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

64 lines
2.3 KiB
C++

/**
* Copyright 2020 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 "minddata/dataset/kernels/image/normalize_pad_op.h"
#include "minddata/dataset/core/cv_tensor.h"
#include "utils/log_adapter.h"
#include <opencv2/opencv.hpp>
using namespace mindspore::dataset;
class MindDataTestNormalizePadOP : public UT::CVOP::CVOpCommon {
public:
MindDataTestNormalizePadOP() : CVOpCommon() {}
};
/// Feature: NormalizePad op
/// Description: Test input parameters with dtype float32 at Runtime level
/// Expectation: Op is successfully processed.
TEST_F(MindDataTestNormalizePadOP, TestFloat32) {
MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat32.";
std::shared_ptr<Tensor> output_tensor;
// Numbers are from the resnet50 model implementation
std::vector<float> mean = {121.0, 115.0, 100.0};
std::vector<float> std = {70.0, 68.0, 71.0};
// NormalizePad Op
std::unique_ptr<NormalizePadOp> op = std::make_unique<NormalizePadOp>(mean, std, "float32", true);
EXPECT_TRUE(op->OneToOne());
Status s = op->Compute(input_tensor_, &output_tensor);
EXPECT_TRUE(s.IsOk());
}
/// Feature: NormalizePad op
/// Description: Test input parameters with dtype float16 at Runtime level
/// Expectation: Op is successfully processed.
TEST_F(MindDataTestNormalizePadOP, TestFloat16) {
MS_LOG(INFO) << "Doing TestNormalizePadOp::TestFloat16.";
std::shared_ptr<Tensor> output_tensor;
// Numbers are from the resnet50 model implementation
std::vector<float> mean = {121.0, 115.0, 100.0};
std::vector<float> std = {70.0, 68.0, 71.0};
// NormalizePad Op
std::unique_ptr<NormalizePadOp> op = std::make_unique<NormalizePadOp>(mean, std, "float16", true);
EXPECT_TRUE(op->OneToOne());
Status s = op->Compute(input_tensor_, &output_tensor);
EXPECT_TRUE(s.IsOk());
}