forked from mindspore-Ecosystem/mindspore
458 lines
12 KiB
C++
458 lines
12 KiB
C++
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/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "dataset/core/client.h"
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#include "common/common.h"
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#include "gtest/gtest.h"
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#include <memory>
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#include <vector>
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#include <iostream>
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#include "dataset/core/tensor_shape.h"
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#include "dataset/engine/datasetops/source/random_data_op.h"
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#include "dataset/engine/data_schema.h"
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using namespace mindspore::dataset;
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using mindspore::MsLogLevel::INFO;
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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::LogStream;
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class MindDataTestRandomDataOp : public UT::DatasetOpTesting {
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};
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// Test info:
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// - Simple test with a user-provided schema generated purely from DataSchema C API
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// - has an interation loop
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//
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// Tree: single node tree with RandomDataOp
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//
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpBasic1) {
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Status rc;
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int32_t rank = 0; // not used
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MS_LOG(INFO) << "UT test RandomDataOpBasic1";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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// Create a schema using the C api's
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std::unique_ptr<DataSchema> testSchema = std::make_unique<DataSchema>();
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// RandomDataOp can randomly fill in unknown dimension lengths of a shape.
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// Most other ops cannot do that as they are limited by the physical data itself. We're
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// more flexible with random data since it is just making stuff up on the fly.
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TensorShape c1Shape({TensorShape::kDimUnknown, TensorShape::kDimUnknown, 3});
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ColDescriptor c1("image",
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DataType(DataType::DE_INT8),
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TensorImpl::kFlexible,
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rank, // not used
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&c1Shape);
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// Column 2 will just be a scalar label number
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TensorShape c2Shape({}); // empty shape is a 1-value scalar Tensor
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ColDescriptor c2("label",
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DataType(DataType::DE_UINT32),
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TensorImpl::kFlexible,
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rank,
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&c2Shape);
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testSchema->AddColumn(c1);
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testSchema->AddColumn(c2);
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(1)
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.SetDataSchema(std::move(testSchema))
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.SetTotalRows(25)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssignRoot(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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std::ostringstream ss;
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ss << *myRandomDataOp;
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MS_LOG(INFO) << "RandomDataOp print: %s" << ss.str();
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MS_LOG(INFO) << "Launching tree and begin iteration";
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rc = myTree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->Launch();
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EXPECT_TRUE(rc.IsOk());
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// Start the loop of reading tensors from our pipeline
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DatasetIterator dI(myTree);
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TensorRow tensorList;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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int rowCount = 0;
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while (!tensorList.empty()) {
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// Don't display these rows...too big to show
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MS_LOG(INFO) << "Row fetched #: " << rowCount;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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rowCount++;
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}
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ASSERT_EQ(rowCount, 25);
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}
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// Test info:
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// - Simple test with a randomly generated schema
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// - no iteration loop on this one, just create the op
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//
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// Tree: single node tree with RandomDataOp
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//
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpBasic2) {
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Status rc;
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MS_LOG(INFO) << "UT test RandomDataOpBasic2";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(1)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssignRoot(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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std::ostringstream ss;
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ss << *myRandomDataOp;
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MS_LOG(INFO) << "RandomDataOp print: " << ss.str();
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}
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// Test info:
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// - json file test with iteration
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//
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// Tree: single node tree with RandomDataOp
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//
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpBasic3) {
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Status rc;
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MS_LOG(INFO) << "UT test RandomDataOpBasic3";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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std::unique_ptr<DataSchema> testSchema = std::make_unique<DataSchema>();
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rc = testSchema->LoadSchemaFile(datasets_root_path_ + "/testRandomData/datasetSchema.json", {});
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(1)
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.SetDataSchema(std::move(testSchema))
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.SetTotalRows(10)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssignRoot(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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std::ostringstream ss;
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ss << *myRandomDataOp;
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MS_LOG(INFO) << "RandomDataOp print: " << ss.str();
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MS_LOG(INFO) << "Launching tree and begin iteration";
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rc = myTree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->Launch();
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EXPECT_TRUE(rc.IsOk());
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// Start the loop of reading tensors from our pipeline
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DatasetIterator dI(myTree);
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TensorRow tensorList;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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int rowCount = 0;
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while (!tensorList.empty()) {
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// Don't display these rows...too big to show
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MS_LOG(INFO) << "Row fetched #: " << rowCount;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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rowCount++;
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}
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ASSERT_EQ(rowCount, 10);
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}
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// Test info:
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// - json schema input it's a fairly simple one
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// - has an interation loop
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//
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// Tree: RepeatOp over RandomDataOp
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//
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// RepeatOp
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// |
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpBasic4) {
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Status rc;
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MS_LOG(INFO) << "UT test RandomDataOpBasic4";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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std::unique_ptr<DataSchema> testSchema = std::make_unique<DataSchema>();
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rc = testSchema->LoadSchemaFile(datasets_root_path_ + "/testRandomData/datasetSchema2.json", {});
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(1)
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.SetDataSchema(std::move(testSchema))
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.SetTotalRows(10)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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uint32_t numRepeats = 2;
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std::shared_ptr<RepeatOp> myRepeatOp;
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rc = RepeatOp::Builder(numRepeats)
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.Build(&myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myRepeatOp->AddChild(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssignRoot(myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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MS_LOG(INFO) << "Launching tree and begin iteration";
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rc = myTree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->Launch();
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EXPECT_TRUE(rc.IsOk());
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// Start the loop of reading tensors from our pipeline
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DatasetIterator dI(myTree);
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TensorRow tensorList;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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int rowCount = 0;
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while (!tensorList.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << rowCount;
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensorList.size(); i++) {
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std::ostringstream ss;
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ss << *tensorList[i] << std::endl;
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MS_LOG(INFO) << "Tensor print: %s" << ss.str();
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}
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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rowCount++;
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}
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ASSERT_EQ(rowCount, 20);
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}
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// Test info:
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// - json schema input it's a fairly simple one
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// - has an interation loop
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// - same as MindDataTestRandomDataOpBasic4 except that this one will have parallel workers
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//
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// Tree: RepeatOp over RandomDataOp
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//
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// RepeatOp
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// |
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpBasic5) {
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Status rc;
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MS_LOG(INFO) << "UT test RandomDataOpBasic5";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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std::unique_ptr<DataSchema> testSchema = std::make_unique<DataSchema>();
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rc = testSchema->LoadSchemaFile(datasets_root_path_ + "/testRandomData/datasetSchema2.json", {});
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(4)
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.SetDataSchema(std::move(testSchema))
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.SetTotalRows(10)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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uint32_t numRepeats = 3;
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std::shared_ptr<RepeatOp> myRepeatOp;
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rc = RepeatOp::Builder(numRepeats)
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.Build(&myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssociateNode(myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myRepeatOp->AddChild(myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->AssignRoot(myRepeatOp);
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EXPECT_TRUE(rc.IsOk());
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MS_LOG(INFO) << "Launching tree and begin iteration";
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rc = myTree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = myTree->Launch();
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EXPECT_TRUE(rc.IsOk());
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// Start the loop of reading tensors from our pipeline
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DatasetIterator dI(myTree);
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TensorRow tensorList;
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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int rowCount = 0;
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while (!tensorList.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << rowCount;
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensorList.size(); i++) {
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std::ostringstream ss;
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ss << *tensorList[i] << std::endl;
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MS_LOG(INFO) << "Tensor print: ", ss.str();
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}
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rc = dI.FetchNextTensorRow(&tensorList);
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EXPECT_TRUE(rc.IsOk());
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rowCount++;
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}
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ASSERT_EQ(rowCount, 30);
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}
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// Test info:
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// - repeat shuffle random
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//
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// Tree: RepeatOp over RandomDataOp
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//
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// RepeatOp
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// |
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// ShuffleOp
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// |
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// RandomDataOp
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//
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TEST_F(MindDataTestRandomDataOp, RandomDataOpTree1) {
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Status rc;
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MS_LOG(INFO) << "UT test RandomDataOpTree1";
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// Start with an empty execution tree
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auto myTree = std::make_shared<ExecutionTree>();
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std::unique_ptr<DataSchema> testSchema = std::make_unique<DataSchema>();
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rc = testSchema->LoadSchemaFile(datasets_root_path_ + "/testRandomData/datasetSchema2.json", {});
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EXPECT_TRUE(rc.IsOk());
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||
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||
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std::shared_ptr<RandomDataOp> myRandomDataOp;
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RandomDataOp::Builder builder;
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rc = builder.SetRowsPerBuffer(2)
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.SetNumWorkers(4)
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.SetDataSchema(std::move(testSchema))
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.SetTotalRows(10)
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.Build(&myRandomDataOp);
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EXPECT_TRUE(rc.IsOk());
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||
|
|
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||
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rc = myTree->AssociateNode(myRandomDataOp);
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||
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EXPECT_TRUE(rc.IsOk());
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||
|
|
|
||
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|
std::shared_ptr<ShuffleOp> myShuffleOp;
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||
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|
rc = ShuffleOp::Builder()
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||
|
|
.SetRowsPerBuffer(2)
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||
|
|
.SetShuffleSize(4)
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||
|
|
.Build(&myShuffleOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
rc = myTree->AssociateNode(myShuffleOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
uint32_t numRepeats = 3;
|
||
|
|
std::shared_ptr<RepeatOp> myRepeatOp;
|
||
|
|
rc = RepeatOp::Builder(numRepeats)
|
||
|
|
.Build(&myRepeatOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
rc = myTree->AssociateNode(myRepeatOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
rc = myRepeatOp->AddChild(myShuffleOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
rc = myShuffleOp->AddChild(myRandomDataOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
rc = myTree->AssignRoot(myRepeatOp);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
MS_LOG(INFO) << "Launching tree and begin iteration";
|
||
|
|
rc = myTree->Prepare();
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
rc = myTree->Launch();
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
|
||
|
|
// Start the loop of reading tensors from our pipeline
|
||
|
|
DatasetIterator dI(myTree);
|
||
|
|
TensorRow tensorList;
|
||
|
|
rc = dI.FetchNextTensorRow(&tensorList);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
int rowCount = 0;
|
||
|
|
while (!tensorList.empty()) {
|
||
|
|
MS_LOG(INFO) << "Row display for row #: " << rowCount;
|
||
|
|
|
||
|
|
// Display the tensor by calling the printer on it
|
||
|
|
for (int i = 0; i < tensorList.size(); i++) {
|
||
|
|
std::ostringstream ss;
|
||
|
|
ss << *tensorList[i] << std::endl;
|
||
|
|
MS_LOG(INFO) << "Tensor print: " << ss.str();
|
||
|
|
}
|
||
|
|
|
||
|
|
rc = dI.FetchNextTensorRow(&tensorList);
|
||
|
|
EXPECT_TRUE(rc.IsOk());
|
||
|
|
rowCount++;
|
||
|
|
}
|
||
|
|
ASSERT_EQ(rowCount, 30);
|
||
|
|
}
|