You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
208 lines
6.5 KiB
208 lines
6.5 KiB
/**
|
|
* Copyright 2019-2021 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 <fstream>
|
|
#include <iostream>
|
|
#include <memory>
|
|
#include <string>
|
|
|
|
#include "common/common.h"
|
|
#include "utils/ms_utils.h"
|
|
#include "minddata/dataset/core/client.h"
|
|
#include "minddata/dataset/core/global_context.h"
|
|
#include "minddata/dataset/engine/datasetops/source/voc_op.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/distributed_sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/pk_sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/random_sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/subset_random_sampler.h"
|
|
#include "minddata/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h"
|
|
#include "minddata/dataset/util/path.h"
|
|
#include "minddata/dataset/util/status.h"
|
|
#include "gtest/gtest.h"
|
|
#include "utils/log_adapter.h"
|
|
#include "securec.h"
|
|
|
|
namespace common = mindspore::common;
|
|
|
|
using namespace mindspore::dataset;
|
|
using mindspore::LogStream;
|
|
using mindspore::ExceptionType::NoExceptionType;
|
|
using mindspore::MsLogLevel::ERROR;
|
|
|
|
std::shared_ptr<BatchOp> Batch(int batch_size = 1, bool drop = false);
|
|
|
|
std::shared_ptr<ExecutionTree> Build(std::vector<std::shared_ptr<DatasetOp>> ops);
|
|
|
|
class MindDataTestVOCOp : public UT::DatasetOpTesting {
|
|
protected:
|
|
};
|
|
|
|
TEST_F(MindDataTestVOCOp, TestVOCDetection) {
|
|
// Start with an empty execution tree
|
|
auto my_tree = std::make_shared<ExecutionTree>();
|
|
std::string dataset_path;
|
|
dataset_path = datasets_root_path_ + "/testVOC2012";
|
|
|
|
std::string task_type("Detection");
|
|
std::string task_mode("train");
|
|
std::shared_ptr<VOCOp> my_voc_op;
|
|
VOCOp::Builder builder;
|
|
Status rc = builder.SetDir(dataset_path).SetTask(task_type).SetUsage(task_mode).Build(&my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->AssociateNode(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
rc = my_tree->AssignRoot(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
MS_LOG(DEBUG) << "Launch tree and begin iteration.";
|
|
rc = my_tree->Prepare();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->Launch();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
// Start the loop of reading tensors from our pipeline
|
|
DatasetIterator di(my_tree);
|
|
TensorRow tensor_list;
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
int row_count = 0;
|
|
while (!tensor_list.empty()) {
|
|
MS_LOG(DEBUG) << "Row display for row #: " << row_count << ".";
|
|
|
|
// Display the tensor by calling the printer on it
|
|
for (int i = 0; i < tensor_list.size(); i++) {
|
|
std::ostringstream ss;
|
|
ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
|
|
MS_LOG(DEBUG) << "Tensor print: " << ss.str() << ".";
|
|
}
|
|
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
row_count++;
|
|
}
|
|
|
|
ASSERT_EQ(row_count, 9);
|
|
}
|
|
|
|
TEST_F(MindDataTestVOCOp, TestVOCSegmentation) {
|
|
// Start with an empty execution tree
|
|
auto my_tree = std::make_shared<ExecutionTree>();
|
|
std::string dataset_path;
|
|
dataset_path = datasets_root_path_ + "/testVOC2012";
|
|
|
|
std::string task_type("Segmentation");
|
|
std::string task_mode("train");
|
|
std::shared_ptr<VOCOp> my_voc_op;
|
|
VOCOp::Builder builder;
|
|
Status rc = builder.SetDir(dataset_path).SetTask(task_type).SetUsage(task_mode).Build(&my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->AssociateNode(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
rc = my_tree->AssignRoot(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
MS_LOG(DEBUG) << "Launch tree and begin iteration.";
|
|
rc = my_tree->Prepare();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->Launch();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
// Start the loop of reading tensors from our pipeline
|
|
DatasetIterator di(my_tree);
|
|
TensorRow tensor_list;
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
int row_count = 0;
|
|
while (!tensor_list.empty()) {
|
|
MS_LOG(DEBUG) << "Row display for row #: " << row_count << ".";
|
|
|
|
// Display the tensor by calling the printer on it
|
|
for (int i = 0; i < tensor_list.size(); i++) {
|
|
std::ostringstream ss;
|
|
ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
|
|
MS_LOG(DEBUG) << "Tensor print: " << ss.str() << ".";
|
|
}
|
|
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
row_count++;
|
|
}
|
|
|
|
ASSERT_EQ(row_count, 10);
|
|
}
|
|
|
|
TEST_F(MindDataTestVOCOp, TestVOCClassIndex) {
|
|
// Start with an empty execution tree
|
|
auto my_tree = std::make_shared<ExecutionTree>();
|
|
std::string dataset_path;
|
|
dataset_path = datasets_root_path_ + "/testVOC2012";
|
|
|
|
std::string task_type("Detection");
|
|
std::string task_mode("train");
|
|
std::map<std::string, int32_t> class_index;
|
|
class_index["car"] = 0;
|
|
class_index["cat"] = 1;
|
|
class_index["train"] = 5;
|
|
std::shared_ptr<VOCOp> my_voc_op;
|
|
VOCOp::Builder builder;
|
|
Status rc =
|
|
builder.SetDir(dataset_path).SetTask(task_type).SetUsage(task_mode).SetClassIndex(class_index).Build(&my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->AssociateNode(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
rc = my_tree->AssignRoot(my_voc_op);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
MS_LOG(DEBUG) << "Launch tree and begin iteration.";
|
|
rc = my_tree->Prepare();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
rc = my_tree->Launch();
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
// Start the loop of reading tensors from our pipeline
|
|
DatasetIterator di(my_tree);
|
|
TensorRow tensor_list;
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
|
|
int row_count = 0;
|
|
while (!tensor_list.empty()) {
|
|
MS_LOG(DEBUG) << "Row display for row #: " << row_count << ".";
|
|
|
|
// Display the tensor by calling the printer on it
|
|
for (int i = 0; i < tensor_list.size(); i++) {
|
|
std::ostringstream ss;
|
|
ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
|
|
MS_LOG(DEBUG) << "Tensor print: " << ss.str() << ".";
|
|
}
|
|
|
|
rc = di.FetchNextTensorRow(&tensor_list);
|
|
ASSERT_TRUE(rc.IsOk());
|
|
row_count++;
|
|
}
|
|
|
|
ASSERT_EQ(row_count, 6);
|
|
}
|