1 /** 2 * Copyright 2019 Huawei Technologies Co., Ltd 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 #include "common/common.h" 17 #include "common/cvop_common.h" 18 #include "minddata/dataset/kernels/image/random_rotation_op.h" 19 #include "minddata/dataset/core/cv_tensor.h" 20 #include "minddata/dataset/kernels/data/to_float16_op.h" 21 #include "utils/log_adapter.h" 22 23 using namespace mindspore::dataset; 24 using mindspore::LogStream; 25 using mindspore::ExceptionType::NoExceptionType; 26 using mindspore::MsLogLevel::INFO; 27 28 class MindDataTestToFloat16Op : public UT::CVOP::CVOpCommon { 29 public: 30 MindDataTestToFloat16Op() : CVOpCommon() {} 31 }; 32 33 TEST_F(MindDataTestToFloat16Op, TestOp) { 34 MS_LOG(INFO) << "Doing TestRandomRotationOp::TestOp."; 35 std::shared_ptr<Tensor> output_tensor; 36 float s_degree = -180; 37 float e_degree = 180; 38 // use compute center to use for rotation 39 std::vector<float> center = {}; 40 bool expand = false; 41 std::unique_ptr<RandomRotationOp> op( 42 new RandomRotationOp(s_degree, e_degree, InterpolationMode::kLinear, expand, center)); 43 EXPECT_TRUE(op->OneToOne()); 44 Status s = op->Compute(input_tensor_, &output_tensor); 45 EXPECT_TRUE(s.IsOk()); 46 EXPECT_EQ(input_tensor_->shape()[0], output_tensor->shape()[0]); 47 EXPECT_EQ(input_tensor_->shape()[1], output_tensor->shape()[1]); 48 49 std::unique_ptr<ToFloat16Op> to_float_op(new ToFloat16Op()); 50 std::shared_ptr<Tensor> output_tensor1; 51 s = op->Compute(output_tensor, &output_tensor1); 52 EXPECT_EQ(output_tensor->shape()[0], output_tensor1->shape()[0]); 53 EXPECT_EQ(output_tensor->shape()[1], output_tensor1->shape()[1]); 54 } 55