Mahdi
4 years ago
parent
7104e42304
commit
9c3c36217e
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/**
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* Copyright 2021 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 <string>
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#include <algorithm>
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#include <unordered_map>
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#include <vector>
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#include "minddata/dataset/engine/consumers/pull_based_tree_consumer.h"
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namespace mindspore::dataset {
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PullBasedIteratorConsumer::PullBasedIteratorConsumer() { tree_adapter_lite_ = std::make_unique<TreeAdapterLite>(); }
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Status PullBasedIteratorConsumer::Init(std::shared_ptr<DatasetNode> root) {
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return tree_adapter_lite_->BuildTree(std::move(root));
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}
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std::vector<TensorRow> PullBasedIteratorConsumer::GetRows(int64_t num_rows) {
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std::vector<TensorRow> rows;
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for (int i = 0; i < num_rows; i++) {
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TensorRow row;
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RETURN_SECOND_IF_ERROR(tree_adapter_lite_->GetNextRow(&row), {});
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if (row.empty()) break;
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rows.push_back(row);
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}
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return rows;
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}
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Status PullBasedIteratorConsumer::GetNextAsVector(std::vector<TensorPtr> *out) {
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RETURN_UNEXPECTED_IF_NULL(out);
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out->clear();
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TensorRow res;
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RETURN_IF_NOT_OK(tree_adapter_lite_->GetNextRow(&res));
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// Return empty vector if there's no data
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RETURN_OK_IF_TRUE(res.empty());
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std::copy(res.begin(), res.end(), std::back_inserter(*out));
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return Status::OK();
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}
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Status PullBasedIteratorConsumer::GetNextAsMap(std::unordered_map<std::string, TensorPtr> *const out_map) {
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RETURN_UNEXPECTED_IF_NULL(out_map);
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out_map->clear();
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TensorRow res;
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RETURN_IF_NOT_OK(tree_adapter_lite_->GetNextRow(&res));
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// Return empty map if there's no data
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RETURN_OK_IF_TRUE(res.empty());
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// Populate the out map from the row and return it
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for (const auto &colMap : tree_adapter_lite_->GetColumnNameMap()) {
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(*out_map)[colMap.first] = std::move(res[colMap.second]);
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}
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return Status::OK();
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}
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Status PullBasedIteratorConsumer::GetNextAsOrderedPair(
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std::vector<std::pair<std::string, std::shared_ptr<Tensor>>> *const vec) {
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std::unique_ptr<TreeAdapter> tree_adapter = std::make_unique<TreeAdapter>();
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CHECK_FAIL_RETURN_UNEXPECTED(vec != nullptr && vec->empty(), "vec is null or non-empty.");
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TensorRow curr_row;
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RETURN_IF_NOT_OK(tree_adapter_lite_->GetNextRow(&curr_row));
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RETURN_OK_IF_TRUE(curr_row.empty());
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size_t num_cols = curr_row.size(); // num_cols is non-empty.
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// order the column names according to their ids
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if (column_order_.empty()) {
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const int32_t invalid_col_id = -1;
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column_order_.resize(num_cols, {std::string(), invalid_col_id});
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for (const auto &itr : tree_adapter->GetColumnNameMap()) {
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int32_t ind = itr.second;
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CHECK_FAIL_RETURN_UNEXPECTED(ind < num_cols && ind >= 0, "column id out of bounds.");
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column_order_[ind] = std::make_pair(itr.first, ind);
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}
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// error check, make sure the ids in col_name_id_map are continuous and starts from 0
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for (const auto &col : column_order_) {
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CHECK_FAIL_RETURN_UNEXPECTED(col.second != invalid_col_id, "column ids are not continuous.");
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}
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}
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vec->reserve(num_cols);
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std::transform(column_order_.begin(), column_order_.end(), std::back_inserter(*vec),
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[curr_row](const auto &col) { return std::make_pair(col.first, curr_row[col.second]); });
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return Status::OK();
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}
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} // namespace mindspore::dataset
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@ -0,0 +1,69 @@
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/**
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* Copyright 2021 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 MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_CONSUMERS_PULL_BASED_TREE_CONSUMER_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_CONSUMERS_PULL_BASED_TREE_CONSUMER_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 <utility>
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#include <vector>
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#include <cstddef>
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#include "minddata/dataset/engine/tree_adapter_lite.h"
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namespace mindspore::dataset {
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class TreeAdapterLite;
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class TensorRow;
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/// Consumer that iterates over the dataset and returns the rows one by one as a in a pull based fashion
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class PullBasedIteratorConsumer {
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public:
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/// Constructor
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PullBasedIteratorConsumer();
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~PullBasedIteratorConsumer() = default;
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Status Init(std::shared_ptr<DatasetNode> root);
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/// \brief Returns the next row in a vector format
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/// \note This is currently a placeholder function
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/// \param[in] num_rows the number of rows that we want to get
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/// \param[out] out std::vector of TensorRows
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/// \return Status error code
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std::vector<TensorRow> GetRows(int64_t num_rows);
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/// Returns the next row in a vector format
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/// \param[out] out std::vector of Tensors
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/// \return Status error code
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Status GetNextAsVector(std::vector<TensorPtr> *out);
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/// Returns the next row in as a map
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/// \param[out] out std::map of string to Tensor
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/// \return Status error code
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Status GetNextAsMap(std::unordered_map<std::string, TensorPtr> *out);
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/// Returns the next row in as a vector
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/// \param[out] out std::vector of pairs of string to Tensor
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/// \return Status error code
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Status GetNextAsOrderedPair(std::vector<std::pair<std::string, std::shared_ptr<Tensor>>> *vec);
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private:
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std::unique_ptr<TreeAdapterLite> tree_adapter_lite_;
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std::vector<std::pair<std::string, int32_t>> column_order_; // key: column name, val: column id
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};
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} // namespace mindspore::dataset
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#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_CONSUMERS_PULL_BASED_TREE_CONSUMER_H_
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/**
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* Copyright 2021 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/engine/tree_adapter_lite.h"
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namespace mindspore {
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namespace dataset {
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TreeAdapterLite::TreeAdapterLite() : root_(nullptr) { tree_ = std::make_unique<ExecutionTree>(); }
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Status TreeAdapterLite::BuildExecutionTreeRecur(std::shared_ptr<DatasetNode> ir, std::shared_ptr<DatasetOp> *const op) {
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// Build the DatasetOp ExecutionTree from the optimized IR tree
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std::vector<std::shared_ptr<DatasetOp>> ops;
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RETURN_IF_NOT_OK(ir->Build(&ops));
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CHECK_FAIL_RETURN_UNEXPECTED(!ops.empty(), "Unable to build node.");
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(*op) = ops.front(); // return the first op to be added as child by the caller of this function
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RETURN_IF_NOT_OK(tree_->AssociateNode(*op));
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for (size_t i = 1; i < ops.size(); i++) {
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RETURN_IF_NOT_OK(tree_->AssociateNode(ops[i]));
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RETURN_IF_NOT_OK(ops[i - 1]->AddChild(ops[i]));
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}
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// Build the children of IR, once they return, add the return value to *op
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for (std::shared_ptr<DatasetNode> child_ir : ir->Children()) {
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std::shared_ptr<DatasetOp> child_op;
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RETURN_IF_NOT_OK(BuildExecutionTreeRecur(child_ir, &child_op));
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RETURN_IF_NOT_OK(ops.back()->AddChild(child_op)); // append children to the last of ops
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}
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return Status::OK();
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}
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Status TreeAdapterLite::BuildTree(std::shared_ptr<DatasetNode> root_ir) {
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RETURN_UNEXPECTED_IF_NULL(root_ir);
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RETURN_IF_NOT_OK(BuildExecutionTreeRecur(root_ir, &root_));
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RETURN_IF_NOT_OK(tree_->AssignRoot(root_));
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return Status::OK();
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}
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Status TreeAdapterLite::GetNextRow(TensorRow *row) {
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RETURN_UNEXPECTED_IF_NULL(root_);
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RETURN_IF_NOT_OK(root_->GetNextRow(row));
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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 +1,58 @@
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/**
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* Copyright 2021 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 MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TREE_ADAPTER_LITE_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TREE_ADAPTER_LITE_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 <utility>
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#include <vector>
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#include "minddata/dataset/engine/ir/datasetops/dataset_node.h"
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namespace mindspore {
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namespace dataset {
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class TensorRow;
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class DatasetNode;
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class TreeAdapterLite {
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public:
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TreeAdapterLite();
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~TreeAdapterLite() = default;
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Status BuildTree(std::shared_ptr<DatasetNode> root_ir);
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// Get rows equal to num_rows
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Status GetNextRow(TensorRow *row);
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std::unordered_map<std::string, int32_t> GetColumnNameMap() const { return tree_->root()->column_name_id_map(); }
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private:
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// This RECURSIVE function walks the (optimized) IR tree in DFS to build its corresponding Execution tree.
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Status BuildExecutionTreeRecur(std::shared_ptr<DatasetNode> ir, std::shared_ptr<DatasetOp> *op);
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std::shared_ptr<DatasetOp> root_; // current connector capacity of root op, used for profiling
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std::unique_ptr<ExecutionTree> tree_;
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TREE_ADAPTER_LITE_H_
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@ -0,0 +1,83 @@
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/**
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* Copyright 2021 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 "common/common.h"
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#include "minddata/dataset/include/datasets.h"
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namespace common = mindspore::common;
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using namespace mindspore::dataset;
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class MindDataTestPipeline : public UT::DatasetOpTesting {
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protected:
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};
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TEST_F(MindDataTestPipeline, TestPullBasedBatch) {
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MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAlbumBasic.";
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std::string folder_path = datasets_root_path_ + "/testAlbum/images";
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std::string schema_file = datasets_root_path_ + "/testAlbum/datasetSchema.json";
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std::vector<std::string> column_names = {"label"};
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// Create a Album Dataset
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std::shared_ptr<Dataset> ds = Album(folder_path, schema_file, column_names);
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EXPECT_NE(ds, nullptr);
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int32_t batch_size = 4;
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ds = ds->Batch(batch_size, true);
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EXPECT_NE(ds, nullptr);
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auto iter = ds->CreatePullBasedIterator();
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EXPECT_NE(iter, nullptr);
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std::vector<mindspore::MSTensor> row;
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Status rc = iter->GetNextRow(&row);
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EXPECT_EQ(rc, Status::OK());
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EXPECT_EQ(row.size(), 1);
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auto temp = row[0].Shape();
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std::vector<int64_t> result = {batch_size, 2};
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EXPECT_EQ(row[0].Shape(), result);
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}
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TEST_F(MindDataTestPipeline, TestPullBasedProject) {
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MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAlbumBasic.";
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std::string folder_path = datasets_root_path_ + "/testAlbum/images";
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std::string schema_file = datasets_root_path_ + "/testAlbum/datasetSchema.json";
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std::vector<std::string> column_names = {"label", "image"};
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// Create a Album Dataset
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std::shared_ptr<Dataset> ds = Album(folder_path, schema_file, column_names);
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EXPECT_NE(ds, nullptr);
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std::vector<mindspore::MSTensor> row;
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auto iter = ds->CreatePullBasedIterator();
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EXPECT_NE(iter, nullptr);
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Status rc = iter->GetNextRow(&row);
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EXPECT_EQ(rc, Status::OK());
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EXPECT_EQ(row.size(), 2);
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std::shared_ptr<Dataset> ds2 = Album(folder_path, schema_file, column_names);
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EXPECT_NE(ds2, nullptr);
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std::vector<std::string> columns_to_project = {"image"};
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ds2 = ds2->Project(columns_to_project);
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EXPECT_NE(ds2, nullptr);
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auto iter2 = ds2->CreatePullBasedIterator();
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EXPECT_NE(iter2, nullptr);
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std::vector<mindspore::MSTensor> new_row;
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rc = iter2->GetNextRow(&new_row);
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EXPECT_EQ(rc, Status::OK());
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EXPECT_EQ(new_row.size(), 1);
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}
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