forked from mindspore-Ecosystem/mindspore
56 lines
2.0 KiB
C++
56 lines
2.0 KiB
C++
/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "common/common.h"
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#include "common/cvop_common.h"
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#include "dataset/kernels/image/random_rotation_op.h"
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#include "dataset/core/cv_tensor.h"
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#include "dataset/kernels/data/to_float16_op.h"
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#include "utils/log_adapter.h"
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using namespace mindspore::dataset;
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using mindspore::MsLogLevel::INFO;
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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::LogStream;
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class MindDataTestToFloat16Op : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestToFloat16Op() : CVOpCommon() {}
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};
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TEST_F(MindDataTestToFloat16Op, TestOp) {
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MS_LOG(INFO) << "Doing TestRandomRotationOp::TestOp.";
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std::shared_ptr<Tensor> output_tensor;
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float s_degree = -180;
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float e_degree = 180;
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// use compute center to use for rotation
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float x_center = -1;
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float y_center = -1;
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bool expand = false;
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std::unique_ptr<RandomRotationOp> op(new RandomRotationOp(
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s_degree, e_degree, x_center, y_center, InterpolationMode::kLinear, expand));
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EXPECT_TRUE(op->OneToOne());
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Status s = op->Compute(input_tensor_, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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EXPECT_EQ(input_tensor_->shape()[0], output_tensor->shape()[0]);
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EXPECT_EQ(input_tensor_->shape()[1], output_tensor->shape()[1]);
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std::unique_ptr<ToFloat16Op> to_float_op(new ToFloat16Op());
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std::shared_ptr<Tensor> output_tensor1;
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s = op->Compute(output_tensor, &output_tensor1);
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EXPECT_EQ(output_tensor->shape()[0], output_tensor1->shape()[0]);
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EXPECT_EQ(output_tensor->shape()[1], output_tensor1->shape()[1]);
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}
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