//***************************************************************************** // 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)); }