-// ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// target.fill(original, true);
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
-// EXPECT_EQ(target, original);
-// }
-// }
+ EXPECT_FALSE(t.empty());
+}
-// TEST(nntrainer_Tensor, fill_uninitialized_n) {
-// nntrainer::Tensor target;
-// // nntrainer::Tensor original = randUniform(3, 1, 2, 3, -1.0f, 1.0f);
-// nntrainer::Tensor original =
-// ranged(3, 5, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(target.fill(original, false), std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, empty_03) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, fill_different_dimension_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
-// nntrainer::Tensor target(3, 1, 3, 2, t_type);
-// // nntrainer::Tensor original = randUniform(3, 1, 2, 3, -1.0f, 1.0f);
-// nntrainer::Tensor original =
-// ranged(3, 1, 2, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// EXPECT_THROW(target.fill(original, false), std::invalid_argument);
-// }
+ EXPECT_FALSE(t.empty());
+}
-// // TEST(nntrainer_Tensor, DISABLED_fill_non_contiguous_n) {
-// // /// there is no way to make non contiguous tensor publicily yet
-// // EXPECT_TRUE(false);
-// // }
+TEST(nntrainer_Tensor, allocate_01_n) {
+ nntrainer::Tensor t;
+ EXPECT_FALSE(t.isAllocated());
-// // TEST(nntrainer_Tensor, DISABLED_fill_different_buffer_size_n) {
-// // /// there is no way to make same dimension, diffrent buffersized tensor
-// // /// publicily yet
-// // EXPECT_TRUE(false);
-// // }
+ t.allocate();
+ EXPECT_FALSE(t.isAllocated());
+}
-// TEST(nntrainer_Tensor, empty_01) {
-// nntrainer::Tensor t;
+TEST(nntrainer_Tensor, allocate_02_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// EXPECT_TRUE(t.empty());
-// }
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
+ EXPECT_FALSE(t.isAllocated());
-// TEST(nntrainer_Tensor, empty_02) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ t.allocate();
+ EXPECT_TRUE(t.isAllocated());
+}
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
+TEST(nntrainer_Tensor, allocate_03_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// EXPECT_FALSE(t.empty());
-// }
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
+ EXPECT_TRUE(t.isAllocated());
-// TEST(nntrainer_Tensor, empty_03) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ t.allocate();
+ EXPECT_TRUE(t.isAllocated());
+}
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
+TEST(nntrainer_Tensor, initialize_01_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// EXPECT_FALSE(t.empty());
-// }
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
+ nntrainer::Tensor::Initializer::ONES);
-// TEST(nntrainer_Tensor, allocate_01_n) {
-// nntrainer::Tensor t;
-// EXPECT_FALSE(t.isAllocated());
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1);
-// t.allocate();
-// EXPECT_FALSE(t.isAllocated());
-// }
+ EXPECT_EQ(golden, t);
+}
-// TEST(nntrainer_Tensor, allocate_02_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, initialize_02_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
-// EXPECT_FALSE(t.isAllocated());
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
-// t.allocate();
-// EXPECT_TRUE(t.isAllocated());
-// }
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1);
-// TEST(nntrainer_Tensor, allocate_03_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ EXPECT_NE(golden, t);
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
-// EXPECT_TRUE(t.isAllocated());
+ t.initialize(nntrainer::Tensor::Initializer::ONES);
+ EXPECT_EQ(golden, t);
+}
-// t.allocate();
-// EXPECT_TRUE(t.isAllocated());
-// }
+TEST(nntrainer_Tensor, initialize_03_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, initialize_01_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, false,
+ nntrainer::Tensor::Initializer::ONES);
+ t.allocate();
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
-// nntrainer::Tensor::Initializer::ONES);
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1);
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1);
+ EXPECT_EQ(golden, t);
+}
-// EXPECT_EQ(golden, t);
-// }
+TEST(nntrainer_Tensor, initialize_04_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, initialize_02_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
+ t.initialize(nntrainer::Tensor::Initializer::ONES);
+ t.allocate();
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true);
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1);
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1);
+ EXPECT_EQ(golden, t);
+}
-// EXPECT_NE(golden, t);
+TEST(nntrainer_Tensor, initialize_05_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// t.initialize(nntrainer::Tensor::Initializer::ONES);
-// EXPECT_EQ(golden, t);
-// }
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
+ t.allocate();
-// TEST(nntrainer_Tensor, initialize_03_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1.f);
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, false,
-// nntrainer::Tensor::Initializer::ONES);
-// t.allocate();
+ /**
+ * Ideally, it should be NE, but it can be equal due to no initialization
+ * EXPECT_NE(golden, t);
+ */
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1);
+ t.initialize(nntrainer::Tensor::Initializer::ONES);
+ EXPECT_EQ(golden, t);
+}
-// EXPECT_EQ(golden, t);
-// }
+TEST(nntrainer_Tensor, initialize_06_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, initialize_04_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
+ nntrainer::Tensor::Initializer::ONES);
+ nntrainer::Tensor golden({1, 2, 3, 4, t_type}, true,
+ nntrainer::Tensor::Initializer::ZEROS);
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
-// t.initialize(nntrainer::Tensor::Initializer::ONES);
-// t.allocate();
+ EXPECT_NE(golden, t);
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1);
+ golden.initialize(nntrainer::Tensor::Initializer::ONES);
+ EXPECT_EQ(golden, t);
+}
-// EXPECT_EQ(golden, t);
-// }
+TEST(nntrainer_Tensor, initialize_07_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, initialize_05_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
+ nntrainer::Tensor::Initializer::ONES);
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, false);
-// t.allocate();
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1);
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1.f);
+ EXPECT_EQ(golden, t);
-// /**
-// * Ideally, it should be NE, but it can be equal due to no initialization
-// * EXPECT_NE(golden, t);
-// */
+ t.setValue(0, 0, 0, 0, 0);
+ t.setValue(0, 0, 0, t.size() - 1, 0);
+ EXPECT_NE(golden, t);
-// t.initialize(nntrainer::Tensor::Initializer::ONES);
-// EXPECT_EQ(golden, t);
-// }
+ t.initialize();
+ EXPECT_EQ(golden, t);
+}
-// TEST(nntrainer_Tensor, initialize_06_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, initialize_08_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
-// nntrainer::Tensor::Initializer::ONES);
-// nntrainer::Tensor golden({1, 2, 3, 4, t_type}, true,
-// nntrainer::Tensor::Initializer::ZEROS);
+ nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
+ nntrainer::Tensor::Initializer::ONES);
-// EXPECT_NE(golden, t);
+ nntrainer::Tensor golden(1, 2, 3, 4, t_type);
+ golden.setValue(1.f);
+ EXPECT_EQ(golden, t);
-// golden.initialize(nntrainer::Tensor::Initializer::ONES);
-// EXPECT_EQ(golden, t);
-// }
+ /// @todo this test case is not valid anymore, since
+ /// std::uniform_real_distribution does not support _Float16
+ // t.initialize(nntrainer::Tensor::Initializer::HE_NORMAL);
+ // EXPECT_NE(golden, t);
+ // t.initialize();
+ // EXPECT_NE(golden, t);
-// TEST(nntrainer_Tensor, initialize_07_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ t.initialize(nntrainer::Tensor::Initializer::ONES);
+ EXPECT_EQ(golden, t);
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
-// nntrainer::Tensor::Initializer::ONES);
+ t.initialize();
+ EXPECT_EQ(golden, t);
+}
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1);
+TEST(nntrainer_Tensor, split_01_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(3);
+ {
+ _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
+ answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
+ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79};
+ answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
+ 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
+ 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split(3, 0), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+ 12, 13, 14, 15, 16, 17, 18, 19, 40, 41, 42, 43,
+ 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+ 56, 57, 58, 59, 80, 81, 82, 83, 84, 85, 86, 87,
+ 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99};
+ answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
+ 32, 33, 34, 35, 36, 37, 38, 39, 60, 61, 62, 63,
+ 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
+ 76, 77, 78, 79, 100, 101, 102, 103, 104, 105, 106, 107,
+ 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split(2, 1), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 20, 21, 22, 23, 24,
+ 25, 26, 27, 28, 29, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 80, 81, 82, 83, 84,
+ 85, 86, 87, 88, 89, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 30, 31, 32, 33, 34,
+ 35, 36, 37, 38, 39, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 90, 91, 92, 93, 94,
+ 95, 96, 97, 98, 99, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split(2, 2), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(5);
+ {
+ _Float16 answer_data[] = {0, 5, 10, 15, 20, 25, 30, 35,
+ 40, 45, 50, 55, 60, 65, 70, 75,
+ 80, 85, 90, 95, 100, 105, 110, 115};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {1, 6, 11, 16, 21, 26, 31, 36,
+ 41, 46, 51, 56, 61, 66, 71, 76,
+ 81, 86, 91, 96, 101, 106, 111, 116};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {2, 7, 12, 17, 22, 27, 32, 37,
+ 42, 47, 52, 57, 62, 67, 72, 77,
+ 82, 87, 92, 97, 102, 107, 112, 117};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {3, 8, 13, 18, 23, 28, 33, 38,
+ 43, 48, 53, 58, 63, 68, 73, 78,
+ 83, 88, 93, 98, 103, 108, 113, 118};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
+ 44, 49, 54, 59, 64, 69, 74, 79,
+ 84, 89, 94, 99, 104, 109, 114, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split(5, 3), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(1, 1, 4, 6, t_type);
+ nntrainer::Tensor t =
+ ranged(1, 1, 4, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {0, 1, 2, 6, 7, 8, 12, 13, 14, 18, 19, 20};
+ answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {3, 4, 5, 9, 10, 11, 15, 16, 17, 21, 22, 23};
+ answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split(2, 3), answer);
+ }
+}
-// EXPECT_EQ(golden, t);
+TEST(nntrainer_Tensor, split_02_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// t.setValue(0, 0, 0, 0, 0);
-// t.setValue(0, 0, 0, t.size() - 1, 0);
-// EXPECT_NE(golden, t);
+ nntrainer::Tensor t(1, 1, 1, 1, t_type);
+ EXPECT_THROW(t.split(0, 0), std::invalid_argument);
+}
-// t.initialize();
-// EXPECT_EQ(golden, t);
-// }
+TEST(nntrainer_Tensor, split_03_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, initialize_08_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t(3, 1, 1, 1, t_type);
+ EXPECT_THROW(t.split(2, 0), std::invalid_argument);
+}
-// nntrainer::Tensor t({1, 2, 3, 4, t_type}, true,
-// nntrainer::Tensor::Initializer::ONES);
+TEST(nntrainer_Tensor, split_04_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+ 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
+ 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
+ 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
+ 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79};
+ answer.emplace_back(ml::train::TensorDim{2, 2, 4, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
+ 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
+ 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({2, 1}, 0), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+ 12, 13, 14, 15, 16, 17, 18, 19, 40, 41, 42, 43,
+ 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+ 56, 57, 58, 59, 80, 81, 82, 83, 84, 85, 86, 87,
+ 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99};
+ answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
+ 32, 33, 34, 35, 36, 37, 38, 39, 60, 61, 62, 63,
+ 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
+ 76, 77, 78, 79, 100, 101, 102, 103, 104, 105, 106, 107,
+ 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({1, 1}, 1), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 20, 21, 22, 23, 24,
+ 25, 26, 27, 28, 29, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
+ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 80, 81, 82, 83, 84,
+ 85, 86, 87, 88, 89, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 30, 31, 32, 33, 34,
+ 35, 36, 37, 38, 39, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 90, 91, 92, 93, 94,
+ 95, 96, 97, 98, 99, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({2, 2}, 2), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(3);
+ {
+ _Float16 answer_data[] = {0, 5, 10, 15, 20, 25, 30, 35,
+ 40, 45, 50, 55, 60, 65, 70, 75,
+ 80, 85, 90, 95, 100, 105, 110, 115};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 1, 2, 3, 6, 7, 8, 11, 12, 13, 16, 17, 18, 21, 22, 23,
+ 26, 27, 28, 31, 32, 33, 36, 37, 38, 41, 42, 43, 46, 47, 48,
+ 51, 52, 53, 56, 57, 58, 61, 62, 63, 66, 67, 68, 71, 72, 73,
+ 76, 77, 78, 81, 82, 83, 86, 87, 88, 91, 92, 93, 96, 97, 98,
+ 101, 102, 103, 106, 107, 108, 111, 112, 113, 116, 117, 118};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 3, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
+ 44, 49, 54, 59, 64, 69, 74, 79,
+ 84, 89, 94, 99, 104, 109, 114, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({1, 3, 1}, 3), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(3);
+ {
+ _Float16 answer_data[] = {
+ 0, 1, 5, 6, 10, 11, 15, 16, 20, 21, 25, 26, 30, 31, 35, 36,
+ 40, 41, 45, 46, 50, 51, 55, 56, 60, 61, 65, 66, 70, 71, 75, 76,
+ 80, 81, 85, 86, 90, 91, 95, 96, 100, 101, 105, 106, 110, 111, 115, 116};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 2, 3, 7, 8, 12, 13, 17, 18, 22, 23, 27, 28, 32, 33, 37, 38,
+ 42, 43, 47, 48, 52, 53, 57, 58, 62, 63, 67, 68, 72, 73, 77, 78,
+ 82, 83, 87, 88, 92, 93, 97, 98, 102, 103, 107, 108, 112, 113, 117, 118};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
+ 44, 49, 54, 59, 64, 69, 74, 79,
+ 84, 89, 94, 99, 104, 109, 114, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({2, 2, 1}, 3), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
+ nntrainer::Tensor t =
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(2);
+ {
+ _Float16 answer_data[] = {
+ 0, 1, 5, 6, 10, 11, 15, 16, 20, 21, 25, 26, 30, 31, 35, 36,
+ 40, 41, 45, 46, 50, 51, 55, 56, 60, 61, 65, 66, 70, 71, 75, 76,
+ 80, 81, 85, 86, 90, 91, 95, 96, 100, 101, 105, 106, 110, 111, 115, 116};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {
+ 2, 3, 4, 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24,
+ 27, 28, 29, 32, 33, 34, 37, 38, 39, 42, 43, 44, 47, 48, 49,
+ 52, 53, 54, 57, 58, 59, 62, 63, 64, 67, 68, 69, 72, 73, 74,
+ 77, 78, 79, 82, 83, 84, 87, 88, 89, 92, 93, 94, 97, 98, 99,
+ 102, 103, 104, 107, 108, 109, 112, 113, 114, 117, 118, 119};
+ answer.emplace_back(ml::train::TensorDim{3, 2, 4, 3, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({2, 3}, 3), answer);
+ }
+ {
+ nntrainer::TensorDim ref_dim(1, 1, 4, 6, t_type);
+ nntrainer::Tensor t =
+ ranged(1, 1, 4, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ std::vector<nntrainer::Tensor> answer;
+ answer.reserve(3);
+ {
+ _Float16 answer_data[] = {0, 6, 12, 18};
+ answer.emplace_back(ml::train::TensorDim{1, 1, 4, 1, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {1, 2, 3, 7, 8, 9, 13, 14, 15, 19, 20, 21};
+ answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
+ answer_data);
+ }
+ {
+ _Float16 answer_data[] = {4, 5, 10, 11, 16, 17, 22, 23};
+ answer.emplace_back(ml::train::TensorDim{1, 1, 4, 2, t_type},
+ answer_data);
+ }
+ EXPECT_EQ(t.split({1, 3, 2}, 3), answer);
+ }
+}
-// nntrainer::Tensor golden(1, 2, 3, 4, t_type);
-// golden.setValue(1.f);
-// EXPECT_EQ(golden, t);
+TEST(nntrainer_Tensor, split_05_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// /// @todo this test case is not valid anymore, since
-// /// std::uniform_real_distribution does not support _Float16
-// // t.initialize(nntrainer::Tensor::Initializer::HE_NORMAL);
-// // EXPECT_NE(golden, t);
-// // t.initialize();
-// // EXPECT_NE(golden, t);
+ nntrainer::Tensor t(3, 1, 1, 1, t_type);
+ EXPECT_THROW(t.split({1, 1}, 0), std::invalid_argument);
+}
-// t.initialize(nntrainer::Tensor::Initializer::ONES);
-// EXPECT_EQ(golden, t);
+TEST(nntrainer_Tensor, split_06_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// t.initialize();
-// EXPECT_EQ(golden, t);
-// }
+ nntrainer::Tensor t(3, 1, 1, 1, t_type);
+ EXPECT_THROW(t.split({2, 0, 1}, 0), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, split_01_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(3);
-// {
-// _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-// 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-// 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-// 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
-// answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-// 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79};
-// answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-// 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-// 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split(3, 0), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-// 12, 13, 14, 15, 16, 17, 18, 19, 40, 41, 42, 43,
-// 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 80, 81, 82, 83, 84, 85, 86, 87,
-// 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99};
-// answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-// 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-// 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split(2, 1), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 20, 21, 22, 23, 24,
-// 25, 26, 27, 28, 29, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 80, 81, 82, 83, 84,
-// 85, 86, 87, 88, 89, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {
-// 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 30, 31, 32, 33, 34,
-// 35, 36, 37, 38, 39, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 90, 91, 92, 93, 94,
-// 95, 96, 97, 98, 99, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split(2, 2), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(5);
-// {
-// _Float16 answer_data[] = {0, 5, 10, 15, 20, 25, 30, 35,
-// 40, 45, 50, 55, 60, 65, 70, 75,
-// 80, 85, 90, 95, 100, 105, 110, 115};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {1, 6, 11, 16, 21, 26, 31, 36,
-// 41, 46, 51, 56, 61, 66, 71, 76,
-// 81, 86, 91, 96, 101, 106, 111, 116};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {2, 7, 12, 17, 22, 27, 32, 37,
-// 42, 47, 52, 57, 62, 67, 72, 77,
-// 82, 87, 92, 97, 102, 107, 112, 117};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {3, 8, 13, 18, 23, 28, 33, 38,
-// 43, 48, 53, 58, 63, 68, 73, 78,
-// 83, 88, 93, 98, 103, 108, 113, 118};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
-// 44, 49, 54, 59, 64, 69, 74, 79,
-// 84, 89, 94, 99, 104, 109, 114, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split(5, 3), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(1, 1, 4, 6, t_type);
-// nntrainer::Tensor t =
-// ranged(1, 1, 4, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {0, 1, 2, 6, 7, 8, 12, 13, 14, 18, 19, 20};
-// answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {3, 4, 5, 9, 10, 11, 15, 16, 17, 21, 22, 23};
-// answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split(2, 3), answer);
-// }
-// }
+TEST(nntrainer_Tensor, split_07_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, split_02_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ nntrainer::Tensor t(3, 1, 1, 1, t_type);
+ EXPECT_THROW(t.split({}, 0), std::invalid_argument);
+}
-// nntrainer::Tensor t(1, 1, 1, 1, t_type);
-// EXPECT_THROW(t.split(0, 0), std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, cat_01_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(2);
+ inputs.emplace_back(
+ ranged(2, 1, 1, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(2, 2, 1, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ _Float16 answer_data[] = {0, 1, 0, 1, 2, 3, 2, 3, 4, 5, 6, 7};
+ nntrainer::Tensor answer(ml::train::TensorDim{2, 3, 1, 2, t_type},
+ answer_data);
+ EXPECT_EQ(nntrainer::Tensor::cat(inputs, 1), answer);
+ }
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(2);
+ inputs.emplace_back(
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(2, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+ 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+ 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
+ 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
+ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,
+ 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+ 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
+ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
+ 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
+ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
+ 75, 76, 77, 78, 79};
+ nntrainer::Tensor answer(ml::train::TensorDim{5, 2, 4, 5, t_type},
+ answer_data);
+ EXPECT_EQ(nntrainer::Tensor::cat(inputs, 0), answer);
+ }
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(2);
+ inputs.emplace_back(
+ ranged(3, 3, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
+ 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
+ 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
+ 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
+ 56, 57, 58, 59, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
+ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
+ 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
+ 38, 39, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+ 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
+ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+ 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+ 114, 115, 116, 117, 118, 119, 40, 41, 42, 43, 44, 45, 46, 47,
+ 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
+ 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
+ 76, 77, 78, 79, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
+ 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
+ 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,
+ 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,
+ 172, 173, 174, 175, 176, 177, 178, 179, 80, 81, 82, 83, 84, 85,
+ 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
+ 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+ 114, 115, 116, 117, 118, 119};
+ nntrainer::Tensor answer(ml::train::TensorDim{3, 5, 4, 5, t_type},
+ answer_data);
+ EXPECT_EQ(nntrainer::Tensor::cat(inputs, 1), answer);
+ }
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(2);
+ inputs.emplace_back(
+ ranged(3, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(3, 2, 2, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ _Float16 answer_data[] = {
+ 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 5, 6, 7,
+ 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 11, 12, 13, 14, 20,
+ 21, 22, 23, 24, 25, 26, 27, 28, 29, 15, 16, 17, 18, 19, 30, 31, 32, 33,
+ 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 45, 46,
+ 47, 48, 49, 25, 26, 27, 28, 29, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59};
+ nntrainer::Tensor answer(ml::train::TensorDim{3, 2, 3, 5, t_type},
+ answer_data);
+ EXPECT_EQ(nntrainer::Tensor::cat(inputs, 2), answer);
+ }
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(3);
+ inputs.emplace_back(
+ ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(3, 2, 4, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ inputs.emplace_back(
+ ranged(3, 2, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
+ _Float16 answer_data[] = {
+ 0, 0, 1, 2, 0, 1, 1, 3, 4, 5, 2, 3, 2, 6, 7, 8, 4, 5,
+ 3, 9, 10, 11, 6, 7, 4, 12, 13, 14, 8, 9, 5, 15, 16, 17, 10, 11,
+ 6, 18, 19, 20, 12, 13, 7, 21, 22, 23, 14, 15, 8, 24, 25, 26, 16, 17,
+ 9, 27, 28, 29, 18, 19, 10, 30, 31, 32, 20, 21, 11, 33, 34, 35, 22, 23,
+ 12, 36, 37, 38, 24, 25, 13, 39, 40, 41, 26, 27, 14, 42, 43, 44, 28, 29,
+ 15, 45, 46, 47, 30, 31, 16, 48, 49, 50, 32, 33, 17, 51, 52, 53, 34, 35,
+ 18, 54, 55, 56, 36, 37, 19, 57, 58, 59, 38, 39, 20, 60, 61, 62, 40, 41,
+ 21, 63, 64, 65, 42, 43, 22, 66, 67, 68, 44, 45, 23, 69, 70, 71, 46, 47};
+ nntrainer::Tensor answer(ml::train::TensorDim{3, 2, 4, 6, t_type},
+ answer_data);
+ EXPECT_EQ(nntrainer::Tensor::cat(inputs, 3), answer);
+ }
+}
-// TEST(nntrainer_Tensor, split_03_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, cat_02_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor t(3, 1, 1, 1, t_type);
-// EXPECT_THROW(t.split(2, 0), std::invalid_argument);
-// }
+ {
+ std::vector<nntrainer::Tensor> inputs;
+ inputs.reserve(2);
+ inputs.emplace_back(nntrainer::Tensor(2, 1, 1, 2, t_type));
+ inputs.emplace_back(nntrainer::Tensor(2, 2, 1, 2, t_type));
+ EXPECT_THROW(nntrainer::Tensor::cat(inputs, 2), std::invalid_argument);
+ }
+}
-// TEST(nntrainer_Tensor, split_04_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
-// 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
-// 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
-// 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-// 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79};
-// answer.emplace_back(ml::train::TensorDim{2, 2, 4, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-// 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-// 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{1, 2, 4, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({2, 1}, 0), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-// 12, 13, 14, 15, 16, 17, 18, 19, 40, 41, 42, 43,
-// 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 80, 81, 82, 83, 84, 85, 86, 87,
-// 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99};
-// answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-// 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-// 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 1, 4, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({1, 1}, 1), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 20, 21, 22, 23, 24,
-// 25, 26, 27, 28, 29, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 80, 81, 82, 83, 84,
-// 85, 86, 87, 88, 89, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {
-// 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 30, 31, 32, 33, 34,
-// 35, 36, 37, 38, 39, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-// 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 90, 91, 92, 93, 94,
-// 95, 96, 97, 98, 99, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 2, 5, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({2, 2}, 2), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(3);
-// {
-// _Float16 answer_data[] = {0, 5, 10, 15, 20, 25, 30, 35,
-// 40, 45, 50, 55, 60, 65, 70, 75,
-// 80, 85, 90, 95, 100, 105, 110, 115};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {
-// 1, 2, 3, 6, 7, 8, 11, 12, 13, 16, 17, 18, 21, 22, 23,
-// 26, 27, 28, 31, 32, 33, 36, 37, 38, 41, 42, 43, 46, 47, 48,
-// 51, 52, 53, 56, 57, 58, 61, 62, 63, 66, 67, 68, 71, 72, 73,
-// 76, 77, 78, 81, 82, 83, 86, 87, 88, 91, 92, 93, 96, 97, 98,
-// 101, 102, 103, 106, 107, 108, 111, 112, 113, 116, 117, 118};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 3, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
-// 44, 49, 54, 59, 64, 69, 74, 79,
-// 84, 89, 94, 99, 104, 109, 114, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({1, 3, 1}, 3), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(3);
-// {
-// _Float16 answer_data[] = {
-// 0, 1, 5, 6, 10, 11, 15, 16, 20, 21, 25, 26, 30, 31, 35, 36,
-// 40, 41, 45, 46, 50, 51, 55, 56, 60, 61, 65, 66, 70, 71, 75, 76,
-// 80, 81, 85, 86, 90, 91, 95, 96, 100, 101, 105, 106, 110, 111, 115, 116};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {
-// 2, 3, 7, 8, 12, 13, 17, 18, 22, 23, 27, 28, 32, 33, 37, 38,
-// 42, 43, 47, 48, 52, 53, 57, 58, 62, 63, 67, 68, 72, 73, 77, 78,
-// 82, 83, 87, 88, 92, 93, 97, 98, 102, 103, 107, 108, 112, 113, 117, 118};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {4, 9, 14, 19, 24, 29, 34, 39,
-// 44, 49, 54, 59, 64, 69, 74, 79,
-// 84, 89, 94, 99, 104, 109, 114, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 1, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({2, 2, 1}, 3), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(3, 2, 4, 5, t_type);
-// nntrainer::Tensor t =
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(2);
-// {
-// _Float16 answer_data[] = {
-// 0, 1, 5, 6, 10, 11, 15, 16, 20, 21, 25, 26, 30, 31, 35, 36,
-// 40, 41, 45, 46, 50, 51, 55, 56, 60, 61, 65, 66, 70, 71, 75, 76,
-// 80, 81, 85, 86, 90, 91, 95, 96, 100, 101, 105, 106, 110, 111, 115, 116};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 2, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {
-// 2, 3, 4, 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24,
-// 27, 28, 29, 32, 33, 34, 37, 38, 39, 42, 43, 44, 47, 48, 49,
-// 52, 53, 54, 57, 58, 59, 62, 63, 64, 67, 68, 69, 72, 73, 74,
-// 77, 78, 79, 82, 83, 84, 87, 88, 89, 92, 93, 94, 97, 98, 99,
-// 102, 103, 104, 107, 108, 109, 112, 113, 114, 117, 118, 119};
-// answer.emplace_back(ml::train::TensorDim{3, 2, 4, 3, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({2, 3}, 3), answer);
-// }
-// {
-// nntrainer::TensorDim ref_dim(1, 1, 4, 6, t_type);
-// nntrainer::Tensor t =
-// ranged(1, 1, 4, 6, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// std::vector<nntrainer::Tensor> answer;
-// answer.reserve(3);
-// {
-// _Float16 answer_data[] = {0, 6, 12, 18};
-// answer.emplace_back(ml::train::TensorDim{1, 1, 4, 1, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {1, 2, 3, 7, 8, 9, 13, 14, 15, 19, 20, 21};
-// answer.emplace_back(ml::train::TensorDim{1, 1, 4, 3, t_type},
-// answer_data);
-// }
-// {
-// _Float16 answer_data[] = {4, 5, 10, 11, 16, 17, 22, 23};
-// answer.emplace_back(ml::train::TensorDim{1, 1, 4, 2, t_type},
-// answer_data);
-// }
-// EXPECT_EQ(t.split({1, 3, 2}, 3), answer);
-// }
-// }
+TEST(nntrainer_Tensor, zoneout_mask_01_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// TEST(nntrainer_Tensor, split_05_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ const float zoneout_rate = 0.3f;
+ nntrainer::Tensor t(10, 10, 10, 10, t_type);
+ nntrainer::Tensor opposite(20, 20, 20, 20, t_type);
+ EXPECT_THROW(t.zoneout_mask(opposite, zoneout_rate), std::invalid_argument);
+}
-// nntrainer::Tensor t(3, 1, 1, 1, t_type);
-// EXPECT_THROW(t.split({1, 1}, 0), std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, zoneout_mask_02_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
+
+ const float zoneout_rate = 0.3f;
+ nntrainer::Tensor t(10, 10, 10, 10, t_type);
+ nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
+ constexpr float epsilon = 1e-3;
+
+ EXPECT_EQ(t.size(), opposite.size());
+
+ auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
+ return val2 - epsilon < val1 && val1 < val2 + epsilon;
+ };
+
+ for (unsigned int i = 0; i < opposite.size(); ++i) {
+ if (is_near(opposite.getValue<_Float16>(i), static_cast<_Float16>(0.0f))) {
+ EXPECT_NEAR(t.getValue<_Float16>(i), 1.0f, epsilon);
+ } else if (is_near(opposite.getValue<_Float16>(i),
+ static_cast<_Float16>(1.0f))) {
+ EXPECT_NEAR(t.getValue<_Float16>(i), 0.0f, epsilon);
+ } else {
+ FAIL() << "This should not be happen";
+ }
+ }
+}
-// TEST(nntrainer_Tensor, split_06_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, zoneout_mask_03_p) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// nntrainer::Tensor t(3, 1, 1, 1, t_type);
-// EXPECT_THROW(t.split({2, 0, 1}, 0), std::invalid_argument);
-// }
+ const float zoneout_rate = 0.3f;
+ nntrainer::Tensor t(10, 10, 100, 100, t_type);
+ nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
+ constexpr float epsilon = 1e-3;
-// TEST(nntrainer_Tensor, split_07_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
+ return val2 - epsilon < val1 && val1 < val2 + epsilon;
+ };
+ auto percentage = [](unsigned int dividend, unsigned int divisor) {
+ return (float)dividend / (float)divisor;
+ };
-// nntrainer::Tensor t(3, 1, 1, 1, t_type);
-// EXPECT_THROW(t.split({}, 0), std::invalid_argument);
-// }
+ {
+ unsigned int zeros = 0;
+ unsigned int ones = 0;
+ for (unsigned int i = 0; i < opposite.size(); ++i) {
+ if (is_near(opposite.getValue<_Float16>(i), static_cast<_Float16>(0.0))) {
+ ++zeros;
+ } else if (is_near(opposite.getValue<_Float16>(i),
+ static_cast<_Float16>(1.0))) {
+ ++ones;
+ } else {
+ FAIL() << "This should not be happen";
+ }
+ }
+ EXPECT_NEAR(percentage(zeros, opposite.size()), (1.0 - zoneout_rate),
+ epsilon);
-// TEST(nntrainer_Tensor, cat_01_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(2);
-// inputs.emplace_back(
-// ranged(2, 1, 1, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(2, 2, 1, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// _Float16 answer_data[] = {0, 1, 0, 1, 2, 3, 2, 3, 4, 5, 6, 7};
-// nntrainer::Tensor answer(ml::train::TensorDim{2, 3, 1, 2, t_type},
-// answer_data);
-// EXPECT_EQ(nntrainer::Tensor::cat(inputs, 1), answer);
-// }
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(2);
-// inputs.emplace_back(
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(2, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
-// 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-// 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
-// 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
-// 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-// 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,
-// 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
-// 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-// 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
-// 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-// 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,
-// 75, 76, 77, 78, 79};
-// nntrainer::Tensor answer(ml::train::TensorDim{5, 2, 4, 5, t_type},
-// answer_data);
-// EXPECT_EQ(nntrainer::Tensor::cat(inputs, 0), answer);
-// }
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(2);
-// inputs.emplace_back(
-// ranged(3, 3, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(3, 2, 4, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-// 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
-// 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
-// 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-// 56, 57, 58, 59, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-// 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-// 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-// 38, 39, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-// 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
-// 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-// 114, 115, 116, 117, 118, 119, 40, 41, 42, 43, 44, 45, 46, 47,
-// 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
-// 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
-// 76, 77, 78, 79, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
-// 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
-// 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,
-// 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,
-// 172, 173, 174, 175, 176, 177, 178, 179, 80, 81, 82, 83, 84, 85,
-// 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-// 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-// 114, 115, 116, 117, 118, 119};
-// nntrainer::Tensor answer(ml::train::TensorDim{3, 5, 4, 5, t_type},
-// answer_data);
-// EXPECT_EQ(nntrainer::Tensor::cat(inputs, 1), answer);
-// }
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(2);
-// inputs.emplace_back(
-// ranged(3, 2, 1, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(3, 2, 2, 5, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// _Float16 answer_data[] = {
-// 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 5, 6, 7,
-// 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 11, 12, 13, 14, 20,
-// 21, 22, 23, 24, 25, 26, 27, 28, 29, 15, 16, 17, 18, 19, 30, 31, 32, 33,
-// 34, 35, 36, 37, 38, 39, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, 45, 46,
-// 47, 48, 49, 25, 26, 27, 28, 29, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59};
-// nntrainer::Tensor answer(ml::train::TensorDim{3, 2, 3, 5, t_type},
-// answer_data);
-// EXPECT_EQ(nntrainer::Tensor::cat(inputs, 2), answer);
-// }
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(3);
-// inputs.emplace_back(
-// ranged(3, 2, 4, 1, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(3, 2, 4, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// inputs.emplace_back(
-// ranged(3, 2, 4, 2, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16));
-// _Float16 answer_data[] = {
-// 0, 0, 1, 2, 0, 1, 1, 3, 4, 5, 2, 3, 2, 6, 7, 8, 4, 5,
-// 3, 9, 10, 11, 6, 7, 4, 12, 13, 14, 8, 9, 5, 15, 16, 17, 10, 11,
-// 6, 18, 19, 20, 12, 13, 7, 21, 22, 23, 14, 15, 8, 24, 25, 26, 16, 17,
-// 9, 27, 28, 29, 18, 19, 10, 30, 31, 32, 20, 21, 11, 33, 34, 35, 22, 23,
-// 12, 36, 37, 38, 24, 25, 13, 39, 40, 41, 26, 27, 14, 42, 43, 44, 28, 29,
-// 15, 45, 46, 47, 30, 31, 16, 48, 49, 50, 32, 33, 17, 51, 52, 53, 34, 35,
-// 18, 54, 55, 56, 36, 37, 19, 57, 58, 59, 38, 39, 20, 60, 61, 62, 40, 41,
-// 21, 63, 64, 65, 42, 43, 22, 66, 67, 68, 44, 45, 23, 69, 70, 71, 46, 47};
-// nntrainer::Tensor answer(ml::train::TensorDim{3, 2, 4, 6, t_type},
-// answer_data);
-// EXPECT_EQ(nntrainer::Tensor::cat(inputs, 3), answer);
-// }
-// }
+ // main test
+ EXPECT_NEAR(percentage(ones, opposite.size()), zoneout_rate, epsilon);
+ }
-// TEST(nntrainer_Tensor, cat_02_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ unsigned int zeros = 0;
+ unsigned int ones = 0;
+ for (unsigned int i = 0; i < t.size(); ++i) {
+ if (is_near(t.getValue<_Float16>(i), (_Float16)0.0)) {
+ ++zeros;
+ } else if (is_near(t.getValue<_Float16>(i), (_Float16)1.0)) {
+ ++ones;
+ } else {
+ FAIL() << "This should not be happen";
+ }
+ }
+ EXPECT_NEAR(percentage(zeros, t.size()), zoneout_rate, epsilon);
-// {
-// std::vector<nntrainer::Tensor> inputs;
-// inputs.reserve(2);
-// inputs.emplace_back(nntrainer::Tensor(2, 1, 1, 2, t_type));
-// inputs.emplace_back(nntrainer::Tensor(2, 2, 1, 2, t_type));
-// EXPECT_THROW(nntrainer::Tensor::cat(inputs, 2), std::invalid_argument);
-// }
-// }
+ // main test
+ EXPECT_NEAR(percentage(ones, t.size()), (1.0f - zoneout_rate), epsilon);
+ }
+}
-// TEST(nntrainer_Tensor, zoneout_mask_01_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+TEST(nntrainer_Tensor, zoneout_mask_04_n) {
+ nntrainer::TensorDim::TensorType t_type;
+ t_type.format = nntrainer::Tformat::NCHW;
+ t_type.data_type = nntrainer::Tdatatype::FP16;
-// const _Float16 zoneout_rate = 0.3f;
-// nntrainer::Tensor t(10, 10, 10, 10, t_type);
-// nntrainer::Tensor opposite(20, 20, 20, 20, t_type);
-// EXPECT_THROW(t.zoneout_mask(opposite, zoneout_rate), std::invalid_argument);
-// }
+ const float zoneout_rate = 0.3f;
+ nntrainer::Tensor t(10, 10, 100, 100, t_type);
+ nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
+ constexpr float epsilon = 1e-3;
-// TEST(nntrainer_Tensor, zoneout_mask_02_p) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
+ return val2 - epsilon < val1 && val1 < val2 + epsilon;
+ };
+ auto percentage = [](unsigned int dividend, unsigned int divisor) {
+ return (float)dividend / (float)divisor;
+ };
-// const _Float16 zoneout_rate = 0.3f;
-// nntrainer::Tensor t(10, 10, 10, 10, t_type);
-// nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
-// constexpr _Float16 epsilon = 1e-3;
-
-// EXPECT_EQ(t.size(), opposite.size());
-
-// auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
-// return val2 - epsilon < val1 && val1 < val2 + epsilon;
-// };
-
-// for (unsigned int i = 0; i < opposite.size(); ++i) {
-// if (is_near(opposite.getValue<_Float16>(i), 0.0f)) {
-// EXPECT_NEAR(t.getValue<_Float16>(i), 1.0f, epsilon);
-// } else if (is_near(opposite.getValue<_Float16>(i), 1.0f)) {
-// EXPECT_NEAR(t.getValue<_Float16>(i), 0.0f, epsilon);
-// } else {
-// FAIL() << "This should not be happen";
-// }
-// }
-// }
+ {
+ unsigned int zeros = 0;
+ unsigned int ones = 0;
+ for (unsigned int i = 0; i < opposite.size(); ++i) {
+ if (is_near(opposite.getValue<_Float16>(i),
+ static_cast<_Float16>(0.0f))) {
+ ++zeros;
+ } else if (is_near(opposite.getValue<_Float16>(i),
+ static_cast<_Float16>(1.0f))) {
+ ++ones;
+ } else {
+ FAIL() << "This should not be happen";
+ }
+ }
+ EXPECT_FALSE(
+ is_near(static_cast<_Float16>(percentage(ones, opposite.size())),
+ static_cast<_Float16>(1.0f - zoneout_rate)));
+ }
-// // TEST(nntrainer_Tensor, zoneout_mask_03_p) {
-// // nntrainer::TensorDim::TensorType t_type;
-// // t_type.format = nntrainer::Tformat::NCHW;
-// // t_type.data_type = nntrainer::Tdatatype::FP16;
-
-// // const _Float16 zoneout_rate = (_Float16)0.3;
-// // nntrainer::Tensor t(10, 10, 100, 100, t_type);
-// // nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
-// // constexpr _Float16 epsilon = (_Float16)1e-3;
-
-// // auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
-// // return val2 - epsilon < val1 && val1 < val2 + epsilon;
-// // };
-// // auto percentage = [](unsigned int dividend, unsigned int divisor) {
-// // return (_Float16)dividend / (_Float16)divisor;
-// // };
-
-// // {
-// // unsigned int zeros = 0;
-// // unsigned int ones = 0;
-// // for (unsigned int i = 0; i < opposite.size(); ++i) {
-// // if (is_near(opposite.getValue<_Float16>(i), (_Float16)0.0)) {
-// // ++zeros;
-// // } else if (is_near(opposite.getValue<_Float16>(i), (_Float16)1.0)) {
-// // ++ones;
-// // } else {
-// // FAIL() << "This should not be happen";
-// // }
-// // }
-// // EXPECT_NEAR(percentage(zeros, opposite.size()),
-// // (_Float16)((_Float16)1.0 - zoneout_rate), epsilon);
-
-// // // main test
-// // EXPECT_NEAR(percentage(ones, opposite.size()), (_Float16)zoneout_rate,
-// // epsilon);
-// // }
-
-// // {
-// // unsigned int zeros = 0;
-// // unsigned int ones = 0;
-// // for (unsigned int i = 0; i < t.size(); ++i) {
-// // if (is_near(t.getValue<_Float16>(i), (_Float16)0.0)) {
-// // ++zeros;
-// // } else if (is_near(t.getValue<_Float16>(i), (_Float16)1.0)) {
-// // ++ones;
-// // } else {
-// // FAIL() << "This should not be happen";
-// // }
-// // }
-// // EXPECT_NEAR(percentage(zeros, t.size()), (_Float16)zoneout_rate, epsilon);
-
-// // // main test
-// // EXPECT_NEAR(percentage(ones, t.size()), (_Float16)(1.0f - zoneout_rate),
-// // epsilon);
-// // }
-// // }
-
-// TEST(nntrainer_Tensor, zoneout_mask_04_n) {
-// nntrainer::TensorDim::TensorType t_type;
-// t_type.format = nntrainer::Tformat::NCHW;
-// t_type.data_type = nntrainer::Tdatatype::FP16;
+ {
+ unsigned int zeros = 0;
+ unsigned int ones = 0;
+ for (unsigned int i = 0; i < t.size(); ++i) {
+ if (is_near(t.getValue<_Float16>(i), static_cast<_Float16>(0.0f))) {
+ ++zeros;
+ } else if (is_near(t.getValue<_Float16>(i),
+ static_cast<_Float16>(1.0f))) {
+ ++ones;
+ } else {
+ FAIL() << "This should not be happen";
+ }
+ }
+ EXPECT_FALSE(is_near(static_cast<_Float16>(percentage(ones, t.size())),
+ static_cast<_Float16>(zoneout_rate)));
+ }
+}
-// const _Float16 zoneout_rate = 0.3f;
-// nntrainer::Tensor t(10, 10, 100, 100, t_type);
-// nntrainer::Tensor opposite = t.zoneout_mask(zoneout_rate);
-// constexpr _Float16 epsilon = 1e-3;
-
-// auto is_near = [epsilon](_Float16 val1, _Float16 val2) {
-// return val2 - epsilon < val1 && val1 < val2 + epsilon;
-// };
-// auto percentage = [](unsigned int dividend, unsigned int divisor) {
-// return (_Float16)dividend / (_Float16)divisor;
-// };
-
-// {
-// unsigned int zeros = 0;
-// unsigned int ones = 0;
-// for (unsigned int i = 0; i < opposite.size(); ++i) {
-// if (is_near(opposite.getValue<_Float16>(i), 0.0f)) {
-// ++zeros;
-// } else if (is_near(opposite.getValue<_Float16>(i), 1.0f)) {
-// ++ones;
-// } else {
-// FAIL() << "This should not be happen";
-// }
-// }
-// EXPECT_FALSE(
-// is_near(percentage(ones, opposite.size()), 1.0f - zoneout_rate));
-// }
+TEST(nntrainer_Tensor, TensorMap_p) {
+ _Float16 dat[] = {1, 2, 3};
-// {
-// unsigned int zeros = 0;
-// unsigned int ones = 0;
-// for (unsigned int i = 0; i < t.size(); ++i) {
-// if (is_near(t.getValue<_Float16>(i), 0.0f)) {
-// ++zeros;
-// } else if (is_near(t.getValue<_Float16>(i), 1.0f)) {
-// ++ones;
-// } else {
-// FAIL() << "This should not be happen";
-// }
-// }
-// EXPECT_FALSE(is_near(percentage(ones, t.size()), zoneout_rate));
-// }
-// }
+ {
+ nntrainer::Tensor a =
+ nntrainer::Tensor::Map(dat, 3 * sizeof(_Float16), {3});
+ /// check if a.getData<_Float16>() has same address with dat
+ EXPECT_EQ(dat, a.getData<_Float16>());
+ {
+ /// check if b.getData<_Float16>() has same address with data
+ nntrainer::Tensor b = a;
+ EXPECT_EQ(dat, b.getData<_Float16>());
+ }
+ }
+ /// check if dat is accessible after destruction of all the tensor
+ EXPECT_FLOAT_EQ(dat[2], 3);
+}
-// TEST(nntrainer_Tensor, TensorMap_p) {
-// _Float16 dat[] = {1, 2, 3};
-
-// {
-// nntrainer::Tensor a = nntrainer::Tensor::Map(dat, 3 * sizeof(_Float16), {3});
-// /// check if a.getData<_Float16>() has same address with dat
-// EXPECT_EQ(dat, a.getData<_Float16>());
-// {
-// /// check if b.getData<_Float16>() has same address with data
-// nntrainer::Tensor b = a;
-// EXPECT_EQ(dat, b.getData<_Float16>());
-// }
-// }
-// /// check if dat is accessible after destruction of all the tensor
-// EXPECT_FLOAT_EQ(dat[2], 3);
-// }
+TEST(nntrainer_Tensor, TensorWrap_01_n) {
+ _Float16 dat[] = {1, 2, 3};
+ EXPECT_THROW(nntrainer::Tensor::Map(dat, 3, nntrainer::TensorDim({})),
+ std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, TensorWrap_01_n) {
-// _Float16 dat[] = {1, 2, 3};
-// EXPECT_THROW(nntrainer::Tensor::Map(dat, 3, nntrainer::TensorDim({})),
-// std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, TensorWrap_02_n) {
+ _Float16 dat[] = {1, 2, 3};
+ EXPECT_THROW(nntrainer::Tensor::Map(dat, 3, {4}), std::invalid_argument);
+}
-// TEST(nntrainer_Tensor, TensorWrap_02_n) {
-// _Float16 dat[] = {1, 2, 3};
-// EXPECT_THROW(nntrainer::Tensor::Map(dat, 3, {4}), std::invalid_argument);
-// }
+TEST(nntrainer_Tensor, TensorPaddedValue_p) {
+ nntrainer::Tensor a =
+ ranged(1, 1, 3, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
+ _Float16 default_padded = -1;
-// TEST(nntrainer_Tensor, TensorPaddedValue_p) {
-// nntrainer::Tensor a =
-// ranged(1, 1, 3, 3, nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16);
-// _Float16 default_padded = -1;
-
-// for (int i = 0; i < 5; ++i) {
-// for (int j = 0; j < 5; ++j) {
-// _Float16 expected = default_padded;
-// if (1 <= i && i <= 3 && 1 <= j && j <= 3) {
-// expected = (i - 1) * 3 + (j - 1);
-// }
-// _Float16 actual =
-// a.getValuePaddedVirtual<_Float16>(0, 0, i, j, 1, 1, default_padded);
-// EXPECT_FLOAT_EQ(actual, expected);
-// }
-// }
-// }
+ for (int i = 0; i < 5; ++i) {
+ for (int j = 0; j < 5; ++j) {
+ _Float16 expected = default_padded;
+ if (1 <= i && i <= 3 && 1 <= j && j <= 3) {
+ expected = (i - 1) * 3 + (j - 1);
+ }
+ _Float16 actual =
+ a.getValuePaddedVirtual<_Float16>(0, 0, i, j, 1, 1, default_padded);
+ EXPECT_FLOAT_EQ(actual, expected);
+ }
+ }
+}
GTEST_API_ int main(int argc, char **argv) {
int result = -1;
projects / platform / core / ml / nntrainer.git / commitdiff