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

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/**
* 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 "minddata/dataset/include/datasets.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/vision.h"
// IR non-leaf nodes
#include "minddata/dataset/engine/ir/datasetops/batch_node.h"
#include "minddata/dataset/engine/ir/datasetops/bucket_batch_by_length_node.h"
#include "minddata/dataset/engine/ir/datasetops/concat_node.h"
#include "minddata/dataset/engine/ir/datasetops/map_node.h"
#include "minddata/dataset/engine/ir/datasetops/project_node.h"
#include "minddata/dataset/engine/ir/datasetops/rename_node.h"
#include "minddata/dataset/engine/ir/datasetops/shuffle_node.h"
#include "minddata/dataset/engine/ir/datasetops/skip_node.h"
#include "minddata/dataset/engine/ir/datasetops/zip_node.h"
// IR leaf nodes
#include "minddata/dataset/engine/ir/datasetops/source/cifar10_node.h"
#include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
#include "minddata/dataset/engine/ir/datasetops/source/mnist_node.h"
#include "minddata/dataset/engine/ir/datasetops/source/voc_node.h"
using namespace mindspore::dataset;
class MindDataTestPipeline : public UT::DatasetOpTesting {
protected:
};
// Tests for vision ops (in alphabetical order)
TEST_F(MindDataTestPipeline, TestAutoContrastSuccess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastSuccess1.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 3;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create auto contrast object with default values
std::shared_ptr<TensorOperation> auto_contrast = vision::AutoContrast();
EXPECT_NE(auto_contrast, nullptr);
// Create a Map operation on ds
ds = ds->Map({auto_contrast});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 15);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestAutoContrastSuccess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastSuccess2.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 3;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create auto contrast object
std::shared_ptr<TensorOperation> auto_contrast = vision::AutoContrast(10, {10, 20});
EXPECT_NE(auto_contrast, nullptr);
// Create a Map operation on ds
ds = ds->Map({auto_contrast});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 15);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestAutoContrastFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastFail with invalid params.";
// Testing invalid cutoff < 0
std::shared_ptr<TensorOperation> auto_contrast1 = vision::AutoContrast(-1.0);
EXPECT_EQ(auto_contrast1, nullptr);
// Testing invalid cutoff > 100
std::shared_ptr<TensorOperation> auto_contrast2 = vision::AutoContrast(110.0, {10, 20});
EXPECT_EQ(auto_contrast2, nullptr);
}
TEST_F(MindDataTestPipeline, TestCenterCrop) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCenterCrop with single integer input.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 3;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create centre crop object with square crop
std::shared_ptr<TensorOperation> centre_out1 = vision::CenterCrop({30});
EXPECT_NE(centre_out1, nullptr);
// Create a Map operation on ds
ds = ds->Map({centre_out1});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 15);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestCenterCropFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCenterCrop with invalid parameters.";
// center crop height value negative
std::shared_ptr<TensorOperation> center_crop = mindspore::dataset::vision::CenterCrop({-32, 32});
EXPECT_EQ(center_crop, nullptr);
// center crop width value negative
center_crop = mindspore::dataset::vision::CenterCrop({32, -32});
EXPECT_EQ(center_crop, nullptr);
// 0 value would result in nullptr
center_crop = mindspore::dataset::vision::CenterCrop({0, 32});
EXPECT_EQ(center_crop, nullptr);
// center crop with 3 values
center_crop = mindspore::dataset::vision::CenterCrop({10, 20, 30});
EXPECT_EQ(center_crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestCropFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCrop with invalid parameters.";
// wrong width
std::shared_ptr<TensorOperation> crop = mindspore::dataset::vision::Crop({0, 0}, {32, -32});
EXPECT_EQ(crop, nullptr);
// wrong height
crop = mindspore::dataset::vision::Crop({0, 0}, {-32, -32});
EXPECT_EQ(crop, nullptr);
// zero height
crop = mindspore::dataset::vision::Crop({0, 0}, {0, 32});
EXPECT_EQ(crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutMixBatchSuccess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchSuccess1.";
// Testing CutMixBatch on a batch of CHW images
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
int number_of_classes = 10;
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> hwc_to_chw = vision::HWC2CHW();
EXPECT_NE(hwc_to_chw, nullptr);
// Create a Map operation on ds
ds = ds->Map({hwc_to_chw}, {"image"});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(number_of_classes);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op =
vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNCHW, 1.0, 1.0);
EXPECT_NE(cutmix_batch_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({cutmix_batch_op}, {"image", "label"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
auto label = row["label"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
MS_LOG(INFO) << "Label shape: " << label->shape();
EXPECT_EQ(image->shape().AsVector().size() == 4 && batch_size == image->shape()[0] && 3 == image->shape()[1] &&
32 == image->shape()[2] && 32 == image->shape()[3],
true);
EXPECT_EQ(label->shape().AsVector().size() == 2 && batch_size == label->shape()[0] &&
number_of_classes == label->shape()[1],
true);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 2);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestCutMixBatchSuccess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchSuccess2.";
// Calling CutMixBatch on a batch of HWC images with default values of alpha and prob
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
int number_of_classes = 10;
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(number_of_classes);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC);
EXPECT_NE(cutmix_batch_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({cutmix_batch_op}, {"image", "label"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
auto label = row["label"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
MS_LOG(INFO) << "Label shape: " << label->shape();
EXPECT_EQ(image->shape().AsVector().size() == 4 && batch_size == image->shape()[0] && 32 == image->shape()[1] &&
32 == image->shape()[2] && 3 == image->shape()[3],
true);
EXPECT_EQ(label->shape().AsVector().size() == 2 && batch_size == label->shape()[0] &&
number_of_classes == label->shape()[1],
true);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 2);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestCutMixBatchFail1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail1 with invalid negative alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op =
vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, -1, 0.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutMixBatchFail2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail2 with invalid negative prob parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op =
vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, -0.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutMixBatchFail3) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail3 with invalid zero alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op =
vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 0.0, 0.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutMixBatchFail4) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail4 with invalid greater than 1 prob parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 10;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op =
vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, 1.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutOutFail1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOutFail1 with invalid parameters.";
// Create object for the tensor op
// Invalid negative length
std::shared_ptr<TensorOperation> cutout_op = vision::CutOut(-10);
EXPECT_EQ(cutout_op, nullptr);
// Invalid negative number of patches
cutout_op = vision::CutOut(10, -1);
EXPECT_EQ(cutout_op, nullptr);
}
TEST_F(MindDataTestPipeline, DISABLED_TestCutOutFail2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOutFail2 with invalid params, boundary cases.";
// Create object for the tensor op
// Invalid zero length
std::shared_ptr<TensorOperation> cutout_op = vision::CutOut(0);
EXPECT_EQ(cutout_op, nullptr);
// Invalid zero number of patches
cutout_op = vision::CutOut(10, 0);
EXPECT_EQ(cutout_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutOut) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOut.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> cut_out1 = vision::CutOut(30, 5);
EXPECT_NE(cut_out1, nullptr);
std::shared_ptr<TensorOperation> cut_out2 = vision::CutOut(30);
EXPECT_NE(cut_out2, nullptr);
// Create a Map operation on ds
ds = ds->Map({cut_out1, cut_out2});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestDecode) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestDecode.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> decode = vision::Decode(true);
EXPECT_NE(decode, nullptr);
// Create a Map operation on ds
ds = ds->Map({decode});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestHwcToChw) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestHwcToChw.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> channel_swap = vision::HWC2CHW();
EXPECT_NE(channel_swap, nullptr);
// Create a Map operation on ds
ds = ds->Map({channel_swap});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
// check if the image is in NCHW
EXPECT_EQ(batch_size == image->shape()[0] && 3 == image->shape()[1] && 2268 == image->shape()[2] &&
4032 == image->shape()[3],
true);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestInvert) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestInvert.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 20));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> invert_op = vision::Invert();
EXPECT_NE(invert_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({invert_op});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestMixUpBatchFail1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchFail1 with negative alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(-1);
EXPECT_EQ(mixup_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestMixUpBatchFail2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchFail2 with zero alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.0);
EXPECT_EQ(mixup_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchSuccess1 with explicit alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(2.0);
EXPECT_NE(mixup_batch_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({mixup_batch_op}, {"image", "label"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 2);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchSuccess1 with default alpha parameter.";
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch();
EXPECT_NE(mixup_batch_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({mixup_batch_op}, {"image", "label"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 2);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestNormalize) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalize.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> normalize = vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
EXPECT_NE(normalize, nullptr);
// Create a Map operation on ds
ds = ds->Map({normalize});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestNormalizeFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalizeFail with invalid parameters.";
// std value at 0.0
std::shared_ptr<TensorOperation> normalize =
mindspore::dataset::vision::Normalize({121.0, 115.0, 100.0}, {0.0, 68.0, 71.0});
EXPECT_EQ(normalize, nullptr);
// normalize with 2 values (not 3 values) for mean
normalize = mindspore::dataset::vision::Normalize({121.0, 115.0}, {70.0, 68.0, 71.0});
EXPECT_EQ(normalize, nullptr);
// normalize with 2 values (not 3 values) for standard deviation
normalize = mindspore::dataset::vision::Normalize({121.0, 115.0, 100.0}, {68.0, 71.0});
EXPECT_EQ(normalize, nullptr);
}
TEST_F(MindDataTestPipeline, TestPad) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestPad.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> pad_op1 = vision::Pad({1, 2, 3, 4}, {0}, BorderType::kSymmetric);
EXPECT_NE(pad_op1, nullptr);
std::shared_ptr<TensorOperation> pad_op2 = vision::Pad({1}, {1, 1, 1}, BorderType::kEdge);
EXPECT_NE(pad_op2, nullptr);
std::shared_ptr<TensorOperation> pad_op3 = vision::Pad({1, 4});
EXPECT_NE(pad_op3, nullptr);
// Create a Map operation on ds
ds = ds->Map({pad_op1, pad_op2, pad_op3});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomAffineFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineFail with invalid parameters.";
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> affine = vision::RandomAffine({0.0, 0.0}, {});
EXPECT_EQ(affine, nullptr);
// Invalid number of values for translate
affine = vision::RandomAffine({0.0, 0.0}, {1, 1, 1, 1, 1});
EXPECT_EQ(affine, nullptr);
// Invalid number of values for shear
affine = vision::RandomAffine({30.0, 30.0}, {0.0, 0.0}, {2.0, 2.0}, {10.0});
EXPECT_EQ(affine, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomAffineSuccess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineSuccess1 with non-default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> affine =
vision::RandomAffine({30.0, 30.0}, {-1.0, 1.0, -1.0, 1.0}, {2.0, 2.0}, {10.0, 10.0, 20.0, 20.0});
EXPECT_NE(affine, nullptr);
// Create a Map operation on ds
ds = ds->Map({affine});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomAffineSuccess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineSuccess2 with default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> affine = vision::RandomAffine({0.0, 0.0});
EXPECT_NE(affine, nullptr);
// Create a Map operation on ds
ds = ds->Map({affine});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomColor) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColor with non-default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Valid case: Set lower bound and upper bound to be the same value zero
std::shared_ptr<TensorOperation> random_color_op_1 = vision::RandomColor(0.0, 0.0);
EXPECT_NE(random_color_op_1, nullptr);
// Failure case: Set invalid lower bound greater than upper bound
std::shared_ptr<TensorOperation> random_color_op_2 = vision::RandomColor(1.0, 0.1);
EXPECT_EQ(random_color_op_2, nullptr);
// Valid case: Set lower bound as zero and less than upper bound
std::shared_ptr<TensorOperation> random_color_op_3 = vision::RandomColor(0.0, 1.1);
EXPECT_NE(random_color_op_3, nullptr);
// Failure case: Set invalid negative lower bound
std::shared_ptr<TensorOperation> random_color_op_4 = vision::RandomColor(-0.5, 0.5);
EXPECT_EQ(random_color_op_4, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_color_op_1, random_color_op_3});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomColorAdjust) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColorAdjust.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Use single value for vectors
std::shared_ptr<TensorOperation> random_color_adjust1 = vision::RandomColorAdjust({1.0}, {0.0}, {0.5}, {0.5});
EXPECT_NE(random_color_adjust1, nullptr);
// Use same 2 values for vectors
std::shared_ptr<TensorOperation> random_color_adjust2 =
vision::RandomColorAdjust({1.0, 1.0}, {0.0, 0.0}, {0.5, 0.5}, {0.5, 0.5});
EXPECT_NE(random_color_adjust2, nullptr);
// Use different 2 value for vectors
std::shared_ptr<TensorOperation> random_color_adjust3 =
vision::RandomColorAdjust({0.5, 1.0}, {0.0, 0.5}, {0.25, 0.5}, {0.25, 0.5});
EXPECT_NE(random_color_adjust3, nullptr);
// Use default input values
std::shared_ptr<TensorOperation> random_color_adjust4 = vision::RandomColorAdjust();
EXPECT_NE(random_color_adjust4, nullptr);
// Use subset of explictly set parameters
std::shared_ptr<TensorOperation> random_color_adjust5 = vision::RandomColorAdjust({0.0, 0.5}, {0.25});
EXPECT_NE(random_color_adjust5, nullptr);
// Create a Map operation on ds
ds = ds->Map(
{random_color_adjust1, random_color_adjust2, random_color_adjust3, random_color_adjust4, random_color_adjust5});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomCropSuccess) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropSuccess.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Testing siez of size vector is 1
std::shared_ptr<TensorOperation> random_crop = vision::RandomCrop({20});
EXPECT_NE(random_crop, nullptr);
// Testing siez of size vector is 2
std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCrop({20, 20});
EXPECT_NE(random_crop1, nullptr);
// Testing siez of paddiing vector is 1
std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCrop({20, 20}, {10});
EXPECT_NE(random_crop2, nullptr);
// Testing siez of paddiing vector is 2
std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCrop({20, 20}, {10, 20});
EXPECT_NE(random_crop3, nullptr);
// Testing siez of paddiing vector is 2
std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCrop({20, 20}, {10, 10, 10, 10});
EXPECT_NE(random_crop4, nullptr);
// Testing siez of fill_value vector is 1
std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCrop({20, 20}, {10, 10, 10, 10}, false, {5});
EXPECT_NE(random_crop5, nullptr);
// Testing siez of fill_value vector is 3
std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCrop({20, 20}, {10, 10, 10, 10}, false, {4, 4, 4});
EXPECT_NE(random_crop6, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_crop, random_crop1, random_crop2, random_crop3, random_crop4, random_crop5, random_crop6});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 10);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomCropFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropFail with invalid parameters.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Testing the size parameter is negative.
std::shared_ptr<TensorOperation> random_crop = vision::RandomCrop({-28, 28});
EXPECT_EQ(random_crop, nullptr);
// Testing the size parameter is None.
std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCrop({});
EXPECT_EQ(random_crop1, nullptr);
// Testing the size of size vector is 3.
std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCrop({28, 28, 28});
EXPECT_EQ(random_crop2, nullptr);
// Testing the padding parameter is negative.
std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCrop({28, 28}, {-5});
EXPECT_EQ(random_crop3, nullptr);
// Testing the size of padding vector is empty.
std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCrop({28, 28}, {});
EXPECT_EQ(random_crop4, nullptr);
// Testing the size of padding vector is 3.
std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCrop({28, 28}, {5, 5, 5});
EXPECT_EQ(random_crop5, nullptr);
// Testing the size of padding vector is 5.
std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCrop({28, 28}, {5, 5, 5, 5, 5});
EXPECT_EQ(random_crop6, nullptr);
// Testing the size of fill_value vector is empty.
std::shared_ptr<TensorOperation> random_crop7 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {});
EXPECT_EQ(random_crop7, nullptr);
// Testing the size of fill_value vector is 2.
std::shared_ptr<TensorOperation> random_crop8 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {0, 0});
EXPECT_EQ(random_crop8, nullptr);
// Testing the size of fill_value vector is 4.
std::shared_ptr<TensorOperation> random_crop9 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {0, 0, 0, 0});
EXPECT_EQ(random_crop9, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomCropWithBboxSuccess) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropWithBboxSuccess.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_crop = mindspore::dataset::vision::RandomCropWithBBox({128, 128});
EXPECT_NE(random_crop, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_crop}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
EXPECT_EQ(image->shape()[0], 128);
EXPECT_EQ(image->shape()[1], 128);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 3);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomCropWithBboxFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropWithBboxFail with invalid parameters.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// The size parameter is negative.
std::shared_ptr<TensorOperation> random_crop = vision::RandomCropWithBBox({-10});
EXPECT_EQ(random_crop, nullptr);
// The parameter in the padding vector is negative.
std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCropWithBBox({10, 10}, {-2, 2, 2, 2});
EXPECT_EQ(random_crop1, nullptr);
// The size container is empty.
std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCropWithBBox({});
EXPECT_EQ(random_crop2, nullptr);
// The size of the size container is too large.
std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCropWithBBox({10, 10, 10});
EXPECT_EQ(random_crop3, nullptr);
// The padding container is empty.
std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCropWithBBox({10, 10}, {});
EXPECT_EQ(random_crop4, nullptr);
// The size of the padding container is too large.
std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5, 5});
EXPECT_EQ(random_crop5, nullptr);
// The fill_value container is empty.
std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5}, false, {});
EXPECT_EQ(random_crop6, nullptr);
// The size of the fill_value container is too large.
std::shared_ptr<TensorOperation> random_crop7 =
vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5}, false, {3, 3, 3, 3});
EXPECT_EQ(random_crop7, nullptr);
}
TEST_F(MindDataTestPipeline, DISABLED_TestRandomHorizontalFlipFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipFail with invalid parameters.";
// Create object for the tensor op
// Invalid zero input
std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0);
EXPECT_EQ(random_horizontal_flip_op, nullptr);
// Invalid >1 input
random_horizontal_flip_op = vision::RandomHorizontalFlip(2);
EXPECT_EQ(random_horizontal_flip_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomHorizontalFlipWithBBoxSuccess) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipWithBBoxSuccess.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlipWithBBox(0.5);
EXPECT_NE(random_horizontal_flip_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_horizontal_flip_op}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 3);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomHorizontalFlipWithBBoxFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipWithBBoxFail with invalid parameters.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Incorrect prob parameter.
std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlipWithBBox(-1.0);
EXPECT_EQ(random_horizontal_flip_op, nullptr);
// Incorrect prob parameter.
std::shared_ptr<TensorOperation> random_horizontal_flip_op1 = vision::RandomHorizontalFlipWithBBox(2.0);
EXPECT_EQ(random_horizontal_flip_op1, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomHorizontalAndVerticalFlip) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalAndVerticalFlip for horizontal and vertical flips.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.75);
EXPECT_NE(random_vertical_flip_op, nullptr);
std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0.5);
EXPECT_NE(random_horizontal_flip_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_vertical_flip_op, random_horizontal_flip_op});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomPosterizeFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeFail with invalid parameters.";
// Create objects for the tensor ops
// Invalid max > 8
std::shared_ptr<TensorOperation> posterize = vision::RandomPosterize({1, 9});
EXPECT_EQ(posterize, nullptr);
// Invalid min < 1
posterize = vision::RandomPosterize({0, 8});
EXPECT_EQ(posterize, nullptr);
// min > max
posterize = vision::RandomPosterize({8, 1});
EXPECT_EQ(posterize, nullptr);
// empty
posterize = vision::RandomPosterize({});
EXPECT_EQ(posterize, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomPosterizeSuccess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess1 with non-default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> posterize = vision::RandomPosterize({1, 4});
EXPECT_NE(posterize, nullptr);
// Create a Map operation on ds
ds = ds->Map({posterize});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomPosterizeSuccess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess2 with default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> posterize = vision::RandomPosterize();
EXPECT_NE(posterize, nullptr);
// Create a Map operation on ds
ds = ds->Map({posterize});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropSuccess1) {
// Testing RandomResizedCrop with default values
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5});
EXPECT_NE(random_resized_crop, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_resized_crop}, {"image"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 5, true);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 10);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropSuccess2) {
// Testing RandomResizedCrop with non-default values
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop =
vision::RandomResizedCrop({5, 10}, {0.25, 0.75}, {0.5, 1.25}, mindspore::dataset::InterpolationMode::kArea, 20);
EXPECT_NE(random_resized_crop, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_resized_crop}, {"image"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 10, true);
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 10);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropFail1) {
// This should fail because size has negative value
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, -10});
EXPECT_EQ(random_resized_crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropFail2) {
// This should fail because scale isn't in {min, max} format
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10}, {4, 3});
EXPECT_EQ(random_resized_crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropFail3) {
// This should fail because ratio isn't in {min, max} format
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10}, {4, 5}, {7, 6});
EXPECT_EQ(random_resized_crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomResizedCropFail4) {
// This should fail because scale has a size of more than 2
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10, 20}, {4, 5}, {7, 6});
EXPECT_EQ(random_resized_crop, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomRotation) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomRotation.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Testing the size of degrees is 1
std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({180});
EXPECT_NE(random_rotation_op, nullptr);
// Testing the size of degrees is 2
std::shared_ptr<TensorOperation> random_rotation_op1 = vision::RandomRotation({-180, 180});
EXPECT_NE(random_rotation_op1, nullptr);
// Testing the size of fill_value is 1
std::shared_ptr<TensorOperation> random_rotation_op2 =
vision::RandomRotation({180}, InterpolationMode::kNearestNeighbour, false, {-1, -1}, {2});
EXPECT_NE(random_rotation_op2, nullptr);
// Testing the size of fill_value is 3
std::shared_ptr<TensorOperation> random_rotation_op3 =
vision::RandomRotation({180}, InterpolationMode::kNearestNeighbour, false, {-1, -1}, {2, 2, 2});
EXPECT_NE(random_rotation_op3, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_rotation_op, random_rotation_op1, random_rotation_op2, random_rotation_op3});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomRotationFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomRotationFail with invalid parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Testing the size of degrees vector is 0
std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({});
EXPECT_EQ(random_rotation_op, nullptr);
// Testing the size of degrees vector is 3
std::shared_ptr<TensorOperation> random_rotation_op1 = vision::RandomRotation({-50.0, 50.0, 100.0});
EXPECT_EQ(random_rotation_op1, nullptr);
// Test the case where the first column value of degrees is greater than the second column value
std::shared_ptr<TensorOperation> random_rotation_op2 = vision::RandomRotation({50.0, -50.0});
EXPECT_EQ(random_rotation_op2, nullptr);
// Testing the size of center vector is 1
std::shared_ptr<TensorOperation> random_rotation_op3 =
vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0});
EXPECT_EQ(random_rotation_op3, nullptr);
// Testing the size of center vector is 3
std::shared_ptr<TensorOperation> random_rotation_op4 =
vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0, -1.0});
EXPECT_EQ(random_rotation_op4, nullptr);
// Testing the size of fill_value vector is 2
std::shared_ptr<TensorOperation> random_rotation_op5 =
vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0}, {2, 2});
EXPECT_EQ(random_rotation_op5, nullptr);
// Testing the size of fill_value vector is 4
std::shared_ptr<TensorOperation> random_rotation_op6 =
vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0}, {2, 2, 2, 2});
EXPECT_EQ(random_rotation_op6, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomSharpness) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSharpness.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Valid case: Input start degree and end degree
std::shared_ptr<TensorOperation> random_sharpness_op_1 = vision::RandomSharpness({0.4, 2.3});
EXPECT_NE(random_sharpness_op_1, nullptr);
// Failure case: Empty degrees vector
std::shared_ptr<TensorOperation> random_sharpness_op_2 = vision::RandomSharpness({});
EXPECT_EQ(random_sharpness_op_2, nullptr);
// Valid case: Use default input values
std::shared_ptr<TensorOperation> random_sharpness_op_3 = vision::RandomSharpness();
EXPECT_NE(random_sharpness_op_3, nullptr);
// Failure case: Single degree value
std::shared_ptr<TensorOperation> random_sharpness_op_4 = vision::RandomSharpness({0.1});
EXPECT_EQ(random_sharpness_op_4, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_sharpness_op_1, random_sharpness_op_3});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomSolarizeSucess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeSucess1.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::vector<uint8_t> threshold = {10, 100};
std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
EXPECT_NE(random_solarize, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_solarize});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 10);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomSolarizeSucess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeSuccess2 with default parameters.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize();
EXPECT_NE(random_solarize, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_solarize});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 10);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomSolarizeFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeFail with invalid parameters.";
std::vector<uint8_t> threshold = {13, 1};
std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
EXPECT_EQ(random_solarize, nullptr);
threshold = {1, 2, 3};
random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
EXPECT_EQ(random_solarize, nullptr);
threshold = {1};
random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
EXPECT_EQ(random_solarize, nullptr);
threshold = {};
random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
EXPECT_EQ(random_solarize, nullptr);
}
TEST_F(MindDataTestPipeline, DISABLED_TestRandomVerticalFlipFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipFail with invalid parameters.";
// Create object for the tensor op
// Invalid zero input
std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0);
EXPECT_EQ(random_vertical_flip_op, nullptr);
// Invalid >1 input
random_vertical_flip_op = vision::RandomVerticalFlip(1.1);
EXPECT_EQ(random_vertical_flip_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestResizeFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResize with invalid parameters.";
// negative resize value
std::shared_ptr<TensorOperation> resize_op = mindspore::dataset::vision::Resize({30, -30});
EXPECT_EQ(resize_op, nullptr);
// zero resize value
resize_op = mindspore::dataset::vision::Resize({0, 30});
EXPECT_EQ(resize_op, nullptr);
// resize with 3 values
resize_op = mindspore::dataset::vision::Resize({30, 20, 10});
EXPECT_EQ(resize_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestRandomVerticalFlipWithBBoxSuccess) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipWithBBoxSuccess.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlipWithBBox(0.4);
EXPECT_NE(random_vertical_flip_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({random_vertical_flip_op}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 3);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRandomVerticalFlipWithBBoxFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipWithBBoxFail with invalid parameters.";
// Create an VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
// Incorrect prob parameter.
std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlipWithBBox(-0.5);
EXPECT_EQ(random_vertical_flip_op, nullptr);
// Incorrect prob parameter.
std::shared_ptr<TensorOperation> random_vertical_flip_op1 = vision::RandomVerticalFlipWithBBox(3.0);
EXPECT_EQ(random_vertical_flip_op1, nullptr);
}
TEST_F(MindDataTestPipeline, TestResize1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResize1 with single integer input.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 6));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 4;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create resize object with single integer input
std::shared_ptr<TensorOperation> resize_op = vision::Resize({30});
EXPECT_NE(resize_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({resize_op});
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 1;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 24);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRescaleSucess1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleSucess1.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, SequentialSampler(0, 1));
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
auto image = row["image"];
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(1.0, 0.0);
EXPECT_NE(rescale, nullptr);
// Convert to the same type
std::shared_ptr<TensorOperation> type_cast = transforms::TypeCast("uint8");
EXPECT_NE(type_cast, nullptr);
ds = ds->Map({rescale, type_cast}, {"image"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter1 = ds->CreateIterator();
EXPECT_NE(iter1, nullptr);
// Iterate the dataset and get each row1
std::unordered_map<std::string, std::shared_ptr<Tensor>> row1;
iter1->GetNextRow(&row1);
auto image1 = row1["image"];
EXPECT_EQ(*image, *image1);
// Manually terminate the pipeline
iter1->Stop();
}
TEST_F(MindDataTestPipeline, TestRescaleSucess2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleSucess2 with different params.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 1));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(1.0 / 255, 1.0);
EXPECT_NE(rescale, nullptr);
ds = ds->Map({rescale}, {"image"});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 1);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestRescaleFail) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleFail with invalid params.";
// incorrect negative rescale parameter
std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(-1.0, 0.0);
EXPECT_EQ(rescale, nullptr);
}
TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail1 with invalid zero num_ops parameter.";
// Create a Mnist Dataset
std::string folder_path = datasets_root_path_ + "/testMnistData/";
std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
EXPECT_NE(random_crop_op, nullptr);
std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
EXPECT_NE(center_crop_op, nullptr);
// Try UniformAugment with invalid zero num_ops value
std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, 0);
EXPECT_EQ(uniform_aug_op, nullptr);
}
TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail2) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail2 with invalid negative num_ops parameter.";
// Create a Mnist Dataset
std::string folder_path = datasets_root_path_ + "/testMnistData/";
std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
EXPECT_NE(random_crop_op, nullptr);
std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
EXPECT_NE(center_crop_op, nullptr);
// Try UniformAugment with invalid negative num_ops value
std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, -1);
EXPECT_EQ(uniform_aug_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestUniformAugWithOps) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugWithOps.";
// Create a Mnist Dataset
std::string folder_path = datasets_root_path_ + "/testMnistData/";
std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 1;
ds = ds->Repeat(repeat_num);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
EXPECT_NE(resize_op, nullptr);
std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
EXPECT_NE(random_crop_op, nullptr);
std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
EXPECT_NE(center_crop_op, nullptr);
std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, 2);
EXPECT_NE(uniform_aug_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({resize_op, uniform_aug_op});
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
EXPECT_EQ(i, 20);
// Manually terminate the pipeline
iter->Stop();
}
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