mindspore/tests/ut/cpp/dataset/to_float16_op_test.cc

56 lines
2.0 KiB
C++

/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* 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 "common/common.h"
#include "common/cvop_common.h"
#include "dataset/kernels/image/random_rotation_op.h"
#include "dataset/core/cv_tensor.h"
#include "dataset/kernels/data/to_float16_op.h"
#include "utils/log_adapter.h"
using namespace mindspore::dataset;
using mindspore::MsLogLevel::INFO;
using mindspore::ExceptionType::NoExceptionType;
using mindspore::LogStream;
class MindDataTestToFloat16Op : public UT::CVOP::CVOpCommon {
public:
MindDataTestToFloat16Op() : CVOpCommon() {}
};
TEST_F(MindDataTestToFloat16Op, TestOp) {
MS_LOG(INFO) << "Doing TestRandomRotationOp::TestOp.";
std::shared_ptr<Tensor> output_tensor;
float s_degree = -180;
float e_degree = 180;
// use compute center to use for rotation
float x_center = -1;
float y_center = -1;
bool expand = false;
std::unique_ptr<RandomRotationOp> op(new RandomRotationOp(
s_degree, e_degree, x_center, y_center, InterpolationMode::kLinear, expand));
EXPECT_TRUE(op->OneToOne());
Status s = op->Compute(input_tensor_, &output_tensor);
EXPECT_TRUE(s.IsOk());
EXPECT_EQ(input_tensor_->shape()[0], output_tensor->shape()[0]);
EXPECT_EQ(input_tensor_->shape()[1], output_tensor->shape()[1]);
std::unique_ptr<ToFloat16Op> to_float_op(new ToFloat16Op());
std::shared_ptr<Tensor> output_tensor1;
s = op->Compute(output_tensor, &output_tensor1);
EXPECT_EQ(output_tensor->shape()[0], output_tensor1->shape()[0]);
EXPECT_EQ(output_tensor->shape()[1], output_tensor1->shape()[1]);
}