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//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/test_tools.hpp"
using namespace ngraph;
using namespace std;
shared_ptr<runtime::Tensor>
make_reduce_result(function<shared_ptr<Node>(const shared_ptr<Node>&, const AxisSet&)> func)
{
Shape shape_a{3, 2};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
Shape shape_rt{2};
auto f = make_shared<Function>(func(A, {0}), ParameterVector{A});
auto backend = runtime::Backend::create("INTERPRETER");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, shape_a);
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6});
auto result = backend->create_tensor(element::f32, shape_rt);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
return result;
}
shared_ptr<runtime::Tensor> make_reduce_result_true(
function<shared_ptr<Node>(const shared_ptr<Node>&, const AxisSet&, bool)> func)
{
Shape shape_a{3, 2};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
Shape shape_rt{2};
auto f = make_shared<Function>(func(A, {0}, true), ParameterVector{A});
auto backend = runtime::Backend::create("INTERPRETER");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, shape_a);
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6});
auto result = backend->create_tensor(element::f32, shape_rt);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
return result;
}
shared_ptr<runtime::Tensor> make_reduce_result_false(
function<shared_ptr<Node>(const shared_ptr<Node>&, const AxisSet&, bool)> func)
{
Shape shape_a{3, 2};
auto A = make_shared<op::Parameter>(element::f32, shape_a);
Shape shape_rt{2};
auto f = make_shared<Function>(func(A, {0}, false), ParameterVector{A});
auto backend = runtime::Backend::create("INTERPRETER");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, shape_a);
copy_data(a, vector<float>{1, 2, 3, 4, 5, 6});
auto result = backend->create_tensor(element::f32, shape_rt);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
return result;
}
TEST(builder, l2_norm)
{
auto result = make_reduce_result(builder::l2_norm);
ASSERT_TRUE(test::all_close((vector<float>{5.9160797831f, 7.48331477355f}),
read_vector<float>(result)));
}
TEST(builder, mean)
{
auto result = make_reduce_result(builder::mean);
ASSERT_TRUE(test::all_close((vector<float>{3, 4}), read_vector<float>(result)));
}
TEST(builder, std_dev)
{
auto result = make_reduce_result_false(builder::std_dev);
ASSERT_TRUE(test::all_close((vector<float>{1.63299316186f, 1.63299316186f}),
read_vector<float>(result)));
result = make_reduce_result_true(builder::std_dev);
ASSERT_TRUE(test::all_close((vector<float>{2, 2}), read_vector<float>(result)));
}
TEST(builder, variance)
{
auto result = make_reduce_result_false(builder::variance);
ASSERT_TRUE(test::all_close((vector<float>{2.66666666666f, 2.66666666666f}),
read_vector<float>(result)));
result = make_reduce_result_true(builder::variance);
ASSERT_TRUE(test::all_close((vector<float>{4, 4}), read_vector<float>(result)));
}
TEST(builder, numpy_transpose)
{
// 2D Transpose
Shape shape{2, 4};
auto param = make_shared<op::Parameter>(element::f32, shape);
auto transposed = dynamic_pointer_cast<op::Reshape>(builder::numpy_transpose(param));
EXPECT_EQ(Shape({4, 2}), transposed->get_output_shape());
// Multidimensional Transpose
shape = Shape{2, 4, 8};
param = make_shared<op::Parameter>(element::f32, shape);
transposed = dynamic_pointer_cast<op::Reshape>(builder::numpy_transpose(param));
EXPECT_EQ(Shape({8, 4, 2}), transposed->get_output_shape());
// Dimshuffle
shape = Shape{2, 4, 8};
param = make_shared<op::Parameter>(element::f32, shape);
transposed =
dynamic_pointer_cast<op::Reshape>(builder::numpy_transpose(param, AxisVector{2, 0, 1}));
EXPECT_EQ(Shape({8, 2, 4}), transposed->get_output_shape());
// Bad Orders
EXPECT_ANY_THROW(
dynamic_pointer_cast<op::Reshape>(builder::numpy_transpose(param, AxisVector{2})));
EXPECT_ANY_THROW(
dynamic_pointer_cast<op::Reshape>(builder::numpy_transpose(param, AxisVector{2, 2, 1})));
}
TEST(builder, tensor_mask)
{
Shape max_sequence_length{3};
auto sequence_lengths = make_shared<op::Parameter>(element::u32, max_sequence_length);
Shape mask_shape{3, 5};
auto f =
make_shared<Function>(builder::tensor_mask<op::Less>(sequence_lengths, 1, 0, mask_shape, 0),
ParameterVector{sequence_lengths});
auto backend = runtime::Backend::create("INTERPRETER");
auto sequence_lengths_data = backend->create_tensor(element::u32, max_sequence_length);
copy_data(sequence_lengths_data, vector<uint32_t>{1, 3, 2});
auto result = backend->create_tensor(element::boolean, mask_shape);
auto handle = backend->compile(f);
handle->call_with_validate({result}, {sequence_lengths_data});
vector<char> expected{1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0};
EXPECT_EQ(expected, read_vector<char>(result));
}