//***************************************************************************** // 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 <algorithm> #include <cinttypes> #include <cmath> #include <cstdlib> #include <random> #include <string> #include "gtest/gtest.h" #include "ngraph/ngraph.hpp" #include "util/all_close.hpp" #include "util/all_close_f.hpp" #include "util/ndarray.hpp" #include "util/test_control.hpp" #include "util/test_tools.hpp" using namespace std; using namespace ngraph; static string s_manifest = "${MANIFEST}"; NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_2_in_3) { Shape shape_a{}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3}; auto r = make_shared<op::OneHot>(A, Shape{3}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{2}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{0, 0, 1}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_1_in_3) { Shape shape_a{}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3}; auto r = make_shared<op::OneHot>(A, Shape{3}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{1}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{0, 1, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_0_in_3) { Shape shape_a{}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3}; auto r = make_shared<op::OneHot>(A, Shape{3}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{0}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{1, 0, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_scalar_oob_in_3) { Shape shape_a{}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3}; auto r = make_shared<op::OneHot>(A, Shape{3}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{3}); vector<int32_t> r_data(4); auto result = backend->create_tensor(element::i32, shape_r, r_data.data()); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ(r_data[3], 0); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_0) { Shape shape_a{8}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3, 8}; auto r = make_shared<op::OneHot>(A, Shape{3, 8}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{2, 1, 0, 0, 2, 2, 1, 0}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ( (vector<int32_t>{0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_1) { Shape shape_a{8}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{8, 3}; auto r = make_shared<op::OneHot>(A, Shape{8, 3}, 1); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{2, 1, 0, 0, 2, 2, 1, 0}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ( (vector<int32_t>{0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_1_barely_oob) { Shape shape_a{8}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{8, 3}; auto r = make_shared<op::OneHot>(A, Shape{8, 3}, 1); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{2, 1, 0, 0, 3, 2, 1, 0}); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); vector<int32_t> rv = read_vector<int32_t>(result); EXPECT_EQ(rv[0], 0); EXPECT_EQ(rv[1], 0); EXPECT_EQ(rv[2], 1); EXPECT_EQ(rv[3], 0); EXPECT_EQ(rv[4], 1); EXPECT_EQ(rv[5], 0); EXPECT_EQ(rv[6], 1); EXPECT_EQ(rv[7], 0); EXPECT_EQ(rv[8], 0); EXPECT_EQ(rv[9], 1); EXPECT_EQ(rv[10], 0); EXPECT_EQ(rv[11], 0); // These are undefined since value is out of bounds // EXPECT_EQ(rv[12], 0); // EXPECT_EQ(rv[13], 0); // EXPECT_EQ(rv[14], 0); EXPECT_EQ(rv[15], 0); EXPECT_EQ(rv[16], 0); EXPECT_EQ(rv[17], 1); EXPECT_EQ(rv[18], 0); EXPECT_EQ(rv[19], 1); EXPECT_EQ(rv[20], 0); EXPECT_EQ(rv[21], 1); EXPECT_EQ(rv[22], 0); EXPECT_EQ(rv[23], 0); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_matrix_0) { Shape shape_a{3, 3}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{3, 3, 3}; auto r = make_shared<op::OneHot>(A, Shape{3, 3, 3}, 0); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{ 0, 1, 1, 2, 1, 0, 0, 2, 1, }); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, one_hot_vector_many_categories) { // Imagenet has roughly 20,000 categories uint32_t category_count = 20000; Shape shape_a{6}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_r{6, category_count}; auto r = make_shared<op::OneHot>(A, Shape{6, category_count}, 1); auto f = make_shared<Function>(r, ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); vector<int32_t> input_data{0, 11, 101, 1001, 10001, static_cast<int32_t>(category_count - 1)}; copy_data(a, input_data); auto result = backend->create_tensor(element::i32, shape_r); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); vector<int32_t> data = read_vector<int32_t>(result); vector<int32_t> bit_positions; for (size_t i = 0; i < shape_size(shape_r); ++i) { if (data[i] == 1) { bit_positions.push_back(i % category_count); } } EXPECT_EQ(bit_positions, input_data); }