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mindspore2022/tests/ut/cpp/dataset/execute_test.cc

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/**
* Copyright 2020-2021 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 "include/api/types.h"
#include "minddata/dataset/core/de_tensor.h"
#include "minddata/dataset/include/dataset/audio.h"
#include "minddata/dataset/include/dataset/execute.h"
#include "minddata/dataset/include/dataset/transforms.h"
#include "minddata/dataset/include/dataset/audio.h"
#include "minddata/dataset/include/dataset/vision.h"
#include "minddata/dataset/include/dataset/audio.h"
#include "minddata/dataset/include/dataset/text.h"
#include "utils/log_adapter.h"
using namespace mindspore::dataset;
using mindspore::LogStream;
using mindspore::ExceptionType::NoExceptionType;
using mindspore::MsLogLevel::INFO;
class MindDataTestExecute : public UT::DatasetOpTesting {
protected:
};
TEST_F(MindDataTestExecute, TestAllpassBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAllpassBiquadWithEager.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> allpass_biquad_01 = std::make_shared<audio::AllpassBiquad>(44100, 200);
mindspore::dataset::Execute Transform01({allpass_biquad_01});
// Filtered waveform by allpassbiquad
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestAllpassBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAllpassBiquadWithWrongArg.";
std::vector<double> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check Q
MS_LOG(INFO) << "Q is zero.";
std::shared_ptr<TensorTransform> allpass_biquad_op = std::make_shared<audio::AllpassBiquad>(44100, 200, 0);
mindspore::dataset::Execute Transform01({allpass_biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestAdjustGammaEager3Channel) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAdjustGammaEager3Channel.";
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
auto decode = vision::Decode();
auto adjust_gamma_op = vision::AdjustGamma(0.1, 1.0);
auto transform = Execute({decode, adjust_gamma_op});
Status rc = transform(image, &image);
EXPECT_EQ(rc, Status::OK());
}
TEST_F(MindDataTestExecute, TestAdjustGammaEager1Channel) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAdjustGammaEager1Channel.";
auto m1 = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
auto decode = vision::Decode();
auto rgb2gray = vision::RGB2GRAY();
auto adjust_gamma_op = vision::AdjustGamma(0.1, 1.0);
auto transform = Execute({decode, rgb2gray, adjust_gamma_op});
Status rc = transform(m1, &m1);
EXPECT_EQ(rc, Status::OK());
}
TEST_F(MindDataTestExecute, TestAmplitudeToDB) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAmplitudeToDB.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 2, 2, 3}), &input));
auto input_ms = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> amplitude_to_db_op = std::make_shared<audio::AmplitudeToDB>();
// apply amplitude_to_db
mindspore::dataset::Execute trans({amplitude_to_db_op});
Status status = trans(input_ms, &input_ms);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestAmplitudeToDBWrongArgs) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAmplitudeToDBWrongArgs.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_ms = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> amplitude_to_db_op =
std::make_shared<audio::AmplitudeToDB>(ScaleType::kPower, 1.0, -1e-10, 80.0);
// apply amplitude_to_db
mindspore::dataset::Execute trans({amplitude_to_db_op});
Status status = trans(input_ms, &input_ms);
EXPECT_FALSE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestAmplitudeToDBWrongInput) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAmplitudeToDBWrongInput.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({20}), &input));
auto input_ms = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> amplitude_to_db_op = std::make_shared<audio::AmplitudeToDB>();
// apply amplitude_to_db
mindspore::dataset::Execute trans({amplitude_to_db_op});
Status status = trans(input_ms, &input_ms);
EXPECT_FALSE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestComposeTransforms) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestComposeTransforms.";
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
std::shared_ptr<TensorTransform> center_crop(new vision::CenterCrop({30}));
std::shared_ptr<TensorTransform> rescale = std::make_shared<vision::Rescale>(1. / 3, 0.5);
auto transform = Execute({decode, center_crop, rescale});
Status rc = transform(image, &image);
EXPECT_EQ(rc, Status::OK());
EXPECT_EQ(30, image.Shape()[0]);
EXPECT_EQ(30, image.Shape()[1]);
}
TEST_F(MindDataTestExecute, TestCrop) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestCrop.";
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
auto decode = vision::Decode();
auto crop = vision::Crop({10, 30}, {10, 15});
auto transform = Execute({decode, crop});
Status rc = transform(image, &image);
EXPECT_EQ(rc, Status::OK());
EXPECT_EQ(image.Shape()[0], 10);
EXPECT_EQ(image.Shape()[1], 15);
}
TEST_F(MindDataTestExecute, TestFrequencyMasking) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestFrequencyMasking.";
std::shared_ptr<Tensor> input_tensor_;
TensorShape s = TensorShape({6, 2});
ASSERT_OK(Tensor::CreateFromVector(
std::vector<float>({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}), s, &input_tensor_));
auto input_tensor = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> frequency_masking_op = std::make_shared<audio::FrequencyMasking>(true, 2);
mindspore::dataset::Execute transform({frequency_masking_op});
Status status = transform(input_tensor, &input_tensor);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestTimeMasking) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTimeMasking.";
std::shared_ptr<Tensor> input_tensor_;
TensorShape s = TensorShape({2, 6});
ASSERT_OK(Tensor::CreateFromVector(
std::vector<float>({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}), s, &input_tensor_));
auto input_tensor = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> time_masking_op = std::make_shared<audio::TimeMasking>(true, 2);
mindspore::dataset::Execute transform({time_masking_op});
Status status = transform(input_tensor, &input_tensor);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestTimeStretchEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTimeStretchEager.";
std::shared_ptr<Tensor> input_tensor_;
// op param
int freq = 4;
int hop_length = 20;
float rate = 1.3;
int frame_num = 10;
// create tensor
TensorShape s = TensorShape({2, freq, frame_num, 2});
// init input vec
std::vector<float> input_vec(2 * freq * frame_num * 2);
for (int ind = 0; ind < input_vec.size(); ind++) {
input_vec[ind] = std::rand() % (1000) / (1000.0f);
}
ASSERT_OK(Tensor::CreateFromVector(input_vec, s, &input_tensor_));
auto input_ms = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> time_stretch_op = std::make_shared<audio::TimeStretch>(hop_length, freq, rate);
// apply timestretch
mindspore::dataset::Execute Transform({time_stretch_op});
Status status = Transform(input_ms, &input_ms);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestTimeStretchParamCheck) {
MS_LOG(INFO) << "Doing MindDataTestTimeStretch-TestTimeStretchParamCheck.";
// Create an input
std::shared_ptr<Tensor> input_tensor_;
std::shared_ptr<Tensor> output_tensor;
TensorShape s = TensorShape({1, 4, 3, 2});
ASSERT_OK(Tensor::CreateFromVector(
std::vector<float>({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}),
s, &input_tensor_));
auto input_ms = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> time_stretch1 = std::make_shared<audio::TimeStretch>(4, 512, -2);
mindspore::dataset::Execute Transform1({time_stretch1});
Status status = Transform1(input_ms, &input_ms);
EXPECT_FALSE(status.IsOk());
std::shared_ptr<TensorTransform> time_stretch2 = std::make_shared<audio::TimeStretch>(4, -512, 2);
mindspore::dataset::Execute Transform2({time_stretch2});
status = Transform2(input_ms, &input_ms);
EXPECT_FALSE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestTransformInput1) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput1.";
// Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
// instantiated via mix of make_shared and new
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Define transform operations
std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
std::shared_ptr<TensorTransform> normalize(
new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
std::shared_ptr<TensorTransform> hwc2chw = std::make_shared<vision::HWC2CHW>();
mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
// Apply transform on image
Status rc = Transform(image, &image);
// Check image info
ASSERT_TRUE(rc.IsOk());
ASSERT_EQ(image.Shape().size(), 3);
ASSERT_EQ(image.Shape()[0], 3);
ASSERT_EQ(image.Shape()[1], 224);
ASSERT_EQ(image.Shape()[2], 224);
}
TEST_F(MindDataTestExecute, TestTransformInput2) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput2.";
// Test Execute with transform op input using API constructors, with std::shared_ptr<TensorTransform pointers,
// instantiated via new
// With this way of creating TensorTransforms, we don't need to explicitly delete the object created with the
// "new" keyword. When the shared pointer goes out of scope the object destructor will be called.
// Read image, construct MSTensor from dataset tensor
std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
// Define transform operations
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
std::shared_ptr<TensorTransform> normalize(
new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
mindspore::dataset::Execute Transform({decode, resize, normalize, hwc2chw});
// Apply transform on image
Status rc = Transform(image, &image);
// Check image info
ASSERT_TRUE(rc.IsOk());
ASSERT_EQ(image.Shape().size(), 3);
ASSERT_EQ(image.Shape()[0], 3);
ASSERT_EQ(image.Shape()[1], 224);
ASSERT_EQ(image.Shape()[2], 224);
}
TEST_F(MindDataTestExecute, TestTransformInput3) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInput3.";
// Test Execute with transform op input using API constructors, with auto pointers
// Read image, construct MSTensor from dataset tensor
std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
// Define transform operations
auto decode = vision::Decode();
mindspore::dataset::Execute Transform1(decode);
auto resize = vision::Resize({224, 224});
mindspore::dataset::Execute Transform2(resize);
// Apply transform on image
Status rc;
rc = Transform1(image, &image);
ASSERT_TRUE(rc.IsOk());
rc = Transform2(image, &image);
ASSERT_TRUE(rc.IsOk());
// Check image info
ASSERT_EQ(image.Shape().size(), 3);
ASSERT_EQ(image.Shape()[0], 224);
ASSERT_EQ(image.Shape()[1], 224);
ASSERT_EQ(image.Shape()[2], 3);
}
TEST_F(MindDataTestExecute, TestTransformInputSequential) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformInputSequential.";
// Test Execute with transform op input using API constructors, with auto pointers;
// Apply 2 transformations sequentially, including single non-vector Transform op input
// Read image, construct MSTensor from dataset tensor
std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
// Define transform#1 operations
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize({224, 224}));
std::shared_ptr<TensorTransform> normalize(
new vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
std::vector<std::shared_ptr<TensorTransform>> op_list = {decode, resize, normalize};
mindspore::dataset::Execute Transform(op_list);
// Apply transform#1 on image
Status rc = Transform(image, &image);
// Define transform#2 operations
std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
mindspore::dataset::Execute Transform2(hwc2chw);
// Apply transform#2 on image
rc = Transform2(image, &image);
// Check image info
ASSERT_TRUE(rc.IsOk());
ASSERT_EQ(image.Shape().size(), 3);
ASSERT_EQ(image.Shape()[0], 3);
ASSERT_EQ(image.Shape()[1], 224);
ASSERT_EQ(image.Shape()[2], 224);
}
TEST_F(MindDataTestExecute, TestTransformDecodeResizeCenterCrop1) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestTransformDecodeResizeCenterCrop1.";
// Test Execute with Decode, Resize and CenterCrop transform ops input using API constructors, with shared pointers
// Read image, construct MSTensor from dataset tensor
std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
mindspore::dataset::Tensor::CreateFromFile("data/dataset/apple.jpg", &de_tensor);
auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
// Define transform operations
std::vector<int32_t> resize_paras = {256, 256};
std::vector<int32_t> crop_paras = {224, 224};
std::shared_ptr<TensorTransform> decode(new vision::Decode());
std::shared_ptr<TensorTransform> resize(new vision::Resize(resize_paras));
std::shared_ptr<TensorTransform> centercrop(new vision::CenterCrop(crop_paras));
std::shared_ptr<TensorTransform> hwc2chw(new vision::HWC2CHW());
std::vector<std::shared_ptr<TensorTransform>> op_list = {decode, resize, centercrop, hwc2chw};
mindspore::dataset::Execute Transform(op_list, MapTargetDevice::kCpu);
// Apply transform on image
Status rc = Transform(image, &image);
// Check image info
ASSERT_TRUE(rc.IsOk());
ASSERT_EQ(image.Shape().size(), 3);
ASSERT_EQ(image.Shape()[0], 3);
ASSERT_EQ(image.Shape()[1], 224);
ASSERT_EQ(image.Shape()[2], 224);
}
TEST_F(MindDataTestExecute, TestUniformAugment) {
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
std::vector<mindspore::MSTensor> image2;
// Transform params
std::shared_ptr<TensorTransform> decode = std::make_shared<vision::Decode>();
std::shared_ptr<TensorTransform> resize_op(new vision::Resize({16, 16}));
std::shared_ptr<TensorTransform> vertical = std::make_shared<vision::RandomVerticalFlip>();
std::shared_ptr<TensorTransform> horizontal = std::make_shared<vision::RandomHorizontalFlip>();
std::shared_ptr<TensorTransform> uniform_op(new vision::UniformAugment({resize_op, vertical, horizontal}, 3));
auto transform1 = Execute({decode});
Status rc = transform1(image, &image);
ASSERT_TRUE(rc.IsOk());
auto transform2 = Execute({uniform_op});
rc = transform2({image}, &image2);
ASSERT_TRUE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestBasicTokenizer) {
std::shared_ptr<Tensor> de_tensor;
Tensor::CreateScalar<std::string>("Welcome to China.", &de_tensor);
auto txt = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
std::vector<mindspore::MSTensor> txt_result;
// Transform params
std::shared_ptr<TensorTransform> tokenizer =
std::make_shared<text::BasicTokenizer>(false, false, NormalizeForm::kNone, false, true);
// BasicTokenizer has 3 outputs so we need a vector to receive its result
auto transform1 = Execute({tokenizer});
Status rc = transform1({txt}, &txt_result);
ASSERT_EQ(txt_result.size(), 3);
ASSERT_TRUE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestRotate) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestRotate.";
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
auto decode = vision::Decode();
auto rotate = vision::Rotate(10.5);
auto transform = Execute({decode, rotate});
Status rc = transform(image, &image);
EXPECT_EQ(rc, Status::OK());
}
TEST_F(MindDataTestExecute, TestResizeWithBBox) {
auto image = ReadFileToTensor("data/dataset/apple.jpg");
std::shared_ptr<TensorTransform> decode_op = std::make_shared<vision::Decode>();
std::shared_ptr<TensorTransform> resizewithbbox_op =
std::make_shared<vision::ResizeWithBBox>(std::vector<int32_t>{250, 500});
// Test Compute(Tensor, Tensor) method of ResizeWithBBox
auto transform = Execute({decode_op, resizewithbbox_op});
// Expect fail since Compute(Tensor, Tensor) is not a valid behaviour for this Op,
// while Compute(TensorRow, TensorRow) is the correct one.
Status rc = transform(image, &image);
EXPECT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestBandBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandBiquadWithEager.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> band_biquad_01 = std::make_shared<audio::BandBiquad>(44100, 200);
mindspore::dataset::Execute Transform01({band_biquad_01});
// Filtered waveform by bandbiquad
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBandBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandBiquadWithWrongArg.";
std::vector<double> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check Q
MS_LOG(INFO) << "Q is zero.";
std::shared_ptr<TensorTransform> band_biquad_op = std::make_shared<audio::BandBiquad>(44100, 200, 0);
mindspore::dataset::Execute Transform01({band_biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBandpassBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandpassBiquadWithEager.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> bandpass_biquad_01 = std::make_shared<audio::BandpassBiquad>(44100, 200);
mindspore::dataset::Execute Transform01({bandpass_biquad_01});
// Filtered waveform by bandpassbiquad
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBandpassBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandpassBiquadWithWrongArg.";
std::vector<double> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check Q
MS_LOG(INFO) << "Q is zero.";
std::shared_ptr<TensorTransform> bandpass_biquad_op = std::make_shared<audio::BandpassBiquad>(44100, 200, 0);
mindspore::dataset::Execute Transform01({bandpass_biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBandrejectBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandrejectBiquadWithEager.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> bandreject_biquad_01 = std::make_shared<audio::BandrejectBiquad>(44100, 200);
mindspore::dataset::Execute Transform01({bandreject_biquad_01});
// Filtered waveform by bandrejectbiquad
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBandrejectBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBandrejectBiquadWithWrongArg.";
std::vector<double> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check Q
MS_LOG(INFO) << "Q is zero.";
std::shared_ptr<TensorTransform> bandreject_biquad_op = std::make_shared<audio::BandrejectBiquad>(44100, 200, 0);
mindspore::dataset::Execute Transform01({bandreject_biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestAngleEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestAngleEager.";
std::vector<double> origin = {1.143, 1.3123, 2.632, 2.554, -1.213, 1.3, 0.456, 3.563};
TensorShape input_shape({4, 2});
std::shared_ptr<Tensor> de_tensor;
Tensor::CreateFromVector(origin, input_shape, &de_tensor);
std::shared_ptr<TensorTransform> angle = std::make_shared<audio::Angle>();
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
mindspore::dataset::Execute Transform({angle});
Status s = Transform(input, &input);
ASSERT_TRUE(s.IsOk());
}
TEST_F(MindDataTestExecute, TestRGB2BGREager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestRGB2BGREager.";
// Read images
auto image = ReadFileToTensor("data/dataset/apple.jpg");
// Transform params
auto decode = vision::Decode();
auto rgb2bgr_op = vision::RGB2BGR();
auto transform = Execute({decode, rgb2bgr_op});
Status rc = transform(image, &image);
EXPECT_EQ(rc, Status::OK());
}
TEST_F(MindDataTestExecute, TestEqualizerBiquadEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestEqualizerBiquadEager.";
int sample_rate = 44100;
float center_freq = 3.5;
float gain = 5.5;
float Q = 0.707;
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.8236, 0.2049, 0.3335, 0.5933, 0.9911, 0.2482, 0.3007, 0.9054,
0.7598, 0.5394, 0.2842, 0.5634, 0.6363, 0.2226, 0.2288};
Tensor::CreateFromVector(test_vector, TensorShape({5, 3}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
std::shared_ptr<TensorTransform> equalizer_biquad(new audio::EqualizerBiquad({sample_rate, center_freq, gain, Q}));
auto transform = Execute({equalizer_biquad});
Status rc = transform({input}, &output);
ASSERT_TRUE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestEqualizerBiquadParamCheckQ) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestEqualizerBiquadParamCheckQ.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.1129, 0.3899, 0.7762, 0.2437, 0.9911, 0.8764, 0.4524, 0.9034,
0.3277, 0.8904, 0.1852, 0.6721, 0.1325, 0.2345, 0.5538};
Tensor::CreateFromVector(test_vector, TensorShape({3, 5}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check Q
std::shared_ptr<TensorTransform> equalizer_biquad_op = std::make_shared<audio::EqualizerBiquad>(44100, 3.5, 5.5, 0);
mindspore::dataset::Execute transform({equalizer_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestEqualizerBiquadParamCheckSampleRate) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestEqualizerBiquadParamCheckSampleRate.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.5236, 0.7049, 0.4335, 0.4533, 0.0911, 0.3482, 0.3407, 0.9054,
0.7598, 0.5394, 0.2842, 0.5634, 0.6363, 0.2226, 0.2288, 0.6743};
Tensor::CreateFromVector(test_vector, TensorShape({4, 4}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check sample_rate
std::shared_ptr<TensorTransform> equalizer_biquad_op = std::make_shared<audio::EqualizerBiquad>(0, 3.5, 5.5, 0.7);
mindspore::dataset::Execute transform({equalizer_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestLowpassBiquadEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestLowpassBiquadEager.";
int sample_rate = 44100;
float cutoff_freq = 2000.0;
float Q = 0.6;
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {23.5, 13.2, 62.5, 27.1, 15.5, 30.3, 44.9, 25.0,
11.3, 37.4, 67.1, 33.8, 73.4, 53.3, 93.7, 31.1};
Tensor::CreateFromVector(test_vector, TensorShape({4, 4}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
std::shared_ptr<TensorTransform> lowpass_biquad(new audio::LowpassBiquad({sample_rate, cutoff_freq, Q}));
auto transform = Execute({lowpass_biquad});
Status rc = transform({input}, &output);
ASSERT_TRUE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestLowpassBiuqadParamCheckQ) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestLowpassBiuqadParamCheckQ.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.8236, 0.2049, 0.3335, 0.5933, 0.9911, 0.2482, 0.3007, 0.9054,
0.7598, 0.5394, 0.2842, 0.5634, 0.6363, 0.2226, 0.2288};
Tensor::CreateFromVector(test_vector, TensorShape({5, 3}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check Q
std::shared_ptr<TensorTransform> lowpass_biquad_op = std::make_shared<audio::LowpassBiquad>(44100, 3000.5, 0);
mindspore::dataset::Execute transform({lowpass_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestLowpassBiuqadParamCheckSampleRate) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestLowpassBiuqadParamCheckSampleRate.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.5, 4.6, 2.2, 0.6, 1.9, 4.7, 2.3, 4.9, 4.7, 0.5, 0.8, 0.9};
Tensor::CreateFromVector(test_vector, TensorShape({6, 2}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check sample_rate
std::shared_ptr<TensorTransform> lowpass_biquad_op = std::make_shared<audio::LowpassBiquad>(0, 2000.5, 0.7);
mindspore::dataset::Execute transform({lowpass_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestComplexNormEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestComplexNormEager.";
// testing
std::shared_ptr<Tensor> input_tensor_;
Tensor::CreateFromVector(std::vector<float>({1.0, 1.0, 2.0, 3.0, 4.0, 4.0}), TensorShape({3, 2}), &input_tensor_);
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> complex_norm_01 = std::make_shared<audio::ComplexNorm>(4.0);
// Filtered waveform by complexnorm
mindspore::dataset::Execute Transform01({complex_norm_01});
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestContrastWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestContrastWithEager.";
// Original waveform
std::vector<float> labels = {4.11, 5.37, 5.85, 5.4, 4.27, 1.861, -1.1291, -4.76, 1.495};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({3, 3}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> contrast_01 = std::make_shared<audio::Contrast>();
mindspore::dataset::Execute Transform01({contrast_01});
// Filtered waveform by contrast
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestContrastWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestContrastWithWrongArg.";
std::vector<double> labels = {-1.007, -5.06, 7.934, 6.683, 1.312, 1.84, 2.246, 2.597};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 4}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check enhancement_amount
MS_LOG(INFO) << "enhancement_amount is negative.";
std::shared_ptr<TensorTransform> contrast_op = std::make_shared<audio::Contrast>(-10);
mindspore::dataset::Execute Transform01({contrast_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestDeemphBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestDeemphBiquadWithEager";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> deemph_biquad_01 = std::make_shared<audio::DeemphBiquad>(44100);
mindspore::dataset::Execute Transform01({deemph_biquad_01});
// Filtered waveform by deemphbiquad
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestDeemphBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestDeemphBiquadWithWrongArg.";
std::vector<double> labels = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({1, 6}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check sample_rate
MS_LOG(INFO) << "sample_rate is zero.";
std::shared_ptr<TensorTransform> deemph_biquad_op = std::make_shared<audio::DeemphBiquad>(0);
mindspore::dataset::Execute Transform01({deemph_biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestHighpassBiquadEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestHighpassBiquadEager.";
int sample_rate = 44100;
float cutoff_freq = 3000.5;
float Q = 0.707;
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.8236, 0.2049, 0.3335, 0.5933, 0.9911, 0.2482, 0.3007, 0.9054,
0.7598, 0.5394, 0.2842, 0.5634, 0.6363, 0.2226, 0.2288};
Tensor::CreateFromVector(test_vector, TensorShape({5, 3}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
std::shared_ptr<TensorTransform> highpass_biquad(new audio::HighpassBiquad({sample_rate, cutoff_freq, Q}));
auto transform = Execute({highpass_biquad});
Status rc = transform({input}, &output);
ASSERT_TRUE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestHighpassBiquadParamCheckQ) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestHighpassBiquadParamCheckQ.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<float> test_vector = {0.6013, 0.8081, 0.6600, 0.4278, 0.4049, 0.0541, 0.8800, 0.7143, 0.0926, 0.3502,
0.6148, 0.8738, 0.1869, 0.9023, 0.4293, 0.2175, 0.5132, 0.2622, 0.6490, 0.0741,
0.7903, 0.3428, 0.1598, 0.4841, 0.8128, 0.7409, 0.7226, 0.4951, 0.5589, 0.9210};
Tensor::CreateFromVector(test_vector, TensorShape({5, 3, 2}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check Q
std::shared_ptr<TensorTransform> highpass_biquad_op = std::make_shared<audio::HighpassBiquad>(44100, 3000.5, 0);
mindspore::dataset::Execute transform({highpass_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestHighpassBiquadParamCheckSampleRate) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestHighpassBiquadParamCheckSampleRate.";
std::vector<mindspore::MSTensor> output;
std::shared_ptr<Tensor> test;
std::vector<double> test_vector = {0.0237, 0.6026, 0.3801, 0.1978, 0.8672, 0.0095, 0.5166, 0.2641, 0.5485, 0.5144};
Tensor::CreateFromVector(test_vector, TensorShape({1, 10}), &test);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
// Check sample_rate
std::shared_ptr<TensorTransform> highpass_biquad_op = std::make_shared<audio::HighpassBiquad>(0, 3000.5, 0.7);
mindspore::dataset::Execute transform({highpass_biquad_op});
Status rc = transform({input}, &output);
ASSERT_FALSE(rc.IsOk());
}
TEST_F(MindDataTestExecute, TestMuLawDecodingEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestMuLawDecodingEager.";
// testing
std::shared_ptr<Tensor> input_tensor_;
Tensor::CreateFromVector(std::vector<float>({1, 254, 231, 155, 101, 77}), TensorShape({1, 6}), &input_tensor_);
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> mu_law_encoding_01 = std::make_shared<audio::MuLawDecoding>(255);
// Filtered waveform by mulawencoding
mindspore::dataset::Execute Transform01({mu_law_encoding_01});
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestLFilterWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestLFilterWithEager.";
// Original waveform
std::vector<float> labels = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 10}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::vector<float> a_coeffs = {0.1, 0.2, 0.3};
std::vector<float> b_coeffs = {0.1, 0.2, 0.3};
std::shared_ptr<TensorTransform> lfilter_01 = std::make_shared<audio::LFilter>(a_coeffs, b_coeffs);
mindspore::dataset::Execute Transform01({lfilter_01});
// Filtered waveform by lfilter
Status s01 = Transform01(input_02, &input_02);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestLFilterWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestLFilterWithWrongArg.";
std::vector<double> labels = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({1, 6}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check a_coeffs size equal to b_coeffs
MS_LOG(INFO) << "a_coeffs size not equal to b_coeffs";
std::vector<float> a_coeffs = {0.1, 0.2, 0.3};
std::vector<float> b_coeffs = {0.1, 0.2};
std::shared_ptr<TensorTransform> lfilter_op = std::make_shared<audio::LFilter>(a_coeffs, b_coeffs);
mindspore::dataset::Execute Transform01({lfilter_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestDCShiftEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestDCShiftEager.";
std::vector<float> origin = {0.67443, 1.87523, 0.73465, -0.74553, -1.54346, 1.54093, -1.23453};
std::shared_ptr<Tensor> de_tensor;
Tensor::CreateFromVector(origin, &de_tensor);
std::shared_ptr<TensorTransform> dc_shift = std::make_shared<audio::DCShift>(0.5, 0.02);
auto input = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
mindspore::dataset::Execute Transform({dc_shift});
Status s = Transform(input, &input);
ASSERT_TRUE(s.IsOk());
}
TEST_F(MindDataTestExecute, TestBiquadWithEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBiquadWithEager.";
// Original waveform
std::vector<float> labels = {3.716064453125, 12.34765625, 5.246826171875, 1.0894775390625,
1.1383056640625, 2.1566162109375, 1.3946533203125, 3.55029296875};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({2, 4}), &input));
auto input_01 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> biquad_01 = std::make_shared<audio::Biquad>(1, 0.02, 0.13, 1, 0.12, 0.3);
mindspore::dataset::Execute Transform01({biquad_01});
// Filtered waveform by biquad
Status s01 = Transform01(input_01, &input_01);
EXPECT_TRUE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestBiquadWithWrongArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestBiquadWithWrongArg.";
std::vector<double> labels = {
2.716064453125000000e-03,
6.347656250000000000e-03,
9.246826171875000000e-03,
1.089477539062500000e-02,
};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(labels, TensorShape({1, 4}), &input));
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
// Check a0
MS_LOG(INFO) << "a0 is zero.";
std::shared_ptr<TensorTransform> biquad_op = std::make_shared<audio::Biquad>(1, 0.02, 0.13, 0, 0.12, 0.3);
mindspore::dataset::Execute Transform01({biquad_op});
Status s01 = Transform01(input_02, &input_02);
EXPECT_FALSE(s01.IsOk());
}
TEST_F(MindDataTestExecute, TestFade) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestFade.";
std::vector<float> waveform = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(waveform, TensorShape({1, 20}), &input));
auto input_01 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade01 = std::make_shared<audio::Fade>(5, 6, FadeShape::kLinear);
mindspore::dataset::Execute Transform01({fade01});
Status s01 = Transform01(input_01, &input_01);
EXPECT_TRUE(s01.IsOk());
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade02 = std::make_shared<audio::Fade>(5, 6, FadeShape::kQuarterSine);
mindspore::dataset::Execute Transform02({fade02});
Status s02 = Transform02(input_02, &input_02);
EXPECT_TRUE(s02.IsOk());
auto input_03 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade03 = std::make_shared<audio::Fade>(5, 6, FadeShape::kExponential);
mindspore::dataset::Execute Transform03({fade03});
Status s03 = Transform03(input_03, &input_03);
EXPECT_TRUE(s03.IsOk());
auto input_04 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade04 = std::make_shared<audio::Fade>(5, 6, FadeShape::kHalfSine);
mindspore::dataset::Execute Transform04({fade04});
Status s04 = Transform01(input_04, &input_04);
EXPECT_TRUE(s04.IsOk());
auto input_05 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade05 = std::make_shared<audio::Fade>(5, 6, FadeShape::kLogarithmic);
mindspore::dataset::Execute Transform05({fade05});
Status s05 = Transform01(input_05, &input_05);
EXPECT_TRUE(s05.IsOk());
}
TEST_F(MindDataTestExecute, TestFadeDefaultArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestFadeDefaultArg.";
std::vector<double> waveform = {
1.573897564868000000e-03, 5.462374385400000000e-03, 3.584989689205400000e-03, 2.035667767462500000e-02,
2.353543454062500000e-02, 1.256616210937500000e-02, 2.394653320312500000e-02, 5.243553968750000000e-02,
2.434554533002500000e-02, 3.454566960937500000e-02, 2.343545454437500000e-02, 2.534343093750000000e-02,
2.354465654550000000e-02, 1.453545517187500000e-02, 1.454645535875000000e-02, 1.433243195312500000e-02,
1.434354554812500000e-02, 3.343435276865400000e-02, 1.234257687312500000e-02, 5.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(waveform, TensorShape({2, 10}), &input));
auto input_01 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade01 = std::make_shared<audio::Fade>();
mindspore::dataset::Execute Transform01({fade01});
Status s01 = Transform01(input_01, &input_01);
EXPECT_TRUE(s01.IsOk());
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade02 = std::make_shared<audio::Fade>(5);
mindspore::dataset::Execute Transform02({fade02});
Status s02 = Transform02(input_02, &input_02);
EXPECT_TRUE(s02.IsOk());
auto input_03 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade03 = std::make_shared<audio::Fade>(5, 6);
mindspore::dataset::Execute Transform03({fade03});
Status s03 = Transform03(input_03, &input_03);
EXPECT_TRUE(s03.IsOk());
}
TEST_F(MindDataTestExecute, TestFadeWithInvalidArg) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestFadeWithInvalidArg.";
std::vector<float> waveform = {
2.716064453125000000e-03, 6.347656250000000000e-03, 9.246826171875000000e-03, 1.089477539062500000e-02,
1.138305664062500000e-02, 1.156616210937500000e-02, 1.394653320312500000e-02, 1.550292968750000000e-02,
1.614379882812500000e-02, 1.840209960937500000e-02, 1.718139648437500000e-02, 1.599121093750000000e-02,
1.647949218750000000e-02, 1.510620117187500000e-02, 1.385498046875000000e-02, 1.345825195312500000e-02,
1.419067382812500000e-02, 1.284790039062500000e-02, 1.052856445312500000e-02, 9.368896484375000000e-03};
std::shared_ptr<Tensor> input;
ASSERT_OK(Tensor::CreateFromVector(waveform, TensorShape({1, 20}), &input));
auto input_01 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade1 = std::make_shared<audio::Fade>(-5, 6);
mindspore::dataset::Execute Transform01({fade1});
Status s01 = Transform01(input_01, &input_01);
EXPECT_FALSE(s01.IsOk());
auto input_02 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade2 = std::make_shared<audio::Fade>(0, -1);
mindspore::dataset::Execute Transform02({fade2});
Status s02 = Transform02(input_02, &input_02);
EXPECT_FALSE(s02.IsOk());
auto input_03 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade3 = std::make_shared<audio::Fade>(30, 10);
mindspore::dataset::Execute Transform03({fade3});
Status s03 = Transform03(input_03, &input_03);
EXPECT_FALSE(s03.IsOk());
auto input_04 = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input));
std::shared_ptr<TensorTransform> fade4 = std::make_shared<audio::Fade>(10, 30);
mindspore::dataset::Execute Transform04({fade4});
Status s04 = Transform04(input_04, &input_04);
EXPECT_FALSE(s04.IsOk());
}
TEST_F(MindDataTestExecute, TestVolDefalutValue) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestVolDefalutValue.";
std::shared_ptr<Tensor> input_tensor_;
TensorShape s = TensorShape({2, 6});
ASSERT_OK(Tensor::CreateFromVector(
std::vector<float>({1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}), s, &input_tensor_));
auto input_tensor = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> vol_op = std::make_shared<audio::Vol>(0.333);
mindspore::dataset::Execute transform({vol_op});
Status status = transform(input_tensor, &input_tensor);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestVolGainTypePower) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestVolGainTypePower.";
std::shared_ptr<Tensor> input_tensor_;
TensorShape s = TensorShape({4, 3});
ASSERT_OK(Tensor::CreateFromVector(
std::vector<double>({4.0f, 5.0f, 3.0f, 5.0f, 4.0f, 6.0f, 6.0f, 1.0f, 2.0f, 3.0f, 2.0f, 1.0f}), s, &input_tensor_));
auto input_tensor = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(input_tensor_));
std::shared_ptr<TensorTransform> vol_op = std::make_shared<audio::Vol>(0.2, GainType::kPower);
mindspore::dataset::Execute transform({vol_op});
Status status = transform(input_tensor, &input_tensor);
EXPECT_TRUE(status.IsOk());
}
TEST_F(MindDataTestExecute, TestMagphaseEager) {
MS_LOG(INFO) << "Doing MindDataTestExecute-TestMagphaseEager.";
float power = 1.0;
std::vector<mindspore::MSTensor> output_tensor;
std::shared_ptr<Tensor> test;
std::vector<float> test_vector = {3, 4, -3, 4, 3, -4, -3, -4,
5, 12, -5, 12, 5, -12, -5, -12};
Tensor::CreateFromVector(test_vector, TensorShape({2, 4, 2}), &test);
auto input_tensor = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(test));
std::shared_ptr<TensorTransform> magphase(new audio::Magphase({power}));
auto transform = Execute({magphase});
Status rc = transform({input_tensor}, &output_tensor);
ASSERT_TRUE(rc.IsOk());
}
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