forked from mindspore-Ecosystem/mindspore
!1157 dataset: add concat operation for dataset
Merge pull request !1157 from ms_yan/concat_dataset
This commit is contained in:
commit
c680cfbf27
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@ -53,6 +53,7 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &D
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{kRepeat, &DEPipeline::ParseRepeatOp},
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{kSkip, &DEPipeline::ParseSkipOp},
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{kZip, &DEPipeline::ParseZipOp},
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{kConcat, &DEPipeline::ParseConcatOp},
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{kRename, &DEPipeline::ParseRenameOp},
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{kDeviceQueue, &DEPipeline::ParseDeviceQueueOp},
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{kGenerator, &DEPipeline::ParseGeneratorOp},
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@ -757,6 +758,14 @@ Status DEPipeline::ParseZipOp(const py::dict &args, std::shared_ptr<DatasetOp> *
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return Status::OK();
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}
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Status DEPipeline::ParseConcatOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
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std::shared_ptr<ConcatOp::Builder> builder = std::make_shared<ConcatOp::Builder>();
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std::shared_ptr<ConcatOp> op;
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RETURN_IF_NOT_OK(builder->Build(&op));
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*ptr = op;
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return Status::OK();
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}
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Status DEPipeline::ParseTFReaderOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
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// Required arguments
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std::shared_ptr<TFReaderOp::Builder> builder = std::make_shared<TFReaderOp::Builder>();
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@ -46,6 +46,7 @@ enum OpName {
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kSkip,
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kTake,
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kZip,
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kConcat,
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kMap,
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kFilter,
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kDeviceQueue,
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@ -127,6 +128,8 @@ class DEPipeline {
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Status ParseZipOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
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Status ParseConcatOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
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Status ParseDeviceQueueOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
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Status ParseTFReaderOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
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@ -476,6 +476,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
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.value("SKIP", OpName::kSkip)
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.value("TAKE", OpName::kTake)
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.value("ZIP", OpName::kZip)
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.value("CONCAT", OpName::kConcat)
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.value("MAP", OpName::kMap)
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.value("FILTER", OpName::kFilter)
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.value("DEVICEQUEUE", OpName::kDeviceQueue)
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@ -42,6 +42,7 @@
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#include "dataset/engine/datasetops/source/tf_reader_op.h"
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#include "dataset/engine/datasetops/take_op.h"
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#include "dataset/engine/datasetops/zip_op.h"
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#include "dataset/engine/datasetops/concat_op.h"
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#include "dataset/engine/execution_tree.h"
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#include "dataset/util/status.h"
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@ -17,6 +17,7 @@ add_library(engine-datasetops OBJECT
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take_op.cc
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shuffle_op.cc
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zip_op.cc
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concat_op.cc
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filter_op.cc
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)
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@ -0,0 +1,145 @@
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/**
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* Copyright 2020 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 <iomanip>
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#include <utility>
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#include "common/utils.h"
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#include "dataset/core/config_manager.h"
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#include "dataset/engine/data_buffer.h"
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#include "dataset/engine/datasetops/concat_op.h"
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#include "dataset/engine/db_connector.h"
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#include "dataset/engine/execution_tree.h"
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namespace mindspore {
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namespace dataset {
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// Builder constructor. Creates the builder object.
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ConcatOp::Builder::Builder() {
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std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
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builder_op_connector_size_ = cfg->op_connector_size();
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}
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// The builder "build" method creates the final object.
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Status ConcatOp::Builder::Build(std::shared_ptr<ConcatOp> *ptr) {
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*ptr = std::make_shared<ConcatOp>(builder_op_connector_size_);
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return Status::OK();
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}
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// Constructor of the ConcatOp.
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ConcatOp::ConcatOp(int32_t op_connector_size) : PipelineOp(op_connector_size), children_num_(0) {}
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// A function that prints info about the Operator
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void ConcatOp::Print(std::ostream &out, bool show_all) const {
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// Always show the id and name as first line regardless if this is summary or detailed print
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out << "(" << std::setw(2) << operator_id_ << ") <ConcatOp>:";
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if (!show_all) {
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// Call the super class for displaying any common 1-liner info
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PipelineOp::Print(out, show_all);
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// Then show any custom derived-internal 1-liner info for this op
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out << "\n";
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} else {
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// Call the super class for displaying any common detailed info
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PipelineOp::Print(out, show_all);
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// Then show any custom derived-internal stuff
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out << "\nDatasets: " << children_num_ << "\n\n";
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}
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}
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// Main entry point for Concat
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Status ConcatOp::operator()() {
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// The children_num_ parameter needs to be put here
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children_num_ = static_cast<int32_t>(child_.size());
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TaskManager::FindMe()->Post();
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std::unique_ptr<DataBuffer> buf;
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RETURN_IF_NOT_OK(child_[0]->GetNextBuffer(&buf));
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// Obtain columns_name_id_map from child_[0]
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column_name_id_map_ = child_[0]->column_name_id_map();
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if (column_name_id_map_.empty()) {
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RETURN_STATUS_UNEXPECTED("Child column name map cannot be empty!");
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}
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int eof_count = 0;
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while (eof_count != children_num_) {
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for (int i = 0; i < children_num_; i++) {
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// 1. Throw the eof buffer when meet it
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if (buf->eof() || buf->eoe()) {
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RETURN_IF_NOT_OK(child_[i]->GetNextBuffer(&buf));
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}
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// 2. Do varification as for column name, column data type and rank of column data
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RETURN_IF_NOT_OK(Verify(i, buf));
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// 3. Put the data into output_connector
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while (!buf->eoe() && !buf->eof()) {
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RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(buf)));
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RETURN_IF_NOT_OK(child_[i]->GetNextBuffer(&buf));
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}
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// 4. Throw the eoe buffer when meet it
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if (buf->eoe() && (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat))) {
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RETURN_IF_NOT_OK(child_[i]->GetNextBuffer(&buf));
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}
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// 5. Add eoe buffer after get buffer from all child
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if (i == (children_num_ - 1)) {
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auto eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
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RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
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}
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if (buf->eof()) {
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eof_count++;
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}
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}
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}
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// 6. Add eof buffer in the end manually
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MS_LOG(DEBUG) << "Add the eof buffer manualy in the end.";
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auto eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
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RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer)));
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return Status::OK();
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}
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Status ConcatOp::Verify(int32_t id, const std::unique_ptr<DataBuffer> &buf) {
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TensorRow new_row;
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buf->GetRow(0, &new_row);
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if (id == 0) {
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// Obtain the column name, data type and data rank in child[0]
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column_name_id_ = child_[id]->column_name_id_map();
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for (auto item : new_row) {
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data_type_.push_back(item->type());
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data_rank_.push_back(item->Rank());
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}
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} else {
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// Compare the column name, data type and data rank with these in child[0]
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if (child_[id]->column_name_id_map() != column_name_id_) {
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RETURN_STATUS_UNEXPECTED("The column name or column order is not the same with previous dataset.");
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}
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int32_t index = 0;
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for (auto item : new_row) {
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if ((item->type() != data_type_[index]) || item->Rank() != data_rank_[index++]) {
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RETURN_STATUS_UNEXPECTED("The data type or data rank is not the same with previous dataset.");
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}
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}
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}
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return Status::OK();
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}
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Status ConcatOp::PrepareNodePostAction() {
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RETURN_IF_NOT_OK(PipelineOp::PrepareNodePostAction());
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tree_->AddToRepeatStack(shared_from_this());
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return Status::OK();
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}
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} // namespace dataset
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} // namespace mindspore
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@ -0,0 +1,95 @@
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/**
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* Copyright 2020 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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#ifndef DATASET_ENGINE_DATASETOPS_CONCAT_OP_H_
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#define DATASET_ENGINE_DATASETOPS_CONCAT_OP_H_
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "dataset/engine/datasetops/pipeline_op.h"
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namespace mindspore {
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namespace dataset {
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class ConcatOp : public PipelineOp {
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public:
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// The nested builder class inside of the ConcatOp is used to help manage all of the arguments
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// for constructing it. This Concat op is very simple though, so this builder is really just
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// provided for a consistent look and feel for creators of Dataset operators overall.
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class Builder {
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public:
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// Builder constructor. Creates the builder object.
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// @note No default args
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// @return This is a constructor.
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Builder();
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// Default destructor
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~Builder() = default;
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// The builder "build" method creates the final object.
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// @return shared_ptr to the new StorageOp object
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Status Build(std::shared_ptr<ConcatOp> *);
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private:
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int32_t builder_op_connector_size_;
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};
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// Constructor of the ConcatOp.
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// @note The builder class should be used to call it
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// @param op_connector_size - connector size
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explicit ConcatOp(int32_t op_connector_size);
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// Destructor
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~ConcatOp() = default;
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// A print method typically used for debugging
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// @param out - The output stream to write output to
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// @param show_all - A bool to control if you want to show all info or just a summary
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void Print(std::ostream &out, bool show_all) const override;
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// << Stream output operator overload
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// @notes This allows you to write the debug print info using stream operators
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// @param out - reference to the output stream being overloaded
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// @param ro - reference to the ConcatOp to display
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// @return - the output stream must be returned
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friend std::ostream &operator<<(std::ostream &out, const ConcatOp &ro) {
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ro.Print(out, false);
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return out;
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}
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// All dataset ops operate by launching a thread (see ExecutionTree). This class functor will
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// provide the master loop that drives the logic for performing the work
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// @return Status - The error code return
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Status operator()() override;
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// During tree prepare phase, operators may have specific post-operations to perform depending on
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// their role.
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// @notes Derived versions of this function should always call it's superclass version first
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// before providing their own implementations.
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Status PrepareNodePostAction() override;
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private:
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Status Verify(int32_t id, const std::unique_ptr<DataBuffer> &buf);
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int32_t children_num_; // The num of child of parent node.
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std::unordered_map<std::string, int32_t> column_name_id_; // Mapping between col index and col name
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std::vector<DataType> data_type_;
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std::vector<dsize_t> data_rank_;
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // DATASET_ENGINE_DATASETOPS_CONCAT_OP_H_
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@ -44,7 +44,7 @@ from .validators import check, check_batch, check_shuffle, check_map, check_filt
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check_rename, \
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check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
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check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
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check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset
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check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset, check_concat
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from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
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try:
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@ -147,6 +147,9 @@ class Dataset:
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self._repeat_count = None
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self._sync = False
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def __add__(self, datasets):
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return self.concat(datasets)
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def get_args(self):
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"""
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Returns attributes (member variables) related to the current class.
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@ -560,6 +563,37 @@ class Dataset:
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raise TypeError("The zip function %s type error!" % (datasets))
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return ZipDataset(datasets)
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@check_concat
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def concat(self, datasets):
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"""
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Concat the datasets in the input list of datasets, supported using "+" to reload concat operation.
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Note:
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The column name,column data type and rank of column data should be the same in input datasets.
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Args:
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datasets (list or class Dataset): A list of datasets or a single class Dataset
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to be concated together with this dataset.
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Returns:
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ConcatDataset, dataset concated.
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Examples:
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>>> import mindspore.dataset as ds
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>>> # ds1 and ds2 are instances of Dataset object
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>>> # creates a dataset by concating ds1 and ds2 with "+" operation
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>>> data1 = ds1 + ds2
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>>> # creates a dataset by concating ds1 and ds2 with concat operation
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>>> data1 = ds1.concat(ds2)
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"""
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if isinstance(datasets, Dataset):
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datasets = [self] + [datasets]
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elif isinstance(datasets, list):
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datasets = [self] + datasets
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else:
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raise TypeError("The concat_dataset function %s type error!" % (datasets))
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return ConcatDataset(datasets)
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@check_rename
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def rename(self, input_columns, output_columns):
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"""
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@ -1658,6 +1692,39 @@ class ZipDataset(DatasetOp):
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return args
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class ConcatDataset(DatasetOp):
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"""
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The result of applying concat dataset operator to the input Dataset.
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Args:
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datasets (list): A list of datasets to be concated together.
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Raises:
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TypeError: If dataset is not an instance of Dataset.
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"""
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def __init__(self, datasets):
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super().__init__()
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for dataset in datasets:
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if not isinstance(dataset, Dataset):
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raise TypeError("The parameter %s of concat has type error!" % (dataset))
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self.datasets = datasets
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for data in datasets:
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self.input.append(data)
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data.output.append(self)
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def get_dataset_size(self):
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"""
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Get the number of batches in an epoch.
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Return:
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Number, number of batches.
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"""
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children_sizes = [c.get_dataset_size() for c in self.input]
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dataset_size = np.sum(children_sizes)
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return dataset_size
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class RenameDataset(DatasetOp):
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"""
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The result of applying Rename operator to the input Dataset.
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|
|
|
@ -156,6 +156,8 @@ class Iterator:
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op_type = OpName.BARRIER
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elif isinstance(dataset, de.ZipDataset):
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op_type = OpName.ZIP
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elif isinstance(dataset, de.ConcatDataset):
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op_type = OpName.CONCAT
|
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elif isinstance(dataset, de.MapDataset):
|
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op_type = OpName.MAP
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elif isinstance(dataset, de.FilterDataset):
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|
|
|
@ -335,6 +335,10 @@ def create_node(node):
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# Create ZipDataset instance, giving dummy input dataset that will be overrided in the caller.
|
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pyobj = de.ZipDataset((de.Dataset(), de.Dataset()))
|
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|
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elif dataset_op == 'ConcatDataset':
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# Create ConcatDataset instance, giving dummy input dataset that will be overrided in the caller.
|
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pyobj = de.ConcatDataset((de.Dataset(), de.Dataset()))
|
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|
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elif dataset_op == 'RenameDataset':
|
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pyobj = de.Dataset().rename(node['input_columns'], node['output_columns'])
|
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|
||||
|
|
|
@ -902,6 +902,26 @@ def check_zip_dataset(method):
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return new_method
|
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|
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|
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def check_concat(method):
|
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"""check the input arguments of concat_dataset method in `Dataset`."""
|
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|
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@wraps(method)
|
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def new_method(*args, **kwargs):
|
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param_dict = make_param_dict(method, args, kwargs)
|
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|
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# check datasets; required argument
|
||||
ds = param_dict.get("datasets")
|
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if ds is None:
|
||||
raise ValueError("datasets is not provided.")
|
||||
|
||||
if not isinstance(ds, (list, datasets.Dataset)):
|
||||
raise ValueError("datasets is not list or of type Dataset.")
|
||||
|
||||
return method(*args, **kwargs)
|
||||
|
||||
return new_method
|
||||
|
||||
|
||||
def check_rename(method):
|
||||
"""check the input arguments of rename."""
|
||||
|
||||
|
|
|
@ -66,6 +66,7 @@ SET(DE_UT_SRCS
|
|||
take_op_test.cc
|
||||
text_file_op_test.cc
|
||||
filter_op_test.cc
|
||||
concat_op_test.cc
|
||||
)
|
||||
|
||||
add_executable(de_ut_tests ${DE_UT_SRCS})
|
||||
|
|
|
@ -0,0 +1,125 @@
|
|||
/**
|
||||
* 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 <iostream>
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
||||
#include "common/common.h"
|
||||
#include "common/utils.h"
|
||||
#include "dataset/core/client.h"
|
||||
#include "gtest/gtest.h"
|
||||
#include "utils/log_adapter.h"
|
||||
|
||||
namespace common = mindspore::common;
|
||||
|
||||
using namespace mindspore::dataset;
|
||||
using mindspore::MsLogLevel::INFO;
|
||||
using mindspore::ExceptionType::NoExceptionType;
|
||||
using mindspore::LogStream;
|
||||
|
||||
class MindDataTestConcatOp : public UT::DatasetOpTesting {};
|
||||
|
||||
|
||||
TEST_F(MindDataTestConcatOp, TestConcatProject) {
|
||||
/* Tree:
|
||||
*
|
||||
* OpId(2) ConcatOp
|
||||
* / \
|
||||
* OpId(0) TFReaderOp OpId(1) TFReaderOp
|
||||
*
|
||||
* Start with an empty execution tree
|
||||
*/
|
||||
MS_LOG(INFO) << "UT test TestConcatProject.";
|
||||
auto my_tree = std::make_shared<ExecutionTree>();
|
||||
|
||||
std::string dataset_path;
|
||||
dataset_path = datasets_root_path_ + "/testTFTestAllTypes/test.data";
|
||||
|
||||
// TFReaderOp1
|
||||
std::shared_ptr<TFReaderOp> my_tfreader_op1;
|
||||
TFReaderOp::Builder builder1;
|
||||
builder1.SetDatasetFilesList({dataset_path})
|
||||
.SetRowsPerBuffer(16)
|
||||
.SetWorkerConnectorSize(16)
|
||||
.SetNumWorkers(16);
|
||||
std::unique_ptr<DataSchema> schema1 = std::make_unique<DataSchema>();
|
||||
schema1->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
|
||||
builder1.SetDataSchema(std::move(schema1));
|
||||
Status rc = builder1.Build(&my_tfreader_op1);
|
||||
ASSERT_TRUE(rc.IsOk());
|
||||
rc = my_tree->AssociateNode(my_tfreader_op1);
|
||||
ASSERT_TRUE(rc.IsOk());
|
||||
|
||||
// TFReaderOp2
|
||||
std::shared_ptr<TFReaderOp> my_tfreader_op2;
|
||||
TFReaderOp::Builder builder2;
|
||||
builder2.SetDatasetFilesList({dataset_path})
|
||||
.SetRowsPerBuffer(16)
|
||||
.SetWorkerConnectorSize(16)
|
||||
.SetNumWorkers(16);
|
||||
std::unique_ptr<DataSchema> schema2 = std::make_unique<DataSchema>();
|
||||
schema2->LoadSchemaFile(datasets_root_path_ + "/testTFTestAllTypes/datasetSchema.json", {});
|
||||
builder2.SetDataSchema(std::move(schema2));
|
||||
rc = builder2.Build(&my_tfreader_op2);
|
||||
ASSERT_TRUE(rc.IsOk());
|
||||
rc = my_tree->AssociateNode(my_tfreader_op2);
|
||||
ASSERT_TRUE(rc.IsOk());
|
||||
|
||||
// Creating ConcatOp
|
||||
std::shared_ptr<ConcatOp> concat_op;
|
||||
rc = ConcatOp::Builder().Build(&concat_op);
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
|
||||
rc = my_tree->AssociateNode(concat_op);
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
rc = concat_op->AddChild(std::move(my_tfreader_op1));
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
rc = concat_op->AddChild(std::move(my_tfreader_op2));
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
rc = my_tree->AssignRoot(concat_op);
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
rc = my_tree->Prepare();
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
|
||||
// Launch the tree execution to kick off threads and start running the pipeline
|
||||
MS_LOG(INFO) << "Launching my tree.";
|
||||
rc = my_tree->Launch();
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
|
||||
// Simulate a parse of data from our pipeline.
|
||||
std::shared_ptr<DatasetOp> rootNode = my_tree->root();
|
||||
|
||||
DatasetIterator di(my_tree);
|
||||
TensorRow tensor_list;
|
||||
rc = di.FetchNextTensorRow(&tensor_list);
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
|
||||
int row_count = 0;
|
||||
while (!tensor_list.empty()) {
|
||||
MS_LOG(INFO) << "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(INFO) << "Tensor print: " << common::SafeCStr(ss.str()) << ".";
|
||||
}
|
||||
rc = di.FetchNextTensorRow(&tensor_list);
|
||||
EXPECT_TRUE(rc.IsOk());
|
||||
row_count++;
|
||||
}
|
||||
ASSERT_EQ(row_count, 24); // Should be 24 rows fetched
|
||||
}
|
|
@ -0,0 +1,377 @@
|
|||
# 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.
|
||||
# ==============================================================================
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.vision.py_transforms as F
|
||||
import mindspore.dataset.transforms.c_transforms as C
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore import log as logger
|
||||
import numpy as np
|
||||
|
||||
|
||||
# In generator dataset: Number of rows is 3, its value is 0, 1, 2
|
||||
def generator():
|
||||
for i in range(3):
|
||||
yield np.array([i]),
|
||||
|
||||
|
||||
# In generator_10 dataset: Number of rows is 7, its value is 3, 4, 5 ... 10
|
||||
def generator_10():
|
||||
for i in range(3, 10):
|
||||
yield np.array([i]),
|
||||
|
||||
# In generator_20 dataset: Number of rows is 10, its value is 10, 11, 12 ... 20
|
||||
def generator_20():
|
||||
for i in range(10, 20):
|
||||
yield np.array([i]),
|
||||
|
||||
|
||||
def test_concat_01():
|
||||
"""
|
||||
Test concat: test concat 2 datasets that have the same column name and data type
|
||||
"""
|
||||
logger.info("test_concat_01")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data3 = data1 + data2
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert i == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 10
|
||||
|
||||
|
||||
def test_concat_02():
|
||||
"""
|
||||
Test concat: test concat 2 datasets using concat operation not "+" operation
|
||||
"""
|
||||
logger.info("test_concat_02")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data3 = data1.concat(data2)
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert i == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 10
|
||||
|
||||
|
||||
def test_concat_03():
|
||||
"""
|
||||
Test concat: test concat dataset that has different column
|
||||
"""
|
||||
logger.info("test_concat_03")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col2"])
|
||||
|
||||
data3 = data1 + data2
|
||||
|
||||
try:
|
||||
for i, d in enumerate(data3):
|
||||
pass
|
||||
assert False
|
||||
except RuntimeError:
|
||||
pass
|
||||
|
||||
|
||||
def test_concat_04():
|
||||
"""
|
||||
Test concat: test concat dataset that has different rank
|
||||
"""
|
||||
logger.info("test_concat_04")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col2"])
|
||||
data2 = data2.batch(3)
|
||||
|
||||
data3 = data1 + data2
|
||||
|
||||
try:
|
||||
for i, d in enumerate(data3):
|
||||
pass
|
||||
assert False
|
||||
except RuntimeError:
|
||||
pass
|
||||
|
||||
|
||||
def test_concat_05():
|
||||
"""
|
||||
Test concat: test concat dataset that has different data type
|
||||
"""
|
||||
logger.info("test_concat_05")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
type_cast_op = C.TypeCast(mstype.float32)
|
||||
data1 = data1.map(input_columns=["col1"], operations=type_cast_op)
|
||||
|
||||
data3 = data1 + data2
|
||||
|
||||
try:
|
||||
for i, d in enumerate(data3):
|
||||
pass
|
||||
assert False
|
||||
except RuntimeError:
|
||||
pass
|
||||
|
||||
|
||||
def test_concat_06():
|
||||
"""
|
||||
Test concat: test concat muti datasets in one time
|
||||
"""
|
||||
logger.info("test_concat_06")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
data3 = ds.GeneratorDataset(generator_20, ["col1"])
|
||||
|
||||
dataset = data1 + data2 + data3
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(dataset):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert i == d[0][0]
|
||||
|
||||
assert sum([1 for _ in dataset]) == 20
|
||||
|
||||
|
||||
def test_concat_07():
|
||||
"""
|
||||
Test concat: test concat one dataset with multi datasets (datasets list)
|
||||
"""
|
||||
logger.info("test_concat_07")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
data3 = ds.GeneratorDataset(generator_20, ["col1"])
|
||||
|
||||
dataset = [data2] + [data3]
|
||||
data4 = data1 + dataset
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data4):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert i == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data4]) == 20
|
||||
|
||||
|
||||
def test_concat_08():
|
||||
"""
|
||||
Test concat: test concat 2 datasets, and then repeat
|
||||
"""
|
||||
logger.info("test_concat_08")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data3 = data1 + data2
|
||||
data3 = data3.repeat(2)
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert i % 10 == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 20
|
||||
|
||||
|
||||
def test_concat_09():
|
||||
"""
|
||||
Test concat: test concat 2 datasets, both of them have been repeat before
|
||||
"""
|
||||
logger.info("test_concat_09")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data1 = data1.repeat(2)
|
||||
data2 = data2.repeat(2)
|
||||
data3 = data1 + data2
|
||||
|
||||
res = [0, 1, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 3, 4, 5, 6, 7, 8, 9]
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert res[i] == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 20
|
||||
|
||||
|
||||
def test_concat_10():
|
||||
"""
|
||||
Test concat: test concat 2 datasets, one of them have repeat before
|
||||
"""
|
||||
logger.info("test_concat_10")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data1 = data1.repeat(2)
|
||||
data3 = data1 + data2
|
||||
|
||||
res = [0, 1, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert res[i] == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 13
|
||||
|
||||
|
||||
def test_concat_11():
|
||||
"""
|
||||
Test concat: test dataset batch then concat
|
||||
"""
|
||||
logger.info("test_concat_11")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_20, ["col1"])
|
||||
|
||||
data1 = data1.batch(3)
|
||||
data2 = data2.batch(5)
|
||||
|
||||
data3 = data1 + data2
|
||||
res = [0, 10, 15, 20]
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert res[i] == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 3
|
||||
|
||||
|
||||
def test_concat_12():
|
||||
"""
|
||||
Test concat: test dataset concat then shuffle
|
||||
"""
|
||||
logger.info("test_concat_12")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_10, ["col1"])
|
||||
|
||||
data1.set_dataset_size(3)
|
||||
data2.set_dataset_size(7)
|
||||
|
||||
data3 = data1 + data2
|
||||
res = [8, 6, 2, 5, 0, 4, 9, 3, 7, 1]
|
||||
|
||||
ds.config.set_seed(1)
|
||||
assert data3.get_dataset_size() == 10
|
||||
data3 = data3.shuffle(buffer_size=10)
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert res[i] == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 10
|
||||
|
||||
|
||||
def test_concat_13():
|
||||
"""
|
||||
Test concat: test dataset batch then shuffle and concat
|
||||
"""
|
||||
logger.info("test_concat_13")
|
||||
data1 = ds.GeneratorDataset(generator, ["col1"])
|
||||
data2 = ds.GeneratorDataset(generator_20, ["col1"])
|
||||
|
||||
data1.set_dataset_size(3)
|
||||
data2.set_dataset_size(10)
|
||||
|
||||
data1 = data1.batch(3)
|
||||
data2 = data2.batch(5)
|
||||
|
||||
data3 = data1 + data2
|
||||
res = [15, 0, 10]
|
||||
|
||||
ds.config.set_seed(1)
|
||||
assert data3.get_dataset_size() == 3
|
||||
|
||||
data3 = data3.shuffle(buffer_size=int(data3.get_dataset_size()))
|
||||
|
||||
# Here i refers to index, d refers to data element
|
||||
for i, d in enumerate(data3):
|
||||
logger.info("data: %i", d[0][0])
|
||||
assert res[i] == d[0][0]
|
||||
|
||||
assert sum([1 for _ in data3]) == 3
|
||||
|
||||
|
||||
def test_concat_14():
|
||||
"""
|
||||
Test concat: create dataset with different dataset folder, and do diffrent operation then concat
|
||||
"""
|
||||
logger.info("test_concat_14")
|
||||
DATA_DIR = "../data/dataset/testPK/data"
|
||||
DATA_DIR2 = "../data/dataset/testImageNetData/train/"
|
||||
|
||||
data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_samples=3)
|
||||
data2 = ds.ImageFolderDatasetV2(DATA_DIR2, num_samples=2)
|
||||
|
||||
transforms1 = F.ComposeOp([F.Decode(),
|
||||
F.Resize((224,224)),
|
||||
F.ToTensor()])
|
||||
|
||||
data1 = data1.map(input_columns=["image"], operations=transforms1())
|
||||
data2 = data2.map(input_columns=["image"], operations=transforms1())
|
||||
data3 = data1 + data2
|
||||
|
||||
expected, output = [], []
|
||||
for d in data1:
|
||||
expected.append(d[0])
|
||||
for d in data2:
|
||||
expected.append(d[0])
|
||||
for d in data3:
|
||||
output.append(d[0])
|
||||
|
||||
assert len(expected) == len(output)
|
||||
np.array_equal(np.array(output), np.array(expected))
|
||||
|
||||
assert sum([1 for _ in data3]) == 5
|
||||
assert data3.get_dataset_size() == 5
|
||||
|
||||
|
||||
def test_concat_15():
|
||||
"""
|
||||
Test concat: create dataset with different format of dataset file, and then concat
|
||||
"""
|
||||
logger.info("test_concat_15")
|
||||
DATA_DIR = "../data/dataset/testPK/data"
|
||||
DATA_DIR2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
|
||||
data1 = ds.ImageFolderDatasetV2(DATA_DIR)
|
||||
data2 = ds.TFRecordDataset(DATA_DIR2, columns_list=["image"])
|
||||
|
||||
data1 = data1.project(["image"])
|
||||
data3 = data1 + data2
|
||||
|
||||
assert sum([1 for _ in data3]) == 47
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_concat_01()
|
||||
test_concat_02()
|
||||
test_concat_03()
|
||||
test_concat_04()
|
||||
test_concat_05()
|
||||
test_concat_06()
|
||||
test_concat_07()
|
||||
test_concat_08()
|
||||
test_concat_09()
|
||||
test_concat_10()
|
||||
test_concat_11()
|
||||
test_concat_12()
|
||||
test_concat_13()
|
||||
test_concat_14()
|
||||
test_concat_15()
|
Loading…
Reference in New Issue