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eval.cpp
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// Copyright (C) 2018-2022 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <cmath>
#include <cstddef>
#include <string>
#include <vector>
#include "engines_util/execute_tools.hpp"
#include "engines_util/test_case.hpp"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "ngraph/node.hpp"
#include "ngraph/node_output.hpp"
#include "ngraph/op/abs.hpp"
#include "ngraph/op/acos.hpp"
#include "ngraph/op/add.hpp"
#include "ngraph/op/asin.hpp"
#include "ngraph/op/atan.hpp"
#include "ngraph/op/broadcast.hpp"
#include "ngraph/op/ceiling.hpp"
#include "ngraph/op/concat.hpp"
#include "ngraph/op/constant.hpp"
#include "ngraph/op/convert.hpp"
#include "ngraph/op/cos.hpp"
#include "ngraph/op/cosh.hpp"
#include "ngraph/op/erf.hpp"
#include "ngraph/op/exp.hpp"
#include "ngraph/op/fake_quantize.hpp"
#include "ngraph/op/floor.hpp"
#include "ngraph/op/gather.hpp"
#include "ngraph/op/log.hpp"
#include "ngraph/op/max_pool.hpp"
#include "ngraph/op/min.hpp"
#include "ngraph/op/minimum.hpp"
#include "ngraph/op/negative.hpp"
#include "ngraph/op/non_zero.hpp"
#include "ngraph/op/not.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/op/range.hpp"
#include "ngraph/op/reduce_logical_and.hpp"
#include "ngraph/op/relu.hpp"
#include "ngraph/op/reshape.hpp"
#include "ngraph/op/round.hpp"
#include "ngraph/op/scatter_elements_update.hpp"
#include "ngraph/op/scatter_update.hpp"
#include "ngraph/op/shape_of.hpp"
#include "ngraph/op/sigmoid.hpp"
#include "ngraph/op/sign.hpp"
#include "ngraph/op/sin.hpp"
#include "ngraph/op/sinh.hpp"
#include "ngraph/op/softmax.hpp"
#include "ngraph/op/softsign.hpp"
#include "ngraph/op/sqrt.hpp"
#include "ngraph/op/squeeze.hpp"
#include "ngraph/op/tan.hpp"
#include "ngraph/op/tanh.hpp"
#include "ngraph/op/topk.hpp"
#include "ngraph/op/unsqueeze.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "ngraph/validation_util.hpp"
#include "sequnce_generator.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_tools.hpp"
#include "util/type_prop.hpp"
NGRAPH_SUPPRESS_DEPRECATED_START
using namespace std;
using namespace ngraph;
#define ASSERT_FLOAT_VECTORS_EQ(expected, result) \
ASSERT_EQ(expected.size(), result.size()) << "Array sizes differ."; \
for (size_t i = 0; i < expected.size(); ++i) { \
ASSERT_FLOAT_EQ(expected[i], result[i]) << "at index: " << i; \
}
TEST(eval, bad_get_data_ptr) {
HostTensor c(element::f32, Shape{});
*c.get_data_ptr<float>() = 1.0;
EXPECT_EQ(*c.get_data_ptr<element::Type_t::f32>(), 1.0);
try {
c.get_data_ptr<element::Type_t::f64>();
FAIL() << "Bad type not detected.";
} catch (const CheckFailure& error) {
EXPECT_HAS_SUBSTRING(error.what(), std::string("get_data_ptr"));
}
try {
c.get_data_ptr<element::Type_t::i32>();
FAIL() << "Bad type not detected.";
} catch (const CheckFailure& error) {
EXPECT_HAS_SUBSTRING(error.what(), std::string("get_data_ptr"));
}
}
TEST(eval, max_eval_parameter) {
auto p = make_shared<op::Parameter>(element::i64, Shape{});
auto result = maximum_value(p);
EXPECT_FALSE(result.first);
EXPECT_EQ(result.second, numeric_limits<uint64_t>::max());
}
TEST(eval, max_eval_constant) {
auto c = op::Constant::create<int64_t>(element::i64, Shape{}, {27});
auto result = maximum_value(c);
ASSERT_TRUE(result.first);
EXPECT_EQ(result.second, 27);
}
TEST(eval, max_eval_minimum_constant) {
auto c = op::Constant::create<int64_t>(element::i64, Shape{}, {27});
auto p = make_shared<op::Parameter>(element::i64, Shape{});
auto m = make_shared<op::v1::Minimum>(c, p);
auto result = maximum_value(m);
ASSERT_TRUE(result.first);
EXPECT_EQ(result.second, 27);
}
TEST(eval, max_eval_reduce_min) {
auto concat = make_shared<op::v0::Convert>(
make_shared<op::v0::Concat>(OutputVector{make_shared<op::v0::Parameter>(element::i64, Shape{4}),
make_shared<op::v0::Constant>(element::i64, Shape{4}, 37)},
0),
element::i32);
auto reduce = make_shared<op::v0::Convert>(
make_shared<op::v1::ReduceMin>(concat, make_shared<op::v0::Constant>(element::i32, Shape{1}, 0)),
element::i64);
auto squeezes = make_shared<op::v0::Squeeze>(
make_shared<op::v0::Unsqueeze>(reduce, make_shared<op::v0::Constant>(element::i32, Shape{1}, 0)),
make_shared<op::v0::Constant>(element::i64, Shape{1}, 0));
EXPECT_EQ(maximum_value(squeezes).second, 37);
}
TEST(eval, evaluate_shape_of) {
auto p = make_shared<op::Parameter>(element::f32, PartialShape{-1, -1});
auto so = make_shared<op::v0::ShapeOf>(p);
auto fun = make_shared<Function>(OutputVector{so}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f})}));
EXPECT_EQ(result->get_element_type(), element::i64);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2}));
auto result_shape = read_vector<int64_t>(result);
vector<int64_t> arg_shape{2, 3};
ASSERT_EQ(result_shape, arg_shape);
}
TEST(eval, evaluate_dynamic_range_sum) {
auto p_start = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p_stop = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p_step = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p1 = make_shared<op::Parameter>(element::f32, PartialShape{});
auto range = make_shared<op::v0::Range>(p_start, p_stop, p_step);
auto add = make_shared<op::v1::Add>(range, p1);
auto fun = make_shared<Function>(OutputVector{add}, ParameterVector{p_start, p_stop, p_step, p1});
auto result_tensor = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result_tensor},
{make_host_tensor<element::Type_t::f32>({}, {1.0f}),
make_host_tensor<element::Type_t::f32>({}, {10.0f}),
make_host_tensor<element::Type_t::f32>({}, {3.0f}),
make_host_tensor<element::Type_t::f32>({}, {7.0f})}));
EXPECT_EQ(result_tensor->get_element_type(), element::f32);
EXPECT_EQ(result_tensor->get_partial_shape(), (PartialShape{3}));
auto cval = read_vector<float>(result_tensor);
vector<float> seq{8.0f, 11.0f, 14.0f};
ASSERT_EQ(cval, seq);
}
TEST(eval, interpret_dynamic_range_sum) {
auto p_start = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p_stop = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p_step = make_shared<op::Parameter>(element::f32, PartialShape{});
auto p1 = make_shared<op::Parameter>(element::f32, PartialShape{});
auto range = make_shared<op::v0::Range>(p_start, p_stop, p_step);
auto add = make_shared<op::v1::Add>(range, p1);
auto fun = make_shared<Function>(OutputVector{add}, ParameterVector{p_start, p_stop, p_step, p1});
auto test_case = test::TestCase(fun);
test_case.add_input(std::vector<float>{1.0f});
test_case.add_input(std::vector<float>{10.0f});
test_case.add_input(std::vector<float>{3.0f});
test_case.add_input(std::vector<float>{7.0f});
vector<float> seq{8.0f, 11.0f, 14.0f};
test_case.add_expected_output({3}, seq);
test_case.run();
}
TEST(eval, evaluate_broadcast_v3_bidirectional) {
Shape shape_a{4, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int32_t>(element::i32, Shape{3}, {2, 1, 4});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{4, 1}, {1.0f, 2.0f, 3.0f, 4.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 4, 4}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_bidirectional_target_rank_smaller_than_input) {
Shape shape_a{1, 1, 1, 1, 1, 1, 1, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{4}, {1, 3, 1, 1});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(shape_a, {1.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{1, 1, 1, 1, 1, 3, 1, 1}));
auto result_val = read_vector<float>(result);
vector<float> expec{1.0f, 1.0f, 1.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_bidirectional_target_rank_smaller_than_input_2) {
Shape shape_a{1, 3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int32_t>(element::i32, Shape{2}, {3, 1});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{1, 3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{1, 3, 1}));
auto result_val = read_vector<float>(result);
vector<float> expec{1.0f, 2.0f, 3.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_bidirectional_dyn) {
Shape shape_a{4, 1};
auto A = make_shared<op::Parameter>(element::i32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i32, Shape{3});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::i32>(Shape{4, 1}, {1, 2, 3, 4}),
make_host_tensor<element::Type_t::i32>(Shape{3}, {2, 1, 4})}));
EXPECT_EQ(result->get_element_type(), element::i32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 4, 4}));
auto result_val = read_vector<int32_t>(result);
vector<int32_t> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4,
1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_numpy) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_numpy_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i32, Shape{3});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i32>(Shape{3}, {2, 3, 6})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_numpy_vs_bidi) {
Shape in_shape{1, 4, 1};
auto A = make_shared<op::Parameter>(element::f32, in_shape);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {1, 4, 4});
auto bcast_v3_num = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::NUMPY);
auto fun_num = make_shared<Function>(OutputVector{bcast_v3_num}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun_num->evaluate({result}, {make_host_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{1, 4, 4}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
ASSERT_EQ(expec, result_val);
auto target_shape2 = op::Constant::create<int64_t>(element::i64, Shape{2}, {1, 4});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape2, op::BroadcastType::BIDIRECTIONAL);
auto fun_bidi = make_shared<Function>(OutputVector{bcast_v3_num}, ParameterVector{A});
auto result2 = make_shared<HostTensor>();
ASSERT_TRUE(
fun_bidi->evaluate({result2}, {make_host_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})}));
EXPECT_EQ(result2->get_element_type(), element::f32);
EXPECT_EQ(result2->get_partial_shape(), (PartialShape{1, 4, 4}));
auto result_val2 = read_vector<float>(result2);
vector<float> expec2{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
ASSERT_EQ(expec2, result_val2);
}
TEST(eval, evaluate_broadcast_v3_bidi_3d) {
Shape in_shape{1, 4, 1};
auto A = make_shared<op::Parameter>(element::f32, in_shape);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {1, 1, 3});
auto bcast_v3_num = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun_num = make_shared<Function>(OutputVector{bcast_v3_num}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun_num->evaluate({result}, {make_host_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{1, 4, 3}));
auto result_val = read_vector<float>(result);
vector<float> expec{1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f};
ASSERT_EQ(expec, result_val);
}
TEST(eval, evaluate_broadcast_v3_bidi_4d) {
Shape in_shape{4, 1, 1};
Shape expec_shape{1, 4, 2, 2};
auto A = make_shared<op::Parameter>(element::f32, in_shape);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{4}, {1, 1, 2, 2});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastType::BIDIRECTIONAL);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(in_shape, {1.0f, 2.0f, 3.0f, 4.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{1, 4, 2, 2}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_pdpd) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastModeSpec(op::BroadcastType::PDPD, 1));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_pdpd_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i32, Shape{3});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A, target_shape, op::BroadcastModeSpec(op::BroadcastType::PDPD, 1));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i32>(Shape{3}, {2, 3, 6})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_numpy) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
auto bcast_v3 = make_shared<op::v1::Broadcast>(A, target_shape);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_numpy_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i64, Shape{3});
auto bcast_v3 = make_shared<op::v1::Broadcast>(A, target_shape);
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 6})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_pdpd) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 6});
auto bcast_v3 =
make_shared<op::v1::Broadcast>(A, target_shape, op::AutoBroadcastSpec(op::AutoBroadcastType::PDPD, 1));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_pdpd_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i64, Shape{3});
auto bcast_v3 =
make_shared<op::v1::Broadcast>(A, target_shape, op::AutoBroadcastSpec(op::AutoBroadcastType::PDPD, 1));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 6})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 6}));
auto result_val = read_vector<float>(result);
vector<float> expec{
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_explicit) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = op::Constant::create<int64_t>(element::i64, Shape{3}, {2, 3, 1});
auto axes_mapping = op::Constant::create<int32_t>(element::i32, Shape{2}, {1, 2});
auto bcast_v3 = make_shared<op::v1::Broadcast>(A,
target_shape,
axes_mapping,
op::AutoBroadcastSpec(op::AutoBroadcastType::EXPLICIT));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 1}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 2, 3, 1, 2, 3};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v1_explicit_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i64, Shape{3});
auto axes_mapping = make_shared<op::Parameter>(element::i32, Shape{2});
auto bcast_v1 = make_shared<op::v1::Broadcast>(A,
target_shape,
axes_mapping,
op::AutoBroadcastSpec(op::AutoBroadcastType::EXPLICIT));
auto fun = make_shared<Function>(OutputVector{bcast_v1}, ParameterVector{A, target_shape, axes_mapping});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 1}),
make_host_tensor<element::Type_t::i32>(Shape{2}, {1, 2})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 1}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 2, 3, 1, 2, 3};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_broadcast_v3_explicit_dyn) {
Shape shape_a{3, 1};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
auto target_shape = make_shared<op::Parameter>(element::i64, Shape{3});
auto axes_mapping = make_shared<op::Parameter>(element::i32, Shape{2});
auto bcast_v3 = make_shared<op::v3::Broadcast>(A,
target_shape,
axes_mapping,
op::BroadcastModeSpec(op::BroadcastType::EXPLICIT));
auto fun = make_shared<Function>(OutputVector{bcast_v3}, ParameterVector{A, target_shape, axes_mapping});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{3, 1}, {1.0f, 2.0f, 3.0f}),
make_host_tensor<element::Type_t::i64>(Shape{3}, {2, 3, 1}),
make_host_tensor<element::Type_t::i32>(Shape{2}, {1, 2})}));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3, 1}));
auto result_val = read_vector<float>(result);
vector<float> expec{1, 2, 3, 1, 2, 3};
ASSERT_EQ(result_val, expec);
}
TEST(eval, test_op_multi_out) {
auto p = make_shared<op::Parameter>(element::f32, PartialShape{2, 3});
auto p2 = make_shared<op::Parameter>(element::f64, PartialShape{2, 2});
auto so = make_shared<TestOpMultiOut>(p, p2);
auto fun = make_shared<Function>(OutputVector{so->output(0), so->output(1)}, ParameterVector{p, p2});
auto result = make_shared<HostTensor>(element::Type_t::f32, Shape{2, 3});
auto result2 = make_shared<HostTensor>(element::Type_t::f64, Shape{2, 2});
HostTensorVector ins{make_host_tensor<element::Type_t::f32>(Shape{2, 3}),
make_host_tensor<element::Type_t::f64>(Shape{2, 2})};
ASSERT_TRUE(fun->evaluate({result, result2}, ins));
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_partial_shape(), (PartialShape{2, 3}));
auto result_val = read_vector<float>(result);
auto arg_val = read_vector<float>(ins[0]);
ASSERT_EQ(result_val, arg_val);
EXPECT_EQ(result2->get_element_type(), element::f64);
EXPECT_EQ(result2->get_partial_shape(), (PartialShape{2, 2}));
auto result_val2 = read_vector<double>(result2);
auto arg_val2 = read_vector<double>(ins[1]);
ASSERT_EQ(result_val2, arg_val2);
}
TEST(eval, evaluate_reshape_v1) {
auto data = make_shared<op::Parameter>(element::f32, Shape{2, 5});
auto pattern = make_shared<op::Parameter>(element::i64, Shape{2});
auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, false);
auto func = make_shared<Function>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
auto result_tensor = make_shared<HostTensor>();
ASSERT_TRUE(func->evaluate({result_tensor},
{make_host_tensor<element::Type_t::f32>({2, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}),
make_host_tensor<element::Type_t::i64>({2}, {5, 2})}));
EXPECT_EQ(result_tensor->get_element_type(), element::f32);
EXPECT_EQ(result_tensor->get_partial_shape(), (PartialShape{5, 2}));
auto computed_val = read_vector<float>(result_tensor);
vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
ASSERT_EQ(computed_val, expected_val);
}
TEST(eval, evaluate_reshape_v1_negative_index) {
auto data = make_shared<op::Parameter>(element::f32, Shape{2, 5});
auto pattern = make_shared<op::Parameter>(element::i64, Shape{2});
auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, false);
auto func = make_shared<Function>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
auto result_tensor = make_shared<HostTensor>();
ASSERT_TRUE(func->evaluate({result_tensor},
{make_host_tensor<element::Type_t::f32>({2, 5}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}),
make_host_tensor<element::Type_t::i64>({2}, {2, -1})}));
EXPECT_EQ(result_tensor->get_element_type(), element::f32);
EXPECT_EQ(result_tensor->get_partial_shape(), (PartialShape{2, 5}));
auto computed_val = read_vector<float>(result_tensor);
vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
ASSERT_EQ(computed_val, expected_val);
}
TEST(eval, evaluate_reshape_v1_negative_index_zero_dim_zero_flag) {
auto data = make_shared<op::Parameter>(element::f32, Shape{2, 2, 2, 2});
auto pattern = make_shared<op::Parameter>(element::i64, Shape{6});
auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
auto func = make_shared<Function>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
auto result_tensor = make_shared<HostTensor>();
ASSERT_TRUE(func->evaluate(
{result_tensor},
{make_host_tensor<element::Type_t::f32>({2, 2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
make_host_tensor<element::Type_t::i64>({6}, {2, 0, 1, -1, 1, 2})}));
EXPECT_EQ(result_tensor->get_element_type(), element::f32);
EXPECT_EQ(result_tensor->get_partial_shape(), (PartialShape{2, 2, 1, 2, 1, 2}));
auto computed_val = read_vector<float>(result_tensor);
vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
ASSERT_EQ(computed_val, expected_val);
}
TEST(eval, evaluate_reshape_v1_pattern_int16) {
auto data = make_shared<op::Parameter>(element::f32, Shape{2, 2, 2, 2});
auto pattern = make_shared<op::Parameter>(element::i16, Shape{6});
auto dyn_reshape = make_shared<op::v1::Reshape>(data, pattern, true);
auto func = make_shared<Function>(OutputVector{dyn_reshape}, ParameterVector{data, pattern});
auto result_tensor = make_shared<HostTensor>();
ASSERT_TRUE(func->evaluate(
{result_tensor},
{make_host_tensor<element::Type_t::f32>({2, 2, 2, 2}, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}),
make_host_tensor<element::Type_t::i16>({6}, {2, 0, 1, -1, 1, 2})}));
EXPECT_EQ(result_tensor->get_element_type(), element::f32);
EXPECT_EQ(result_tensor->get_partial_shape(), (PartialShape{2, 2, 1, 2, 1, 2}));
auto computed_val = read_vector<float>(result_tensor);
vector<float> expected_val{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
ASSERT_EQ(computed_val, expected_val);
}
TEST(eval, evaluate_convert) {
auto p = make_shared<op::Parameter>(element::f32, PartialShape{-1, -1});
auto convert = make_shared<op::v0::Convert>(p, element::i64);
auto fun = make_shared<Function>(OutputVector{convert}, ParameterVector{p});
std::vector<std::vector<float>> inputs{{-1, 1}};
std::vector<std::vector<int64_t>> expected_result{{-1, 1}};
for (size_t i = 0; i < inputs.size(); i++) {
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{1, 2}, inputs[i])}));
EXPECT_EQ(result->get_element_type(), element::i64);
EXPECT_EQ(result->get_shape(), (Shape{1, 2}));
auto result_data = read_vector<int64_t>(result);
ASSERT_EQ(result_data, expected_result[i]);
}
}
TEST(eval, evaluate_abs) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 3});
auto abs = make_shared<op::Abs>(p);
auto fun = make_shared<Function>(OutputVector{abs}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_erf) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 3});
auto erf = make_shared<op::Erf>(p);
auto fun = make_shared<Function>(OutputVector{erf}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{std::erf(0.0f),
std::erf(-1.0f),
std::erf(-2.0f),
std::erf(-3.0f),
std::erf(4.0f),
std::erf(5.0f)};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_exp) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 3});
auto exp = make_shared<op::Exp>(p);
auto fun = make_shared<Function>(OutputVector{exp}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 3}, {0.0f, -1.0f, -2.0f, -3.0f, 4.0f, 5.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{std::exp(0.0f),
std::exp(-1.0f),
std::exp(-2.0f),
std::exp(-3.0f),
std::exp(4.0f),
std::exp(5.0f)};
ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}
TEST(eval, evaluate_floor) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 2});
auto floor = make_shared<op::Floor>(p);
auto fun = make_shared<Function>(OutputVector{floor}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{2, 2}, {-2.5f, -2.0f, 0.3f, 4.8f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{-3.0f, -2.0f, 0.0f, 4.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_floor_int32) {
auto p = make_shared<op::Parameter>(element::i32, Shape{2, 2});
auto floor = make_shared<op::Floor>(p);
auto fun = make_shared<Function>(OutputVector{floor}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::i32>(Shape{2, 2}, {-2, -136314888, 0x40000010, 0x40000001})}));
EXPECT_EQ(result->get_element_type(), element::i32);
auto result_val = read_vector<int32_t>(result);
vector<int32_t> expec{-2, -136314888, 0x40000010, 0x40000001};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_log) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 2, 2});
auto log = make_shared<op::Log>(p);
auto fun = make_shared<Function>(OutputVector{log}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate(
{result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 2, 2}, {0.125f, 0.25f, 0.5f, 1.f, 2.f, 4.f, 8.f, 16.f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{std::log(0.125f),
std::log(0.25f),
std::log(0.5f),
std::log(1.f),
std::log(2.f),
std::log(4.f),
std::log(8.f),
std::log(16.f)};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_negative_f32) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 5});
auto negate = make_shared<op::Negative>(p);
auto fun = make_shared<Function>(OutputVector{negate}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate({result},
{make_host_tensor<element::Type_t::f32>(
Shape{2, 5},
{1.35f, 8.76f, -8.0f, 17.234f, -2.121f, 1.0f, 8.7f, -8.92f, 17.0f, -1.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{-1.35f, -8.76f, 8.0f, -17.234f, 2.121f, -1.0f, -8.7f, 8.92f, -17.0f, 1.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_negative_i32) {
auto p = make_shared<op::Parameter>(element::i32, Shape{2, 5});
auto negate = make_shared<op::Negative>(p);
auto fun = make_shared<Function>(OutputVector{negate}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::i32>(Shape{2, 5}, {1, 8, -8, 17, -2, 1, 8, -8, 17, 0})}));
EXPECT_EQ(result->get_element_type(), element::i32);
auto result_val = read_vector<int32_t>(result);
vector<int32_t> expec{-1, -8, 8, -17, 2, -1, -8, 8, -17, 0};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_relu_2Ffprop_f32) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 5});
auto relu = make_shared<op::Relu>(p);
auto fun = make_shared<Function>(OutputVector{relu}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate(
{result},
{make_host_tensor<element::Type_t::f32>(Shape{2, 5}, {1, 8, -8, 17, -0.5f, 0.1f, 8.5f, -8, 17, -0.5f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{1, 8, 0, 17, 0, 0.1f, 8.5f, 0, 17, 0};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_relu_2Ffprop_i32) {
auto p = make_shared<op::Parameter>(element::i32, Shape{2, 5});
auto relu = make_shared<op::Relu>(p);
auto fun = make_shared<Function>(OutputVector{relu}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result},
{make_host_tensor<element::Type_t::i32>(Shape{2, 5}, {1, 8, -8, 17, -2, 1, 8, -8, 17, -1})}));
EXPECT_EQ(result->get_element_type(), element::i32);
auto result_val = read_vector<int32_t>(result);
vector<int32_t> expec{1, 8, 0, 17, 0, 1, 8, 0, 17, 0};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_round) {
auto p = make_shared<op::Parameter>(element::f32, Shape{5});
auto round = make_shared<op::v5::Round>(p, op::v5::Round::RoundMode::HALF_TO_EVEN);
auto fun = make_shared<Function>(OutputVector{round}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{5}, {0.9f, 2.5f, 2.3f, 1.5f, -4.5f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{1.0f, 2.0f, 2.0f, 2.0f, -4.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_round_2D) {
auto p = make_shared<op::Parameter>(element::f32, Shape{3, 5});
auto round = make_shared<op::v5::Round>(p, op::v5::Round::RoundMode::HALF_TO_EVEN);
auto fun = make_shared<Function>(OutputVector{round}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate(
{result},
{make_host_tensor<element::Type_t::f32>(
Shape{3, 5},
{0.1f, 0.5f, 0.9f, 1.2f, 1.5f, 1.8f, 2.3f, 2.5f, 2.7f, -1.1f, -1.5f, -1.9f, -2.2f, -2.5f, -2.8f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{0.f, 0.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f, 3.f, -1.f, -2.f, -2.f, -2.f, -2.f, -3.f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_sigmoid) {
auto p = make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
auto sigmoid = make_shared<op::Sigmoid>(p);
auto fun = make_shared<Function>(OutputVector{sigmoid}, ParameterVector{p});
auto result = make_shared<HostTensor>();
float x1 = 1.0f;
float x2 = 4.0f;
float sigma1 = 1.0f / (1.0f + std::exp(-x1));
float sigma2 = 1.0f / (1.0f + std::exp(-x2));
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{1, 1, 2, 2}, {x1, x2, x1, x2})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{sigma1, sigma2, sigma1, sigma2};
EXPECT_EQ(result_val.size(), expec.size());
}
TEST(eval, evaluate_sign) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 3});
auto sign = make_shared<op::Sign>(p);
auto fun = make_shared<Function>(OutputVector{sign}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(
fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{2, 3}, {1, -2, 0, -4.8f, 4.8f, -0.0f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{1, -1, 0, -1, 1, 0};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_sin) {
auto p = make_shared<op::Parameter>(element::f32, Shape{11});
auto sin = make_shared<op::Sin>(p);
auto fun = make_shared<Function>(OutputVector{sin}, ParameterVector{p});
auto result = make_shared<HostTensor>();
ASSERT_TRUE(fun->evaluate(
{result},
{make_host_tensor<element::Type_t::f32>(Shape{11},
{0.f, 0.25f, -0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f})}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{0.00000000f,
0.24740396f,
-0.24740396f,
0.47942554f,
-0.47942554f,
0.84147098f,
-0.84147098f,
0.90929743f,
-0.90929743f,
-0.75680250f,
0.75680250f};
ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}
TEST(eval, evaluate_sinh) {
auto p = make_shared<op::Parameter>(element::f32, Shape{6});
auto sinh = make_shared<op::Sinh>(p);
auto fun = make_shared<Function>(OutputVector{sinh}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{1.0f, 0.0f, -0.0f, -1.0f, 5.0f, -5.0f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{6}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
return sinhf(x);
});
ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}
TEST(eval, evaluate_sqrt) {
auto p = make_shared<op::Parameter>(element::f32, Shape{6});
auto sqrt = make_shared<op::Sqrt>(p);
auto fun = make_shared<Function>(OutputVector{sqrt}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{16, 4, 81, 100, 10000, 0};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{6}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{4, 2, 9, 10, 100, 0};
ASSERT_FLOAT_VECTORS_EQ(expec, result_val);
}
TEST(eval, evaluate_acos) {
auto p = make_shared<op::Parameter>(element::f32, Shape{11});
auto acos = make_shared<op::Acos>(p);
auto fun = make_shared<Function>(OutputVector{acos}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{-1.f, -0.75f, -0.5f, -0.25f, -0.125f, 0.f, 0.125f, 0.25f, 0.5f, 0.75f, 1.f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{11}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
return std::acos(x);
});
ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}
TEST(eval, evaluate_asin) {
auto p = make_shared<op::Parameter>(element::f32, Shape{11});
auto asin = make_shared<op::Asin>(p);
auto fun = make_shared<Function>(OutputVector{asin}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{-1.f, -0.75f, -0.5f, -0.25f, -0.125f, 0.f, 0.125f, 0.25f, 0.5f, 0.75f, 1.f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{11}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
return std::asin(x);
});
ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}
TEST(eval, evaluate_atan) {
auto p = make_shared<op::Parameter>(element::f32, Shape{11});
auto atan = make_shared<op::Atan>(p);
auto fun = make_shared<Function>(OutputVector{atan}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{-4.f, -2.f, -1.f, -0.5f, -0.25f, 0.f, 0.25f, 0.5f, 1.f, 2.f, 4.f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{11}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {
return std::atan(x);
});
ASSERT_FLOAT_VECTORS_EQ(input, result_val);
}
TEST(eval, evaluate_ceiling) {
auto p = make_shared<op::Parameter>(element::f32, Shape{2, 2});
auto ceil = make_shared<op::Ceiling>(p);
auto fun = make_shared<Function>(OutputVector{ceil}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{-2.5f, -2.0f, 0.3f, 4.8f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{2, 2}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
vector<float> expec{-2.0f, -2.0f, 1.0f, 5.0f};
ASSERT_EQ(result_val, expec);
}
TEST(eval, evaluate_cos) {
auto p = make_shared<op::Parameter>(element::f32, Shape{11});
auto cos = make_shared<op::Cos>(p);
auto fun = make_shared<Function>(OutputVector{cos}, ParameterVector{p});
auto result = make_shared<HostTensor>();
vector<float> input{0.f, 0.25f, -0.25f, 0.5f, -0.5f, 1.f, -1.f, 2.f, -2.f, 4.f, -4.f};
ASSERT_TRUE(fun->evaluate({result}, {make_host_tensor<element::Type_t::f32>(Shape{11}, input)}));
EXPECT_EQ(result->get_element_type(), element::f32);
auto result_val = read_vector<float>(result);
std::transform(input.begin(), input.end(), input.begin(), [](float x) -> float {