forked from mindspore-Ecosystem/mindspore
336 lines
10 KiB
C++
336 lines
10 KiB
C++
/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "minddata/dataset/core/client.h"
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#include "common/common.h"
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#include "common/utils.h"
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#include "gtest/gtest.h"
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#include "utils/log_adapter.h"
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#include <memory>
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#include <vector>
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#include <iostream>
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namespace common = mindspore::common;
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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 MindDataTestShuffleOp : public UT::DatasetOpTesting {
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};
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// Test info:
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// - Dataset from testDataset1 has 10 rows, 2 columns.
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// - RowsPerBuffer buffer setting of 2 divides evenly into total rows.
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// - Shuffle size is multiple of rows per buffer.
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//
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// Tree: shuffle over TFReader
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//
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// ShuffleOp
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// |
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// TFReaderOp
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//
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TEST_F(MindDataTestShuffleOp, TestShuffleBasic1) {
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Status rc;
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MS_LOG(INFO) << "UT test TestShuffleBasic1.";
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// Start with an empty execution tree
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auto my_tree = std::make_shared<ExecutionTree>();
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std::string dataset_path;
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dataset_path = datasets_root_path_ + "/testDataset1/testDataset1.data";
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std::shared_ptr<TFReaderOp> my_tfreader_op;
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rc = TFReaderOp::Builder()
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.SetDatasetFilesList({dataset_path})
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.SetRowsPerBuffer(2)
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.SetWorkerConnectorSize(16)
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.SetNumWorkers(1)
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.Build(&my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<ShuffleOp> my_shuffle_op;
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rc = ShuffleOp::Builder().SetRowsPerBuffer(2).SetShuffleSize(4).Build(&my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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// Set children/root layout.
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rc = my_shuffle_op->AddChild(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssignRoot(my_shuffle_op);
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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 = my_tree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->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(my_tree);
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TensorRow tensor_list;
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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int row_count = 0;
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while (!tensor_list.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensor_list.size(); i++) {
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std::ostringstream ss;
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ss << "(" << tensor_list[i] << "): " << *tensor_list[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(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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row_count++;
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}
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ASSERT_EQ(row_count, 10);
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}
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// Test info:
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// - Dataset from testDataset1 has 10 rows, 2 columns.
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// - RowsPerBuffer buffer setting of 3 does not divide evenly into total rows, thereby causing
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// partially filled buffers.
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// - Shuffle size is not a multiple of rows per buffer.
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// - User has provided a non-default seed value.
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//
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// Tree: shuffle over TFReader
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//
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// ShuffleOp
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// |
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// TFReaderOp
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//
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TEST_F(MindDataTestShuffleOp, TestShuffleBasic2) {
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Status rc;
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MS_LOG(INFO) << "UT test TestShuffleBasic2.";
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// Start with an empty execution tree
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auto my_tree = std::make_shared<ExecutionTree>();
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std::string dataset_path;
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dataset_path = datasets_root_path_ + "/testDataset1/testDataset1.data";
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std::shared_ptr<TFReaderOp> my_tfreader_op;
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rc = TFReaderOp::Builder()
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.SetDatasetFilesList({dataset_path})
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.SetRowsPerBuffer(3)
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.SetWorkerConnectorSize(16)
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.SetNumWorkers(2)
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.Build(&my_tfreader_op);
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ASSERT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<ShuffleOp> my_shuffle_op;
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rc = ShuffleOp::Builder().SetShuffleSize(4).SetShuffleSeed(100).SetRowsPerBuffer(3).Build(&my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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// Set children/root layout.
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rc = my_shuffle_op->AddChild(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssignRoot(my_shuffle_op);
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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 = my_tree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->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(my_tree);
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TensorRow tensor_list;
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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int row_count = 0;
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while (!tensor_list.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensor_list.size(); i++) {
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std::ostringstream ss;
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ss << "(" << tensor_list[i] << "): " << *tensor_list[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(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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row_count++;
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}
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ASSERT_EQ(row_count, 10);
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}
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// Test info:
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// - Dataset from testDataset1 has 10 rows, 2 columns.
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// - RowsPerBuffer buffer setting of 3 does not divide evenly into total rows, thereby causing
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// partially filled buffers
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// - Shuffle size captures the entire dataset size (actually sets a value that is larger than the
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// amount of rows in the dataset.
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//
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// Tree: shuffle over TFReader
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//
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// ShuffleOp
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// |
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// TFReaderOp
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//
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TEST_F(MindDataTestShuffleOp, TestShuffleBasic3) {
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Status rc;
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MS_LOG(INFO) << "UT test TestShuffleBasic3.";
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// Start with an empty execution tree
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auto my_tree = std::make_shared<ExecutionTree>();
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std::string dataset_path;
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dataset_path = datasets_root_path_ + "/testDataset1/testDataset1.data";
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std::shared_ptr<TFReaderOp> my_tfreader_op;
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rc = TFReaderOp::Builder()
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.SetDatasetFilesList({dataset_path})
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.SetRowsPerBuffer(3)
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.SetWorkerConnectorSize(16)
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.SetNumWorkers(2)
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.Build(&my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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my_tree->AssociateNode(my_tfreader_op);
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std::shared_ptr<ShuffleOp> my_shuffle_op;
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rc = ShuffleOp::Builder().SetShuffleSize(100).SetRowsPerBuffer(3).Build(&my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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// Set children/root layout.
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rc = my_shuffle_op->AddChild(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssignRoot(my_shuffle_op);
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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 = my_tree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->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(my_tree);
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TensorRow tensor_list;
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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int row_count = 0;
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while (!tensor_list.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensor_list.size(); i++) {
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std::ostringstream ss;
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ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
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MS_LOG(INFO) << "Tensor print: " << common::SafeCStr(ss.str()) << ".";
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}
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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row_count++;
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}
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ASSERT_EQ(row_count, 10);
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}
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// Test info:
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// - Dataset from testDataset1 has 10 rows, 2 columns.
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// - RowsPerBuffer buffer setting of 3 does not divide evenly into total rows thereby causing
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// partially filled buffers
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// - Shuffle size is not a multiple of rows per buffer.
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// - shuffle seed is given, and subsequent epochs will change the seed each time.
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// - Repeat count of 2
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//
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// Tree: Repeat over shuffle over TFReader
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//
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// Repeat
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// |
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// shuffle
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// |
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// TFReaderOp
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//
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TEST_F(MindDataTestShuffleOp, TestRepeatShuffle) {
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Status rc;
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MS_LOG(INFO) << "UT test TestRepeatShuffle.";
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// Start with an empty execution tree
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auto my_tree = std::make_shared<ExecutionTree>();
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std::string dataset_path;
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dataset_path = datasets_root_path_ + "/testDataset1/testDataset1.data";
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std::shared_ptr<TFReaderOp> my_tfreader_op;
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rc = TFReaderOp::Builder()
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.SetDatasetFilesList({dataset_path})
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.SetRowsPerBuffer(3)
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.SetWorkerConnectorSize(16)
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.SetNumWorkers(2)
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.Build(&my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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std::shared_ptr<ShuffleOp> my_shuffle_op;
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rc = ShuffleOp::Builder()
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.SetShuffleSize(4)
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.SetShuffleSeed(100)
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.SetRowsPerBuffer(3)
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.SetReshuffleEachEpoch(true)
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.Build(&my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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uint32_t numRepeats = 2;
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std::shared_ptr<RepeatOp> my_repeat_op;
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rc = RepeatOp::Builder(numRepeats).Build(&my_repeat_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssociateNode(my_repeat_op);
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EXPECT_TRUE(rc.IsOk());
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// Set children/root layout.
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rc = my_repeat_op->AddChild(my_shuffle_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_shuffle_op->AddChild(my_tfreader_op);
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->AssignRoot(my_repeat_op);
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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 = my_tree->Prepare();
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EXPECT_TRUE(rc.IsOk());
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rc = my_tree->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(my_tree);
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TensorRow tensor_list;
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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int row_count = 0;
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while (!tensor_list.empty()) {
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MS_LOG(INFO) << "Row display for row #: " << row_count << ".";
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// Display the tensor by calling the printer on it
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for (int i = 0; i < tensor_list.size(); i++) {
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std::ostringstream ss;
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ss << *tensor_list[i] << std::endl;
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MS_LOG(INFO) << "Tensor print: " << common::SafeCStr(ss.str()) << ".";
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}
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rc = di.FetchNextTensorRow(&tensor_list);
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EXPECT_TRUE(rc.IsOk());
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row_count++;
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}
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ASSERT_EQ(row_count, 20);
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}
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