forked from huawei/mindspore2022
restructuring
This commit is contained in:
nhussain
parent
7ec0b5857a
commit
82103a693d
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@ -1,16 +1,29 @@
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file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
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set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
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if (ENABLE_PYTHON)
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add_library(APItoPython OBJECT
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de_pipeline.cc
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python_bindings.cc
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)
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target_include_directories(APItoPython PRIVATE ${pybind11_INCLUDE_DIRS})
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endif()
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add_library(APItoPython OBJECT
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python/de_pipeline.cc
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python/pybind_register.cc
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python/bindings.cc
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python/bindings/dataset/engine/cache/bindings.cc
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python/bindings/dataset/core/bindings.cc
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python/bindings/dataset/kernels/data/bindings.cc
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python/bindings/dataset/kernels/bindings.cc
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python/bindings/dataset/engine/datasetops/bindings.cc
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python/bindings/dataset/engine/datasetops/source/bindings.cc
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python/bindings/dataset/engine/gnn/bindings.cc
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python/bindings/dataset/kernels/image/bindings.cc
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python/bindings/dataset/engine/datasetops/source/sampler/bindings.cc
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python/bindings/dataset/text/bindings.cc
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python/bindings/dataset/text/kernels/bindings.cc
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python/bindings/mindrecord/include/bindings.cc
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)
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target_include_directories(APItoPython PRIVATE ${pybind11_INCLUDE_DIRS})
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endif ()
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add_library(cpp-API OBJECT
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datasets.cc
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iterator.cc
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transforms.cc
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samplers.cc
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)
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datasets.cc
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iterator.cc
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transforms.cc
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samplers.cc
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)
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@ -0,0 +1,122 @@
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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 "pybind11/pybind11.h"
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#include "pybind11/stl.h"
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#include "pybind11/stl_bind.h"
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#include "minddata/dataset/api/python/pybind_register.h"
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#include "minddata/dataset/api/python/de_pipeline.h"
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namespace mindspore {
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namespace dataset {
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PYBIND_REGISTER(
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DEPipeline, 0, ([](const py::module *m) {
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(void)py::class_<DEPipeline>(*m, "DEPipeline")
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.def(py::init<>())
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.def(
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"AddNodeToTree",
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[](DEPipeline &de, const OpName &op_name, const py::dict &args) {
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py::dict out;
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THROW_IF_ERROR(de.AddNodeToTree(op_name, args, &out));
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return out;
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},
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py::return_value_policy::reference)
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.def_static("AddChildToParentNode",
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[](const DsOpPtr &child_op, const DsOpPtr &parent_op) {
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THROW_IF_ERROR(DEPipeline::AddChildToParentNode(child_op, parent_op));
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})
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.def("AssignRootNode",
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[](DEPipeline &de, const DsOpPtr &dataset_op) { THROW_IF_ERROR(de.AssignRootNode(dataset_op)); })
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.def("SetBatchParameters",
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[](DEPipeline &de, const py::dict &args) { THROW_IF_ERROR(de.SetBatchParameters(args)); })
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.def("LaunchTreeExec", [](DEPipeline &de, int32_t num_epochs) { THROW_IF_ERROR(de.LaunchTreeExec(num_epochs)); })
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.def("GetNextAsMap",
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[](DEPipeline &de) {
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py::dict out;
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THROW_IF_ERROR(de.GetNextAsMap(&out));
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return out;
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})
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.def("GetNextAsList",
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[](DEPipeline &de) {
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py::list out;
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THROW_IF_ERROR(de.GetNextAsList(&out));
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return out;
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})
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.def("GetOutputShapes",
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[](DEPipeline &de) {
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py::list out;
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THROW_IF_ERROR(de.GetOutputShapes(&out));
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return out;
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})
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.def("GetOutputTypes",
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[](DEPipeline &de) {
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py::list out;
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THROW_IF_ERROR(de.GetOutputTypes(&out));
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return out;
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})
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.def("GetDatasetSize", &DEPipeline::GetDatasetSize)
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.def("GetBatchSize", &DEPipeline::GetBatchSize)
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.def("GetNumClasses", &DEPipeline::GetNumClasses)
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.def("GetRepeatCount", &DEPipeline::GetRepeatCount)
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.def("StopSend", [](DEPipeline &de) { THROW_IF_ERROR(de.StopSend()); })
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.def("SaveDataset", [](DEPipeline &de, const std::vector<std::string> &file_names, const std::string &file_type) {
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THROW_IF_ERROR(de.SaveDataset(file_names, file_type));
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return true;
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});
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}));
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PYBIND_REGISTER(OpName, 0, ([](const py::module *m) {
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(void)py::enum_<OpName>(*m, "OpName", py::arithmetic())
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.value("SHUFFLE", OpName::kShuffle)
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.value("BATCH", OpName::kBatch)
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.value("BUCKETBATCH", OpName::kBucketBatch)
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.value("BARRIER", OpName::kBarrier)
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.value("MINDRECORD", OpName::kMindrecord)
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.value("CACHE", OpName::kCache)
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.value("REPEAT", OpName::kRepeat)
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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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.value("GENERATOR", OpName::kGenerator)
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.export_values()
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.value("RENAME", OpName::kRename)
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.value("TFREADER", OpName::kTfReader)
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.value("PROJECT", OpName::kProject)
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.value("IMAGEFOLDER", OpName::kImageFolder)
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.value("MNIST", OpName::kMnist)
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.value("MANIFEST", OpName::kManifest)
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.value("VOC", OpName::kVoc)
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.value("COCO", OpName::kCoco)
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.value("CIFAR10", OpName::kCifar10)
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.value("CIFAR100", OpName::kCifar100)
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.value("RANDOMDATA", OpName::kRandomData)
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.value("BUILDVOCAB", OpName::kBuildVocab)
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.value("SENTENCEPIECEVOCAB", OpName::kSentencePieceVocab)
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.value("CELEBA", OpName::kCelebA)
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.value("TEXTFILE", OpName::kTextFile)
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.value("EPOCHCTRL", OpName::kEpochCtrl)
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.value("CSV", OpName::kCsv)
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.value("CLUE", OpName::kClue);
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}));
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} // namespace dataset
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} // namespace mindspore
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@ -0,0 +1,114 @@
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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 "pybind11/pybind11.h"
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#include "pybind11/stl.h"
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#include "pybind11/stl_bind.h"
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#include "minddata/dataset/api/python/pybind_register.h"
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#include "minddata/dataset/core/global_context.h"
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#include "minddata/dataset/core/constants.h"
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#include "minddata/dataset/api/python/de_pipeline.h"
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namespace mindspore {
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namespace dataset {
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PYBIND_REGISTER(GlobalContext, 0, ([](const py::module *m) {
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(void)py::class_<GlobalContext>(*m, "GlobalContext")
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.def_static("config_manager", &GlobalContext::config_manager, py::return_value_policy::reference);
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}));
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PYBIND_REGISTER(ConfigManager, 0, ([](const py::module *m) {
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(void)py::class_<ConfigManager, std::shared_ptr<ConfigManager>>(*m, "ConfigManager")
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.def("__str__", &ConfigManager::ToString)
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.def("set_rows_per_buffer", &ConfigManager::set_rows_per_buffer)
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.def("set_num_parallel_workers", &ConfigManager::set_num_parallel_workers)
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.def("set_worker_connector_size", &ConfigManager::set_worker_connector_size)
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.def("set_op_connector_size", &ConfigManager::set_op_connector_size)
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.def("set_seed", &ConfigManager::set_seed)
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.def("set_monitor_sampling_interval", &ConfigManager::set_monitor_sampling_interval)
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.def("get_rows_per_buffer", &ConfigManager::rows_per_buffer)
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.def("get_num_parallel_workers", &ConfigManager::num_parallel_workers)
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.def("get_worker_connector_size", &ConfigManager::worker_connector_size)
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.def("get_op_connector_size", &ConfigManager::op_connector_size)
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.def("get_seed", &ConfigManager::seed)
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.def("get_monitor_sampling_interval", &ConfigManager::monitor_sampling_interval)
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.def("load", [](ConfigManager &c, std::string s) { THROW_IF_ERROR(c.LoadFile(s)); });
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}));
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PYBIND_REGISTER(Tensor, 0, ([](const py::module *m) {
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(void)py::class_<Tensor, std::shared_ptr<Tensor>>(*m, "Tensor", py::buffer_protocol())
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.def(py::init([](py::array arr) {
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std::shared_ptr<Tensor> out;
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THROW_IF_ERROR(Tensor::CreateFromNpArray(arr, &out));
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return out;
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}))
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.def_buffer([](Tensor &tensor) {
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py::buffer_info info;
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THROW_IF_ERROR(Tensor::GetBufferInfo(&tensor, &info));
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return info;
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})
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.def("__str__", &Tensor::ToString)
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.def("shape", &Tensor::shape)
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.def("type", &Tensor::type)
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.def("as_array", [](py::object &t) {
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auto &tensor = py::cast<Tensor &>(t);
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if (tensor.type() == DataType::DE_STRING) {
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py::array res;
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tensor.GetDataAsNumpyStrings(&res);
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return res;
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}
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py::buffer_info info;
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THROW_IF_ERROR(Tensor::GetBufferInfo(&tensor, &info));
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return py::array(pybind11::dtype(info), info.shape, info.strides, info.ptr, t);
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});
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}));
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PYBIND_REGISTER(TensorShape, 0, ([](const py::module *m) {
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(void)py::class_<TensorShape>(*m, "TensorShape")
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.def(py::init<py::list>())
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.def("__str__", &TensorShape::ToString)
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.def("as_list", &TensorShape::AsPyList)
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.def("is_known", &TensorShape::known);
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}));
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PYBIND_REGISTER(DataType, 0, ([](const py::module *m) {
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(void)py::class_<DataType>(*m, "DataType")
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.def(py::init<std::string>())
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.def(py::self == py::self)
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.def("__str__", &DataType::ToString)
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.def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
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}));
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PYBIND_REGISTER(BorderType, 0, ([](const py::module *m) {
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(void)py::enum_<BorderType>(*m, "BorderType", py::arithmetic())
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.value("DE_BORDER_CONSTANT", BorderType::kConstant)
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.value("DE_BORDER_EDGE", BorderType::kEdge)
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.value("DE_BORDER_REFLECT", BorderType::kReflect)
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.value("DE_BORDER_SYMMETRIC", BorderType::kSymmetric)
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.export_values();
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}));
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PYBIND_REGISTER(InterpolationMode, 0, ([](const py::module *m) {
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(void)py::enum_<InterpolationMode>(*m, "InterpolationMode", py::arithmetic())
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.value("DE_INTER_LINEAR", InterpolationMode::kLinear)
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.value("DE_INTER_CUBIC", InterpolationMode::kCubic)
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.value("DE_INTER_AREA", InterpolationMode::kArea)
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.value("DE_INTER_NEAREST_NEIGHBOUR", InterpolationMode::kNearestNeighbour)
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.export_values();
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}));
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} // namespace dataset
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} // namespace mindspore
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29
mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/cache/bindings.cc
vendored
Normal file
29
mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/cache/bindings.cc
vendored
Normal file
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@ -0,0 +1,29 @@
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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.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
||||
* 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.
|
||||
*/
|
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#include "minddata/dataset/api/python/pybind_register.h"
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#include "minddata/dataset/engine/cache/cache_client.h"
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namespace mindspore {
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namespace dataset {
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PYBIND_REGISTER(CacheClient, 0, ([](const py::module *m) {
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(void)py::class_<CacheClient, std::shared_ptr<CacheClient>>(*m, "CacheClient")
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.def(py::init<uint32_t, uint64_t, bool>());
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}));
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} // namespace dataset
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} // namespace mindspore
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@ -0,0 +1,35 @@
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
|
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*
|
||||
* 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.
|
||||
*/
|
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|
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#include "minddata/dataset/api/python/pybind_register.h"
|
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#include "minddata/dataset/engine/datasetops/batch_op.h"
|
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namespace mindspore {
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namespace dataset {
|
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PYBIND_REGISTER(CBatchInfo, 0, ([](const py::module *m) {
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(void)py::class_<BatchOp::CBatchInfo>(*m, "CBatchInfo")
|
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.def(py::init<int64_t, int64_t, int64_t>())
|
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.def("get_epoch_num", &BatchOp::CBatchInfo::get_epoch_num)
|
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.def("get_batch_num", &BatchOp::CBatchInfo::get_batch_num);
|
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}));
|
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|
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PYBIND_REGISTER(DatasetOp, 0, ([](const py::module *m) {
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(void)py::class_<DatasetOp, std::shared_ptr<DatasetOp>>(*m, "DatasetOp");
|
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}));
|
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|
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} // namespace dataset
|
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} // namespace mindspore
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|
|
@ -0,0 +1,186 @@
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/**
|
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* 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 "minddata/dataset/api/python/pybind_register.h"
|
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|
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#include "pybind11/pybind11.h"
|
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#include "pybind11/stl.h"
|
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#include "pybind11/stl_bind.h"
|
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|
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#include "minddata/dataset/engine/datasetops/dataset_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/cifar_op.h"
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#include "minddata/dataset/engine/datasetops/source/clue_op.h"
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#include "minddata/dataset/engine/datasetops/source/csv_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/coco_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/image_folder_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/io_block.h"
|
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#include "minddata/dataset/engine/datasetops/source/manifest_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/mindrecord_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/mnist_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/random_data_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/text_file_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/tf_reader_op.h"
|
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#include "minddata/dataset/engine/datasetops/source/voc_op.h"
|
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|
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namespace mindspore {
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namespace dataset {
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PYBIND_REGISTER(CifarOp, 1, ([](const py::module *m) {
|
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(void)py::class_<CifarOp, DatasetOp, std::shared_ptr<CifarOp>>(*m, "CifarOp")
|
||||
.def_static("get_num_rows", [](const std::string &dir, bool isCifar10) {
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int64_t count = 0;
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THROW_IF_ERROR(CifarOp::CountTotalRows(dir, isCifar10, &count));
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return count;
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||||
});
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||||
}));
|
||||
|
||||
PYBIND_REGISTER(ClueOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ClueOp, DatasetOp, std::shared_ptr<ClueOp>>(*m, "ClueOp")
|
||||
.def_static("get_num_rows", [](const py::list &files) {
|
||||
int64_t count = 0;
|
||||
std::vector<std::string> filenames;
|
||||
for (auto file : files) {
|
||||
file.is_none() ? (void)filenames.emplace_back("") : filenames.push_back(py::str(file));
|
||||
}
|
||||
THROW_IF_ERROR(ClueOp::CountAllFileRows(filenames, &count));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(CsvOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<CsvOp, DatasetOp, std::shared_ptr<CsvOp>>(*m, "CsvOp")
|
||||
.def_static("get_num_rows", [](const py::list &files, bool csv_header) {
|
||||
int64_t count = 0;
|
||||
std::vector<std::string> filenames;
|
||||
for (auto file : files) {
|
||||
file.is_none() ? (void)filenames.emplace_back("") : filenames.push_back(py::str(file));
|
||||
}
|
||||
THROW_IF_ERROR(CsvOp::CountAllFileRows(filenames, csv_header, &count));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
PYBIND_REGISTER(CocoOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<CocoOp, DatasetOp, std::shared_ptr<CocoOp>>(*m, "CocoOp")
|
||||
.def_static("get_class_indexing",
|
||||
[](const std::string &dir, const std::string &file, const std::string &task) {
|
||||
std::vector<std::pair<std::string, std::vector<int32_t>>> output_class_indexing;
|
||||
THROW_IF_ERROR(CocoOp::GetClassIndexing(dir, file, task, &output_class_indexing));
|
||||
return output_class_indexing;
|
||||
})
|
||||
.def_static("get_num_rows",
|
||||
[](const std::string &dir, const std::string &file, const std::string &task) {
|
||||
int64_t count = 0;
|
||||
THROW_IF_ERROR(CocoOp::CountTotalRows(dir, file, task, &count));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ImageFolderOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ImageFolderOp, DatasetOp, std::shared_ptr<ImageFolderOp>>(*m, "ImageFolderOp")
|
||||
.def_static("get_num_rows_and_classes", [](const std::string &path) {
|
||||
int64_t count = 0, num_classes = 0;
|
||||
THROW_IF_ERROR(
|
||||
ImageFolderOp::CountRowsAndClasses(path, std::set<std::string>{}, &count, &num_classes));
|
||||
return py::make_tuple(count, num_classes);
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ManifestOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ManifestOp, DatasetOp, std::shared_ptr<ManifestOp>>(*m, "ManifestOp")
|
||||
.def_static("get_num_rows_and_classes",
|
||||
[](const std::string &file, const py::dict &dict, const std::string &usage) {
|
||||
int64_t count = 0, num_classes = 0;
|
||||
THROW_IF_ERROR(ManifestOp::CountTotalRows(file, dict, usage, &count, &num_classes));
|
||||
return py::make_tuple(count, num_classes);
|
||||
})
|
||||
.def_static("get_class_indexing", [](const std::string &file, const py::dict &dict,
|
||||
const std::string &usage) {
|
||||
std::map<std::string, int32_t> output_class_indexing;
|
||||
THROW_IF_ERROR(ManifestOp::GetClassIndexing(file, dict, usage, &output_class_indexing));
|
||||
return output_class_indexing;
|
||||
});
|
||||
}));
|
||||
PYBIND_REGISTER(MindRecordOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<MindRecordOp, DatasetOp, std::shared_ptr<MindRecordOp>>(*m, "MindRecordOp")
|
||||
.def_static("get_num_rows", [](const std::vector<std::string> &paths, bool load_dataset,
|
||||
const py::object &sampler, const int64_t num_padded) {
|
||||
int64_t count = 0;
|
||||
std::shared_ptr<mindrecord::ShardOperator> op;
|
||||
if (py::hasattr(sampler, "create_for_minddataset")) {
|
||||
auto create = sampler.attr("create_for_minddataset");
|
||||
op = create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
|
||||
}
|
||||
THROW_IF_ERROR(MindRecordOp::CountTotalRows(paths, load_dataset, op, &count, num_padded));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(MnistOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<MnistOp, DatasetOp, std::shared_ptr<MnistOp>>(*m, "MnistOp")
|
||||
.def_static("get_num_rows", [](const std::string &dir) {
|
||||
int64_t count = 0;
|
||||
THROW_IF_ERROR(MnistOp::CountTotalRows(dir, &count));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(TextFileOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<TextFileOp, DatasetOp, std::shared_ptr<TextFileOp>>(*m, "TextFileOp")
|
||||
.def_static("get_num_rows", [](const py::list &files) {
|
||||
int64_t count = 0;
|
||||
std::vector<std::string> filenames;
|
||||
for (auto file : files) {
|
||||
!file.is_none() ? filenames.push_back(py::str(file)) : (void)filenames.emplace_back("");
|
||||
}
|
||||
THROW_IF_ERROR(TextFileOp::CountAllFileRows(filenames, &count));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(TFReaderOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<TFReaderOp, DatasetOp, std::shared_ptr<TFReaderOp>>(*m, "TFReaderOp")
|
||||
.def_static(
|
||||
"get_num_rows", [](const py::list &files, int64_t numParallelWorkers, bool estimate = false) {
|
||||
int64_t count = 0;
|
||||
std::vector<std::string> filenames;
|
||||
for (auto l : files) {
|
||||
!l.is_none() ? filenames.push_back(py::str(l)) : (void)filenames.emplace_back("");
|
||||
}
|
||||
THROW_IF_ERROR(TFReaderOp::CountTotalRows(&count, filenames, numParallelWorkers, estimate));
|
||||
return count;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(VOCOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<VOCOp, DatasetOp, std::shared_ptr<VOCOp>>(*m, "VOCOp")
|
||||
.def_static("get_num_rows",
|
||||
[](const std::string &dir, const std::string &task_type, const std::string &task_mode,
|
||||
const py::dict &dict, int64_t numSamples) {
|
||||
int64_t count = 0;
|
||||
THROW_IF_ERROR(VOCOp::CountTotalRows(dir, task_type, task_mode, dict, &count));
|
||||
return count;
|
||||
})
|
||||
.def_static("get_class_indexing", [](const std::string &dir, const std::string &task_type,
|
||||
const std::string &task_mode, const py::dict &dict) {
|
||||
std::map<std::string, int32_t> output_class_indexing;
|
||||
THROW_IF_ERROR(VOCOp::GetClassIndexing(dir, task_type, task_mode, dict, &output_class_indexing));
|
||||
return output_class_indexing;
|
||||
});
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
/**
|
||||
* 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 "minddata/dataset/api/python/pybind_register.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/distributed_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/pk_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/python_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/random_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/subset_random_sampler.h"
|
||||
#include "minddata/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(Sampler, 0, ([](const py::module *m) {
|
||||
(void)py::class_<Sampler, std::shared_ptr<Sampler>>(*m, "Sampler")
|
||||
.def("set_num_rows",
|
||||
[](Sampler &self, int64_t rows) { THROW_IF_ERROR(self.SetNumRowsInDataset(rows)); })
|
||||
.def("set_num_samples",
|
||||
[](Sampler &self, int64_t samples) { THROW_IF_ERROR(self.SetNumSamples(samples)); })
|
||||
.def("initialize", [](Sampler &self) { THROW_IF_ERROR(self.InitSampler()); })
|
||||
.def("get_indices",
|
||||
[](Sampler &self) {
|
||||
py::array ret;
|
||||
THROW_IF_ERROR(self.GetAllIdsThenReset(&ret));
|
||||
return ret;
|
||||
})
|
||||
.def("add_child", [](std::shared_ptr<Sampler> self, std::shared_ptr<Sampler> child) {
|
||||
THROW_IF_ERROR(self->AddChild(child));
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(DistributedSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<DistributedSampler, Sampler, std::shared_ptr<DistributedSampler>>(
|
||||
*m, "DistributedSampler")
|
||||
.def(py::init<int64_t, int64_t, int64_t, bool, uint32_t>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(PKSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<PKSampler, Sampler, std::shared_ptr<PKSampler>>(*m, "PKSampler")
|
||||
.def(py::init<int64_t, int64_t, bool>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(PythonSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<PythonSampler, Sampler, std::shared_ptr<PythonSampler>>(*m, "PythonSampler")
|
||||
.def(py::init<int64_t, py::object>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomSampler, Sampler, std::shared_ptr<RandomSampler>>(*m, "RandomSampler")
|
||||
.def(py::init<int64_t, bool, bool>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SequentialSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<SequentialSampler, Sampler, std::shared_ptr<SequentialSampler>>(*m,
|
||||
"SequentialSampler")
|
||||
.def(py::init<int64_t, int64_t>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SubsetRandomSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<SubsetRandomSampler, Sampler, std::shared_ptr<SubsetRandomSampler>>(
|
||||
*m, "SubsetRandomSampler")
|
||||
.def(py::init<int64_t, std::vector<int64_t>>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(WeightedRandomSampler, 1, ([](const py::module *m) {
|
||||
(void)py::class_<WeightedRandomSampler, Sampler, std::shared_ptr<WeightedRandomSampler>>(
|
||||
*m, "WeightedRandomSampler")
|
||||
.def(py::init<int64_t, std::vector<double>, bool>());
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
|
||||
#include "minddata/dataset/engine/gnn/graph.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(
|
||||
Graph, 0, ([](const py::module *m) {
|
||||
(void)py::class_<gnn::Graph, std::shared_ptr<gnn::Graph>>(*m, "Graph")
|
||||
.def(py::init([](std::string dataset_file, int32_t num_workers) {
|
||||
std::shared_ptr<gnn::Graph> g_out = std::make_shared<gnn::Graph>(dataset_file, num_workers);
|
||||
THROW_IF_ERROR(g_out->Init());
|
||||
return g_out;
|
||||
}))
|
||||
.def("get_all_nodes",
|
||||
[](gnn::Graph &g, gnn::NodeType node_type) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetAllNodes(node_type, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_all_edges",
|
||||
[](gnn::Graph &g, gnn::EdgeType edge_type) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetAllEdges(edge_type, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_nodes_from_edges",
|
||||
[](gnn::Graph &g, std::vector<gnn::NodeIdType> edge_list) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetNodesFromEdges(edge_list, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_all_neighbors",
|
||||
[](gnn::Graph &g, std::vector<gnn::NodeIdType> node_list, gnn::NodeType neighbor_type) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetAllNeighbors(node_list, neighbor_type, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_sampled_neighbors",
|
||||
[](gnn::Graph &g, std::vector<gnn::NodeIdType> node_list, std::vector<gnn::NodeIdType> neighbor_nums,
|
||||
std::vector<gnn::NodeType> neighbor_types) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetSampledNeighbors(node_list, neighbor_nums, neighbor_types, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_neg_sampled_neighbors",
|
||||
[](gnn::Graph &g, std::vector<gnn::NodeIdType> node_list, gnn::NodeIdType neighbor_num,
|
||||
gnn::NodeType neg_neighbor_type) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.GetNegSampledNeighbors(node_list, neighbor_num, neg_neighbor_type, &out));
|
||||
return out;
|
||||
})
|
||||
.def("get_node_feature",
|
||||
[](gnn::Graph &g, std::shared_ptr<Tensor> node_list, std::vector<gnn::FeatureType> feature_types) {
|
||||
TensorRow out;
|
||||
THROW_IF_ERROR(g.GetNodeFeature(node_list, feature_types, &out));
|
||||
return out.getRow();
|
||||
})
|
||||
.def("get_edge_feature",
|
||||
[](gnn::Graph &g, std::shared_ptr<Tensor> edge_list, std::vector<gnn::FeatureType> feature_types) {
|
||||
TensorRow out;
|
||||
THROW_IF_ERROR(g.GetEdgeFeature(edge_list, feature_types, &out));
|
||||
return out.getRow();
|
||||
})
|
||||
.def("graph_info",
|
||||
[](gnn::Graph &g) {
|
||||
py::dict out;
|
||||
THROW_IF_ERROR(g.GraphInfo(&out));
|
||||
return out;
|
||||
})
|
||||
.def("random_walk",
|
||||
[](gnn::Graph &g, std::vector<gnn::NodeIdType> node_list, std::vector<gnn::NodeType> meta_path,
|
||||
float step_home_param, float step_away_param, gnn::NodeIdType default_node) {
|
||||
std::shared_ptr<Tensor> out;
|
||||
THROW_IF_ERROR(g.RandomWalk(node_list, meta_path, step_home_param, step_away_param, default_node, &out));
|
||||
return out;
|
||||
});
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,91 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
#include "minddata/dataset/core/global_context.h"
|
||||
#include "minddata/dataset/api/python/de_pipeline.h"
|
||||
|
||||
#include "mindspore/ccsrc/minddata/dataset/kernels/data/compose_op.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/kernels/data/no_op.h"
|
||||
#include "minddata/dataset/kernels/py_func_op.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/kernels/data/random_apply_op.h"
|
||||
#include "mindspore/ccsrc/minddata/dataset/kernels/data/random_choice_op.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
Status PyListToTensorOps(const py::list &py_ops, std::vector<std::shared_ptr<TensorOp>> *ops) {
|
||||
RETURN_UNEXPECTED_IF_NULL(ops);
|
||||
for (auto op : py_ops) {
|
||||
if (py::isinstance<TensorOp>(op)) {
|
||||
ops->emplace_back(op.cast<std::shared_ptr<TensorOp>>());
|
||||
} else if (py::isinstance<py::function>(op)) {
|
||||
ops->emplace_back(std::make_shared<PyFuncOp>(op.cast<py::function>()));
|
||||
} else {
|
||||
RETURN_STATUS_UNEXPECTED("element is neither a TensorOp nor a pyfunc.");
|
||||
}
|
||||
}
|
||||
CHECK_FAIL_RETURN_UNEXPECTED(!ops->empty(), "TensorOp list is empty.");
|
||||
for (auto const &op : *ops) {
|
||||
RETURN_UNEXPECTED_IF_NULL(op);
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
PYBIND_REGISTER(TensorOp, 0, ([](const py::module *m) {
|
||||
(void)py::class_<TensorOp, std::shared_ptr<TensorOp>>(*m, "TensorOp")
|
||||
.def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ComposeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ComposeOp, TensorOp, std::shared_ptr<ComposeOp>>(*m, "ComposeOp")
|
||||
.def(py::init([](const py::list &ops) {
|
||||
std::vector<std::shared_ptr<TensorOp>> t_ops;
|
||||
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
|
||||
return std::make_shared<ComposeOp>(t_ops);
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(NoOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<NoOp, TensorOp, std::shared_ptr<NoOp>>(
|
||||
*m, "NoOp", "TensorOp that does nothing, for testing purposes only.")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomChoiceOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomChoiceOp, TensorOp, std::shared_ptr<RandomChoiceOp>>(*m, "RandomChoiceOp")
|
||||
.def(py::init([](const py::list &ops) {
|
||||
std::vector<std::shared_ptr<TensorOp>> t_ops;
|
||||
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
|
||||
return std::make_shared<RandomChoiceOp>(t_ops);
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomApplyOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomApplyOp, TensorOp, std::shared_ptr<RandomApplyOp>>(*m, "RandomApplyOp")
|
||||
.def(py::init([](double prob, const py::list &ops) {
|
||||
std::vector<std::shared_ptr<TensorOp>> t_ops;
|
||||
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
|
||||
if (prob < 0 || prob > 1) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be within [0,1]."));
|
||||
}
|
||||
return std::make_shared<RandomApplyOp>(prob, t_ops);
|
||||
}));
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,133 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
#include "minddata/dataset/kernels/data/concatenate_op.h"
|
||||
#include "minddata/dataset/kernels/data/duplicate_op.h"
|
||||
#include "minddata/dataset/kernels/data/fill_op.h"
|
||||
#include "minddata/dataset/kernels/data/mask_op.h"
|
||||
#include "minddata/dataset/kernels/data/one_hot_op.h"
|
||||
#include "minddata/dataset/kernels/data/pad_end_op.h"
|
||||
#include "minddata/dataset/kernels/data/slice_op.h"
|
||||
#include "minddata/dataset/kernels/data/to_float16_op.h"
|
||||
#include "minddata/dataset/kernels/data/type_cast_op.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(ConcatenateOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ConcatenateOp, TensorOp, std::shared_ptr<ConcatenateOp>>(
|
||||
*m, "ConcatenateOp", "Tensor operation concatenate tensors.")
|
||||
.def(py::init<int8_t, std::shared_ptr<Tensor>, std::shared_ptr<Tensor>>(), py::arg("axis"),
|
||||
py::arg("prepend").none(true), py::arg("append").none(true));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(DuplicateOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<DuplicateOp, TensorOp, std::shared_ptr<DuplicateOp>>(*m, "DuplicateOp",
|
||||
"Duplicate tensor.")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(FillOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<FillOp, TensorOp, std::shared_ptr<FillOp>>(
|
||||
*m, "FillOp", "Tensor operation to return tensor filled with same value as input fill value.")
|
||||
.def(py::init<std::shared_ptr<Tensor>>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(MaskOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<MaskOp, TensorOp, std::shared_ptr<MaskOp>>(
|
||||
*m, "MaskOp", "Tensor mask operation using relational comparator")
|
||||
.def(py::init<RelationalOp, std::shared_ptr<Tensor>, DataType>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(OneHotOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<OneHotOp, TensorOp, std::shared_ptr<OneHotOp>>(
|
||||
*m, "OneHotOp", "Tensor operation to apply one hot encoding. Takes number of classes.")
|
||||
.def(py::init<int32_t>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(PadEndOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<PadEndOp, TensorOp, std::shared_ptr<PadEndOp>>(
|
||||
*m, "PadEndOp", "Tensor operation to pad end of tensor with a pad value.")
|
||||
.def(py::init<TensorShape, std::shared_ptr<Tensor>>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SliceOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<SliceOp, TensorOp, std::shared_ptr<SliceOp>>(*m, "SliceOp",
|
||||
"Tensor slice operation.")
|
||||
.def(py::init<bool>())
|
||||
.def(py::init([](const py::list &py_list) {
|
||||
std::vector<dsize_t> c_list;
|
||||
for (auto l : py_list) {
|
||||
if (!l.is_none()) {
|
||||
c_list.push_back(py::reinterpret_borrow<py::int_>(l));
|
||||
}
|
||||
}
|
||||
return std::make_shared<SliceOp>(c_list);
|
||||
}))
|
||||
.def(py::init([](const py::tuple &py_slice) {
|
||||
if (py_slice.size() != 3) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
|
||||
}
|
||||
Slice c_slice;
|
||||
if (!py_slice[0].is_none() && !py_slice[1].is_none() && !py_slice[2].is_none()) {
|
||||
c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[0]),
|
||||
py::reinterpret_borrow<py::int_>(py_slice[1]),
|
||||
py::reinterpret_borrow<py::int_>(py_slice[2]));
|
||||
} else if (py_slice[0].is_none() && py_slice[2].is_none()) {
|
||||
c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[1]));
|
||||
} else if (!py_slice[0].is_none() && !py_slice[1].is_none()) {
|
||||
c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[0]),
|
||||
py::reinterpret_borrow<py::int_>(py_slice[1]));
|
||||
}
|
||||
|
||||
if (!c_slice.valid()) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
|
||||
}
|
||||
return std::make_shared<SliceOp>(c_slice);
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ToFloat16Op, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ToFloat16Op, TensorOp, std::shared_ptr<ToFloat16Op>>(
|
||||
*m, "ToFloat16Op", py::dynamic_attr(),
|
||||
"Tensor operator to type cast float32 data to a float16 type.")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(TypeCastOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<TypeCastOp, TensorOp, std::shared_ptr<TypeCastOp>>(
|
||||
*m, "TypeCastOp", "Tensor operator to type cast data to a specified type.")
|
||||
.def(py::init<DataType>(), py::arg("data_type"))
|
||||
.def(py::init<std::string>(), py::arg("data_type"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RelationalOp, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<RelationalOp>(*m, "RelationalOp", py::arithmetic())
|
||||
.value("EQ", RelationalOp::kEqual)
|
||||
.value("NE", RelationalOp::kNotEqual)
|
||||
.value("LT", RelationalOp::kLess)
|
||||
.value("LE", RelationalOp::kLessEqual)
|
||||
.value("GT", RelationalOp::kGreater)
|
||||
.value("GE", RelationalOp::kGreaterEqual)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,346 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
#include "minddata/dataset/kernels/py_func_op.h"
|
||||
#include "minddata/dataset/kernels/image/auto_contrast_op.h"
|
||||
#include "minddata/dataset/kernels/image/bounding_box_augment_op.h"
|
||||
#include "minddata/dataset/kernels/image/center_crop_op.h"
|
||||
#include "minddata/dataset/kernels/image/cut_out_op.h"
|
||||
#include "minddata/dataset/kernels/image/decode_op.h"
|
||||
#include "minddata/dataset/kernels/image/equalize_op.h"
|
||||
#include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
|
||||
#include "minddata/dataset/kernels/image/image_utils.h"
|
||||
#include "minddata/dataset/kernels/image/invert_op.h"
|
||||
#include "minddata/dataset/kernels/image/normalize_op.h"
|
||||
#include "minddata/dataset/kernels/image/pad_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_color_adjust_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_crop_and_resize_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_crop_and_resize_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_crop_decode_resize_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_crop_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_crop_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_resize_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_resize_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_rotation_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_select_subpolicy_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
|
||||
#include "minddata/dataset/kernels/image/random_vertical_flip_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/rescale_op.h"
|
||||
#include "minddata/dataset/kernels/image/resize_bilinear_op.h"
|
||||
#include "minddata/dataset/kernels/image/resize_op.h"
|
||||
#include "minddata/dataset/kernels/image/resize_with_bbox_op.h"
|
||||
#include "minddata/dataset/kernels/image/uniform_aug_op.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(AutoContrastOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<AutoContrastOp, TensorOp, std::shared_ptr<AutoContrastOp>>(
|
||||
*m, "AutoContrastOp", "Tensor operation to apply autocontrast on an image.")
|
||||
.def(py::init<float, std::vector<uint32_t>>(), py::arg("cutoff") = AutoContrastOp::kCutOff,
|
||||
py::arg("ignore") = AutoContrastOp::kIgnore);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(NormalizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<NormalizeOp, TensorOp, std::shared_ptr<NormalizeOp>>(
|
||||
*m, "NormalizeOp", "Tensor operation to normalize an image. Takes mean and std.")
|
||||
.def(py::init<float, float, float, float, float, float>(), py::arg("meanR"), py::arg("meanG"),
|
||||
py::arg("meanB"), py::arg("stdR"), py::arg("stdG"), py::arg("stdB"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(EqualizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<EqualizeOp, TensorOp, std::shared_ptr<EqualizeOp>>(
|
||||
*m, "EqualizeOp", "Tensor operation to apply histogram equalization on images.")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(InvertOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<InvertOp, TensorOp, std::shared_ptr<InvertOp>>(
|
||||
*m, "InvertOp", "Tensor operation to apply invert on RGB images.")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RescaleOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RescaleOp, TensorOp, std::shared_ptr<RescaleOp>>(
|
||||
*m, "RescaleOp", "Tensor operation to rescale an image. Takes scale and shift.")
|
||||
.def(py::init<float, float>(), py::arg("rescale"), py::arg("shift"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(CenterCropOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<CenterCropOp, TensorOp, std::shared_ptr<CenterCropOp>>(
|
||||
*m, "CenterCropOp",
|
||||
"Tensor operation to crop and image in the middle. Takes height and width (optional)")
|
||||
.def(py::init<int32_t, int32_t>(), py::arg("height"), py::arg("width") = CenterCropOp::kDefWidth);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ResizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ResizeOp, TensorOp, std::shared_ptr<ResizeOp>>(
|
||||
*m, "ResizeOp", "Tensor operation to resize an image. Takes height, width and mode")
|
||||
.def(py::init<int32_t, int32_t, InterpolationMode>(), py::arg("targetHeight"),
|
||||
py::arg("targetWidth") = ResizeOp::kDefWidth,
|
||||
py::arg("interpolation") = ResizeOp::kDefInterpolation);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ResizeWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ResizeWithBBoxOp, TensorOp, std::shared_ptr<ResizeWithBBoxOp>>(
|
||||
*m, "ResizeWithBBoxOp", "Tensor operation to resize an image. Takes height, width and mode.")
|
||||
.def(py::init<int32_t, int32_t, InterpolationMode>(), py::arg("targetHeight"),
|
||||
py::arg("targetWidth") = ResizeWithBBoxOp::kDefWidth,
|
||||
py::arg("interpolation") = ResizeWithBBoxOp::kDefInterpolation);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
RandomResizeWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomResizeWithBBoxOp, TensorOp, std::shared_ptr<RandomResizeWithBBoxOp>>(
|
||||
*m, "RandomResizeWithBBoxOp",
|
||||
"Tensor operation to resize an image using a randomly selected interpolation. Takes height and width.")
|
||||
.def(py::init<int32_t, int32_t>(), py::arg("targetHeight"),
|
||||
py::arg("targetWidth") = RandomResizeWithBBoxOp::kDefTargetWidth);
|
||||
}));
|
||||
PYBIND_REGISTER(UniformAugOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<UniformAugOp, TensorOp, std::shared_ptr<UniformAugOp>>(
|
||||
*m, "UniformAugOp", "Tensor operation to apply random augmentation(s).")
|
||||
.def(py::init<std::vector<std::shared_ptr<TensorOp>>, int32_t>(), py::arg("transforms"),
|
||||
py::arg("NumOps") = UniformAugOp::kDefNumOps);
|
||||
}));
|
||||
PYBIND_REGISTER(BoundingBoxAugmentOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<BoundingBoxAugmentOp, TensorOp, std::shared_ptr<BoundingBoxAugmentOp>>(
|
||||
*m, "BoundingBoxAugmentOp",
|
||||
"Tensor operation to apply a transformation on a random choice of bounding boxes.")
|
||||
.def(py::init<std::shared_ptr<TensorOp>, float>(), py::arg("transform"),
|
||||
py::arg("ratio") = BoundingBoxAugmentOp::kDefRatio);
|
||||
}));
|
||||
PYBIND_REGISTER(ResizeBilinearOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ResizeBilinearOp, TensorOp, std::shared_ptr<ResizeBilinearOp>>(
|
||||
*m, "ResizeBilinearOp",
|
||||
"Tensor operation to resize an image using "
|
||||
"Bilinear mode. Takes height and width.")
|
||||
.def(py::init<int32_t, int32_t>(), py::arg("targetHeight"),
|
||||
py::arg("targetWidth") = ResizeBilinearOp::kDefWidth);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(DecodeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<DecodeOp, TensorOp, std::shared_ptr<DecodeOp>>(
|
||||
*m, "DecodeOp", "Tensor operation to decode a jpg image")
|
||||
.def(py::init<>())
|
||||
.def(py::init<bool>(), py::arg("rgb_format") = DecodeOp::kDefRgbFormat);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomHorizontalFlipOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomHorizontalFlipOp, TensorOp, std::shared_ptr<RandomHorizontalFlipOp>>(
|
||||
*m, "RandomHorizontalFlipOp", "Tensor operation to randomly flip an image horizontally.")
|
||||
.def(py::init<float>(), py::arg("probability") = RandomHorizontalFlipOp::kDefProbability);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
RandomHorizontalFlipWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomHorizontalFlipWithBBoxOp, TensorOp, std::shared_ptr<RandomHorizontalFlipWithBBoxOp>>(
|
||||
*m, "RandomHorizontalFlipWithBBoxOp",
|
||||
"Tensor operation to randomly flip an image horizontally, while flipping bounding boxes.")
|
||||
.def(py::init<float>(), py::arg("probability") = RandomHorizontalFlipWithBBoxOp::kDefProbability);
|
||||
}));
|
||||
PYBIND_REGISTER(RandomVerticalFlipOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomVerticalFlipOp, TensorOp, std::shared_ptr<RandomVerticalFlipOp>>(
|
||||
*m, "RandomVerticalFlipOp", "Tensor operation to randomly flip an image vertically.")
|
||||
.def(py::init<float>(), py::arg("probability") = RandomVerticalFlipOp::kDefProbability);
|
||||
}));
|
||||
PYBIND_REGISTER(RandomVerticalFlipWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)
|
||||
py::class_<RandomVerticalFlipWithBBoxOp, TensorOp, std::shared_ptr<RandomVerticalFlipWithBBoxOp>>(
|
||||
*m, "RandomVerticalFlipWithBBoxOp",
|
||||
"Tensor operation to randomly flip an image vertically"
|
||||
" and adjust bounding boxes.")
|
||||
.def(py::init<float>(), py::arg("probability") = RandomVerticalFlipWithBBoxOp::kDefProbability);
|
||||
}));
|
||||
PYBIND_REGISTER(
|
||||
RandomCropOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomCropOp, TensorOp, std::shared_ptr<RandomCropOp>>(*m, "RandomCropOp",
|
||||
"Gives random crop of specified size "
|
||||
"Takes crop size")
|
||||
.def(
|
||||
py::init<int32_t, int32_t, int32_t, int32_t, int32_t, int32_t, BorderType, bool, uint8_t, uint8_t, uint8_t>(),
|
||||
py::arg("cropHeight"), py::arg("cropWidth"), py::arg("padTop") = RandomCropOp::kDefPadTop,
|
||||
py::arg("padBottom") = RandomCropOp::kDefPadBottom, py::arg("padLeft") = RandomCropOp::kDefPadLeft,
|
||||
py::arg("padRight") = RandomCropOp::kDefPadRight, py::arg("borderType") = RandomCropOp::kDefBorderType,
|
||||
py::arg("padIfNeeded") = RandomCropOp::kDefPadIfNeeded, py::arg("fillR") = RandomCropOp::kDefFillR,
|
||||
py::arg("fillG") = RandomCropOp::kDefFillG, py::arg("fillB") = RandomCropOp::kDefFillB);
|
||||
}));
|
||||
PYBIND_REGISTER(
|
||||
HwcToChwOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<HwcToChwOp, TensorOp, std::shared_ptr<HwcToChwOp>>(*m, "ChannelSwapOp").def(py::init<>());
|
||||
}));
|
||||
PYBIND_REGISTER(
|
||||
RandomCropWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomCropWithBBoxOp, TensorOp, std::shared_ptr<RandomCropWithBBoxOp>>(
|
||||
*m, "RandomCropWithBBoxOp",
|
||||
"Gives random crop of given "
|
||||
"size + adjusts bboxes "
|
||||
"Takes crop size")
|
||||
.def(
|
||||
py::init<int32_t, int32_t, int32_t, int32_t, int32_t, int32_t, BorderType, bool, uint8_t, uint8_t, uint8_t>(),
|
||||
py::arg("cropHeight"), py::arg("cropWidth"), py::arg("padTop") = RandomCropWithBBoxOp::kDefPadTop,
|
||||
py::arg("padBottom") = RandomCropWithBBoxOp::kDefPadBottom,
|
||||
py::arg("padLeft") = RandomCropWithBBoxOp::kDefPadLeft,
|
||||
py::arg("padRight") = RandomCropWithBBoxOp::kDefPadRight,
|
||||
py::arg("borderType") = RandomCropWithBBoxOp::kDefBorderType,
|
||||
py::arg("padIfNeeded") = RandomCropWithBBoxOp::kDefPadIfNeeded,
|
||||
py::arg("fillR") = RandomCropWithBBoxOp::kDefFillR, py::arg("fillG") = RandomCropWithBBoxOp::kDefFillG,
|
||||
py::arg("fillB") = RandomCropWithBBoxOp::kDefFillB);
|
||||
}));
|
||||
PYBIND_REGISTER(CutOutOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<CutOutOp, TensorOp, std::shared_ptr<CutOutOp>>(
|
||||
*m, "CutOutOp",
|
||||
"Tensor operation to randomly erase a portion of the image. Takes height and width.")
|
||||
.def(py::init<int32_t, int32_t, int32_t, bool, uint8_t, uint8_t, uint8_t>(), py::arg("boxHeight"),
|
||||
py::arg("boxWidth"), py::arg("numPatches"), py::arg("randomColor") = CutOutOp::kDefRandomColor,
|
||||
py::arg("fillR") = CutOutOp::kDefFillR, py::arg("fillG") = CutOutOp::kDefFillG,
|
||||
py::arg("fillB") = CutOutOp::kDefFillB);
|
||||
}));
|
||||
PYBIND_REGISTER(PadOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<PadOp, TensorOp, std::shared_ptr<PadOp>>(
|
||||
*m, "PadOp",
|
||||
"Pads image with specified color, default black, "
|
||||
"Takes amount to pad for top, bottom, left, right of image, boarder type and color")
|
||||
.def(py::init<int32_t, int32_t, int32_t, int32_t, BorderType, uint8_t, uint8_t, uint8_t>(),
|
||||
py::arg("padTop"), py::arg("padBottom"), py::arg("padLeft"), py::arg("padRight"),
|
||||
py::arg("borderTypes") = PadOp::kDefBorderType, py::arg("fillR") = PadOp::kDefFillR,
|
||||
py::arg("fillG") = PadOp::kDefFillG, py::arg("fillB") = PadOp::kDefFillB);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomCropDecodeResizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomCropDecodeResizeOp, TensorOp, std::shared_ptr<RandomCropDecodeResizeOp>>(
|
||||
*m, "RandomCropDecodeResizeOp", "equivalent to RandomCropAndResize but crops before decoding")
|
||||
.def(py::init<int32_t, int32_t, float, float, float, float, InterpolationMode, int32_t>(),
|
||||
py::arg("targetHeight"), py::arg("targetWidth"),
|
||||
py::arg("scaleLb") = RandomCropDecodeResizeOp::kDefScaleLb,
|
||||
py::arg("scaleUb") = RandomCropDecodeResizeOp::kDefScaleUb,
|
||||
py::arg("aspectLb") = RandomCropDecodeResizeOp::kDefAspectLb,
|
||||
py::arg("aspectUb") = RandomCropDecodeResizeOp::kDefAspectUb,
|
||||
py::arg("interpolation") = RandomCropDecodeResizeOp::kDefInterpolation,
|
||||
py::arg("maxIter") = RandomCropDecodeResizeOp::kDefMaxIter);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
RandomResizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomResizeOp, TensorOp, std::shared_ptr<RandomResizeOp>>(
|
||||
*m, "RandomResizeOp",
|
||||
"Tensor operation to resize an image using a randomly selected interpolation. Takes height and width.")
|
||||
.def(py::init<int32_t, int32_t>(), py::arg("targetHeight"),
|
||||
py::arg("targetWidth") = RandomResizeOp::kDefTargetWidth);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomColorAdjustOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomColorAdjustOp, TensorOp, std::shared_ptr<RandomColorAdjustOp>>(
|
||||
*m, "RandomColorAdjustOp",
|
||||
"Tensor operation to adjust an image's color randomly."
|
||||
"Takes range for brightness, contrast, saturation, hue and")
|
||||
.def(py::init<float, float, float, float, float, float, float, float>(),
|
||||
py::arg("bright_factor_start"), py::arg("bright_factor_end"), py::arg("contrast_factor_start"),
|
||||
py::arg("contrast_factor_end"), py::arg("saturation_factor_start"),
|
||||
py::arg("saturation_factor_end"), py::arg("hue_factor_start"), py::arg("hue_factor_end"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomCropAndResizeWithBBoxOp, 1, ([](const py::module *m) {
|
||||
(void)
|
||||
py::class_<RandomCropAndResizeWithBBoxOp, TensorOp, std::shared_ptr<RandomCropAndResizeWithBBoxOp>>(
|
||||
*m, "RandomCropAndResizeWithBBoxOp",
|
||||
"Tensor operation to randomly crop an image (with BBoxes) and resize to a given size."
|
||||
"Takes output height and width and"
|
||||
"optional parameters for lower and upper bound for aspect ratio (h/w) and scale,"
|
||||
"interpolation mode, and max attempts to crop")
|
||||
.def(py::init<int32_t, int32_t, float, float, float, float, InterpolationMode, int32_t>(),
|
||||
py::arg("targetHeight"), py::arg("targetWidth"),
|
||||
py::arg("scaleLb") = RandomCropAndResizeWithBBoxOp::kDefScaleLb,
|
||||
py::arg("scaleUb") = RandomCropAndResizeWithBBoxOp::kDefScaleUb,
|
||||
py::arg("aspectLb") = RandomCropAndResizeWithBBoxOp::kDefAspectLb,
|
||||
py::arg("aspectUb") = RandomCropAndResizeWithBBoxOp::kDefAspectUb,
|
||||
py::arg("interpolation") = RandomCropAndResizeWithBBoxOp::kDefInterpolation,
|
||||
py::arg("maxIter") = RandomCropAndResizeWithBBoxOp::kDefMaxIter);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomCropAndResizeOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomCropAndResizeOp, TensorOp, std::shared_ptr<RandomCropAndResizeOp>>(
|
||||
*m, "RandomCropAndResizeOp",
|
||||
"Tensor operation to randomly crop an image and resize to a given size."
|
||||
"Takes output height and width and"
|
||||
"optional parameters for lower and upper bound for aspect ratio (h/w) and scale,"
|
||||
"interpolation mode, and max attempts to crop")
|
||||
.def(py::init<int32_t, int32_t, float, float, float, float, InterpolationMode, int32_t>(),
|
||||
py::arg("targetHeight"), py::arg("targetWidth"),
|
||||
py::arg("scaleLb") = RandomCropAndResizeOp::kDefScaleLb,
|
||||
py::arg("scaleUb") = RandomCropAndResizeOp::kDefScaleUb,
|
||||
py::arg("aspectLb") = RandomCropAndResizeOp::kDefAspectLb,
|
||||
py::arg("aspectUb") = RandomCropAndResizeOp::kDefAspectUb,
|
||||
py::arg("interpolation") = RandomCropAndResizeOp::kDefInterpolation,
|
||||
py::arg("maxIter") = RandomCropAndResizeOp::kDefMaxIter);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomRotationOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomRotationOp, TensorOp, std::shared_ptr<RandomRotationOp>>(
|
||||
*m, "RandomRotationOp",
|
||||
"Tensor operation to apply RandomRotation."
|
||||
"Takes a range for degrees and "
|
||||
"optional parameters for rotation center and image expand")
|
||||
.def(
|
||||
py::init<float, float, float, float, InterpolationMode, bool, uint8_t, uint8_t, uint8_t>(),
|
||||
py::arg("startDegree"), py::arg("endDegree"), py::arg("centerX") = RandomRotationOp::kDefCenterX,
|
||||
py::arg("centerY") = RandomRotationOp::kDefCenterY,
|
||||
py::arg("interpolation") = RandomRotationOp::kDefInterpolation,
|
||||
py::arg("expand") = RandomRotationOp::kDefExpand, py::arg("fillR") = RandomRotationOp::kDefFillR,
|
||||
py::arg("fillG") = RandomRotationOp::kDefFillG, py::arg("fillB") = RandomRotationOp::kDefFillB);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RandomSelectSubpolicyOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RandomSelectSubpolicyOp, TensorOp, std::shared_ptr<RandomSelectSubpolicyOp>>(
|
||||
*m, "RandomSelectSubpolicyOp")
|
||||
.def(py::init([](const py::list &py_policy) {
|
||||
std::vector<Subpolicy> cpp_policy;
|
||||
for (auto &py_sub : py_policy) {
|
||||
cpp_policy.push_back({});
|
||||
for (auto handle : py_sub.cast<py::list>()) {
|
||||
py::tuple tp = handle.cast<py::tuple>();
|
||||
if (tp.is_none() || tp.size() != 2) {
|
||||
THROW_IF_ERROR(
|
||||
Status(StatusCode::kUnexpectedError, "Each tuple in subpolicy should be (op, prob)."));
|
||||
}
|
||||
std::shared_ptr<TensorOp> t_op;
|
||||
if (py::isinstance<TensorOp>(tp[0])) {
|
||||
t_op = (tp[0]).cast<std::shared_ptr<TensorOp>>();
|
||||
} else if (py::isinstance<py::function>(tp[0])) {
|
||||
t_op = std::make_shared<PyFuncOp>((tp[0]).cast<py::function>());
|
||||
} else {
|
||||
THROW_IF_ERROR(
|
||||
Status(StatusCode::kUnexpectedError, "op is neither a tensorOp nor a pyfunc."));
|
||||
}
|
||||
double prob = (tp[1]).cast<py::float_>();
|
||||
if (prob < 0 || prob > 1) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be with [0,1]."));
|
||||
}
|
||||
cpp_policy.back().emplace_back(std::make_pair(t_op, prob));
|
||||
}
|
||||
}
|
||||
return std::make_shared<RandomSelectSubpolicyOp>(cpp_policy);
|
||||
}));
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
#include "minddata/dataset/text/vocab.h"
|
||||
#include "minddata/dataset/text/sentence_piece_vocab.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(Vocab, 0, ([](const py::module *m) {
|
||||
(void)py::class_<Vocab, std::shared_ptr<Vocab>>(*m, "Vocab")
|
||||
.def(py::init<>())
|
||||
.def_static("from_list",
|
||||
[](const py::list &words, const py::list &special_tokens, bool special_first) {
|
||||
std::shared_ptr<Vocab> v;
|
||||
THROW_IF_ERROR(Vocab::BuildFromPyList(words, special_tokens, special_first, &v));
|
||||
return v;
|
||||
})
|
||||
.def_static(
|
||||
"from_file",
|
||||
[](const std::string &path, const std::string &dlm, int32_t vocab_size,
|
||||
const py::list &special_tokens, bool special_first) {
|
||||
std::shared_ptr<Vocab> v;
|
||||
THROW_IF_ERROR(Vocab::BuildFromFile(path, dlm, vocab_size, special_tokens, special_first, &v));
|
||||
return v;
|
||||
})
|
||||
.def_static("from_dict", [](const py::dict &words) {
|
||||
std::shared_ptr<Vocab> v;
|
||||
THROW_IF_ERROR(Vocab::BuildFromPyDict(words, &v));
|
||||
return v;
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SentencePieceVocab, 0, ([](const py::module *m) {
|
||||
(void)py::class_<SentencePieceVocab, std::shared_ptr<SentencePieceVocab>>(*m, "SentencePieceVocab")
|
||||
.def(py::init<>())
|
||||
.def_static("from_file",
|
||||
[](const py::list &paths, const int vocab_size, const float character_coverage,
|
||||
const SentencePieceModel model_type, const py::dict ¶ms) {
|
||||
std::shared_ptr<SentencePieceVocab> v;
|
||||
std::vector<std::string> path_list;
|
||||
for (auto path : paths) {
|
||||
path_list.emplace_back(py::str(path));
|
||||
}
|
||||
std::unordered_map<std::string, std::string> param_map;
|
||||
for (auto param : params) {
|
||||
std::string key = py::reinterpret_borrow<py::str>(param.first);
|
||||
if (key == "input" || key == "vocab_size" || key == "model_prefix" ||
|
||||
key == "character_coverage" || key == "model_type") {
|
||||
continue;
|
||||
}
|
||||
param_map[key] = py::reinterpret_borrow<py::str>(param.second);
|
||||
}
|
||||
THROW_IF_ERROR(SentencePieceVocab::BuildFromFile(
|
||||
path_list, vocab_size, character_coverage, model_type, param_map, &v));
|
||||
return v;
|
||||
})
|
||||
.def_static("save_model", [](const std::shared_ptr<SentencePieceVocab> *vocab, std::string path,
|
||||
std::string filename) {
|
||||
THROW_IF_ERROR(SentencePieceVocab::SaveModel(vocab, path, filename));
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SentencePieceModel, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<SentencePieceModel>(*m, "SentencePieceModel", py::arithmetic())
|
||||
.value("DE_SENTENCE_PIECE_UNIGRAM", SentencePieceModel::kUnigram)
|
||||
.value("DE_SENTENCE_PIECE_BPE", SentencePieceModel::kBpe)
|
||||
.value("DE_SENTENCE_PIECE_CHAR", SentencePieceModel::kChar)
|
||||
.value("DE_SENTENCE_PIECE_WORD", SentencePieceModel::kWord)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,244 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
|
||||
#include "minddata/dataset/text/kernels/jieba_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/lookup_op.h"
|
||||
#include "minddata/dataset/text/kernels/ngram_op.h"
|
||||
#include "minddata/dataset/text/kernels/sliding_window_op.h"
|
||||
#include "minddata/dataset/text/kernels/to_number_op.h"
|
||||
#include "minddata/dataset/text/kernels/unicode_char_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/wordpiece_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/sentence_piece_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/truncate_sequence_pair_op.h"
|
||||
|
||||
#ifdef ENABLE_ICU4C
|
||||
#include "minddata/dataset/text/kernels/basic_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/bert_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/case_fold_op.h"
|
||||
#include "minddata/dataset/text/kernels/normalize_utf8_op.h"
|
||||
#include "minddata/dataset/text/kernels/regex_replace_op.h"
|
||||
#include "minddata/dataset/text/kernels/regex_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/unicode_script_tokenizer_op.h"
|
||||
#include "minddata/dataset/text/kernels/whitespace_tokenizer_op.h"
|
||||
#endif
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
#ifdef ENABLE_ICU4C
|
||||
|
||||
PYBIND_REGISTER(BasicTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<BasicTokenizerOp, TensorOp, std::shared_ptr<BasicTokenizerOp>>(
|
||||
*m, "BasicTokenizerOp", "Tokenize a scalar tensor of UTF-8 string by specific rules.")
|
||||
.def(py::init<const bool &, const bool &, const NormalizeForm &, const bool &, const bool &>(),
|
||||
py::arg("lower_case") = BasicTokenizerOp::kDefLowerCase,
|
||||
py::arg("keep_whitespace") = BasicTokenizerOp::kDefKeepWhitespace,
|
||||
py::arg("normalization_form") = BasicTokenizerOp::kDefNormalizationForm,
|
||||
py::arg("preserve_unused_token") = BasicTokenizerOp::kDefPreserveUnusedToken,
|
||||
py::arg("with_offsets") = BasicTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(WhitespaceTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<WhitespaceTokenizerOp, TensorOp, std::shared_ptr<WhitespaceTokenizerOp>>(
|
||||
*m, "WhitespaceTokenizerOp", "Tokenize a scalar tensor of UTF-8 string on ICU defined whitespaces.")
|
||||
.def(py::init<const bool &>(), py::arg(" with_offsets ") = WhitespaceTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(UnicodeScriptTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<UnicodeScriptTokenizerOp, TensorOp, std::shared_ptr<UnicodeScriptTokenizerOp>>(
|
||||
*m, "UnicodeScriptTokenizerOp",
|
||||
"Tokenize a scalar tensor of UTF-8 string on Unicode script boundaries.")
|
||||
.def(py::init<>())
|
||||
.def(py::init<const bool &, const bool &>(),
|
||||
py::arg("keep_whitespace") = UnicodeScriptTokenizerOp::kDefKeepWhitespace,
|
||||
py::arg("with_offsets") = UnicodeScriptTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(CaseFoldOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<CaseFoldOp, TensorOp, std::shared_ptr<CaseFoldOp>>(
|
||||
*m, "CaseFoldOp", "Apply case fold operation on utf-8 string tensor")
|
||||
.def(py::init<>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(NormalizeUTF8Op, 1, ([](const py::module *m) {
|
||||
(void)py::class_<NormalizeUTF8Op, TensorOp, std::shared_ptr<NormalizeUTF8Op>>(
|
||||
*m, "NormalizeUTF8Op", "Apply normalize operation on utf-8 string tensor.")
|
||||
.def(py::init<>())
|
||||
.def(py::init<NormalizeForm>(), py::arg("normalize_form") = NormalizeUTF8Op::kDefNormalizeForm);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RegexReplaceOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RegexReplaceOp, TensorOp, std::shared_ptr<RegexReplaceOp>>(
|
||||
*m, "RegexReplaceOp",
|
||||
"Replace utf-8 string tensor with 'replace' according to regular expression 'pattern'.")
|
||||
.def(py::init<const std::string &, const std::string &, bool>(), py::arg("pattern"),
|
||||
py::arg("replace"), py::arg("replace_all"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(RegexTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<RegexTokenizerOp, TensorOp, std::shared_ptr<RegexTokenizerOp>>(
|
||||
*m, "RegexTokenizerOp", "Tokenize a scalar tensor of UTF-8 string by regex expression pattern.")
|
||||
.def(py::init<const std::string &, const std::string &, const bool &>(), py::arg("delim_pattern"),
|
||||
py::arg("keep_delim_pattern"), py::arg("with_offsets") = RegexTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
PYBIND_REGISTER(BertTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<BertTokenizerOp, TensorOp, std::shared_ptr<BertTokenizerOp>>(
|
||||
*m, "BertTokenizerOp", "Tokenizer used for Bert text process.")
|
||||
.def(py::init<const std::shared_ptr<Vocab> &, const std::string &, const int &, const std::string &,
|
||||
const bool &, const bool &, const NormalizeForm &, const bool &, const bool &>(),
|
||||
py::arg("vocab"),
|
||||
py::arg("suffix_indicator") = std::string(WordpieceTokenizerOp::kDefSuffixIndicator),
|
||||
py::arg("max_bytes_per_token") = WordpieceTokenizerOp::kDefMaxBytesPerToken,
|
||||
py::arg("unknown_token") = std::string(WordpieceTokenizerOp::kDefUnknownToken),
|
||||
py::arg("lower_case") = BasicTokenizerOp::kDefLowerCase,
|
||||
py::arg("keep_whitespace") = BasicTokenizerOp::kDefKeepWhitespace,
|
||||
py::arg("normalization_form") = BasicTokenizerOp::kDefNormalizationForm,
|
||||
py::arg("preserve_unused_token") = BasicTokenizerOp::kDefPreserveUnusedToken,
|
||||
py::arg("with_offsets") = WordpieceTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(NormalizeForm, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<NormalizeForm>(*m, "NormalizeForm", py::arithmetic())
|
||||
.value("DE_NORMALIZE_NONE", NormalizeForm::kNone)
|
||||
.value("DE_NORMALIZE_NFC", NormalizeForm::kNfc)
|
||||
.value("DE_NORMALIZE_NFKC", NormalizeForm::kNfkc)
|
||||
.value("DE_NORMALIZE_NFD", NormalizeForm::kNfd)
|
||||
.value("DE_NORMALIZE_NFKD", NormalizeForm::kNfkd)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
#endif
|
||||
|
||||
PYBIND_REGISTER(JiebaTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<JiebaTokenizerOp, TensorOp, std::shared_ptr<JiebaTokenizerOp>>(
|
||||
*m, "JiebaTokenizerOp", "")
|
||||
.def(py::init<const std::string &, const std::string &, const JiebaMode &, const bool &>(),
|
||||
py::arg("hmm_path"), py::arg("mp_path"), py::arg("mode") = JiebaMode::kMix,
|
||||
py::arg("with_offsets") = JiebaTokenizerOp::kDefWithOffsets)
|
||||
.def("add_word", [](JiebaTokenizerOp &self, const std::string word, int freq) {
|
||||
THROW_IF_ERROR(self.AddWord(word, freq));
|
||||
});
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(UnicodeCharTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<UnicodeCharTokenizerOp, TensorOp, std::shared_ptr<UnicodeCharTokenizerOp>>(
|
||||
*m, "UnicodeCharTokenizerOp", "Tokenize a scalar tensor of UTF-8 string to Unicode characters.")
|
||||
.def(py::init<const bool &>(), py::arg("with_offsets") = UnicodeCharTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(LookupOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<LookupOp, TensorOp, std::shared_ptr<LookupOp>>(
|
||||
*m, "LookupOp", "Tensor operation to LookUp each word.")
|
||||
.def(py::init([](std::shared_ptr<Vocab> vocab, const py::object &py_word) {
|
||||
if (vocab == nullptr) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "vocab object type is incorrect or null."));
|
||||
}
|
||||
if (py_word.is_none()) {
|
||||
return std::make_shared<LookupOp>(vocab, Vocab::kNoTokenExists);
|
||||
}
|
||||
std::string word = py::reinterpret_borrow<py::str>(py_word);
|
||||
WordIdType default_id = vocab->Lookup(word);
|
||||
if (default_id == Vocab::kNoTokenExists) {
|
||||
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError,
|
||||
"default unknown token: " + word + " doesn't exist in vocab."));
|
||||
}
|
||||
return std::make_shared<LookupOp>(vocab, default_id);
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(NgramOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<NgramOp, TensorOp, std::shared_ptr<NgramOp>>(*m, "NgramOp",
|
||||
"TensorOp performs ngram mapping.")
|
||||
.def(py::init<const std::vector<int32_t> &, int32_t, int32_t, const std::string &,
|
||||
const std::string &, const std::string &>(),
|
||||
py::arg("ngrams"), py::arg("l_pad_len"), py::arg("r_pad_len"), py::arg("l_pad_token"),
|
||||
py::arg("r_pad_token"), py::arg("separator"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
WordpieceTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<WordpieceTokenizerOp, TensorOp, std::shared_ptr<WordpieceTokenizerOp>>(
|
||||
*m, "WordpieceTokenizerOp", "Tokenize scalar token or 1-D tokens to subword tokens.")
|
||||
.def(
|
||||
py::init<const std::shared_ptr<Vocab> &, const std::string &, const int &, const std::string &, const bool &>(),
|
||||
py::arg("vocab"), py::arg("suffix_indicator") = std::string(WordpieceTokenizerOp::kDefSuffixIndicator),
|
||||
py::arg("max_bytes_per_token") = WordpieceTokenizerOp::kDefMaxBytesPerToken,
|
||||
py::arg("unknown_token") = std::string(WordpieceTokenizerOp::kDefUnknownToken),
|
||||
py::arg("with_offsets") = WordpieceTokenizerOp::kDefWithOffsets);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SlidingWindowOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<SlidingWindowOp, TensorOp, std::shared_ptr<SlidingWindowOp>>(
|
||||
*m, "SlidingWindowOp", "TensorOp to apply sliding window to a 1-D Tensor.")
|
||||
.def(py::init<uint32_t, int32_t>(), py::arg("width"), py::arg("axis"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
SentencePieceTokenizerOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<SentencePieceTokenizerOp, TensorOp, std::shared_ptr<SentencePieceTokenizerOp>>(
|
||||
*m, "SentencePieceTokenizerOp", "Tokenize scalar token or 1-D tokens to tokens by sentence piece.")
|
||||
.def(
|
||||
py::init<std::shared_ptr<SentencePieceVocab> &, const SPieceTokenizerLoadType, const SPieceTokenizerOutType>(),
|
||||
py::arg("vocab"), py::arg("load_type") = SPieceTokenizerLoadType::kModel,
|
||||
py::arg("out_type") = SPieceTokenizerOutType::kString)
|
||||
.def(py::init<const std::string &, const std::string &, const SPieceTokenizerLoadType,
|
||||
const SPieceTokenizerOutType>(),
|
||||
py::arg("model_path"), py::arg("model_filename"), py::arg("load_type") = SPieceTokenizerLoadType::kFile,
|
||||
py::arg("out_type") = SPieceTokenizerOutType::kString);
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ToNumberOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<ToNumberOp, TensorOp, std::shared_ptr<ToNumberOp>>(
|
||||
*m, "ToNumberOp", "TensorOp to convert strings to numbers.")
|
||||
.def(py::init<DataType>(), py::arg("data_type"))
|
||||
.def(py::init<std::string>(), py::arg("data_type"));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(TruncateSequencePairOp, 1, ([](const py::module *m) {
|
||||
(void)py::class_<TruncateSequencePairOp, TensorOp, std::shared_ptr<TruncateSequencePairOp>>(
|
||||
*m, "TruncateSequencePairOp", "Tensor operation to truncate two tensors to a max_length")
|
||||
.def(py::init<int64_t>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(JiebaMode, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<JiebaMode>(*m, "JiebaMode", py::arithmetic())
|
||||
.value("DE_JIEBA_MIX", JiebaMode::kMix)
|
||||
.value("DE_JIEBA_MP", JiebaMode::kMp)
|
||||
.value("DE_JIEBA_HMM", JiebaMode::kHmm)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SPieceTokenizerOutType, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<SPieceTokenizerOutType>(*m, "SPieceTokenizerOutType", py::arithmetic())
|
||||
.value("DE_SPIECE_TOKENIZER_OUTTYPE_KString", SPieceTokenizerOutType::kString)
|
||||
.value("DE_SPIECE_TOKENIZER_OUTTYPE_KINT", SPieceTokenizerOutType::kInt)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(SPieceTokenizerLoadType, 0, ([](const py::module *m) {
|
||||
(void)py::enum_<SPieceTokenizerLoadType>(*m, "SPieceTokenizerLoadType", py::arithmetic())
|
||||
.value("DE_SPIECE_TOKENIZER_LOAD_KFILE", SPieceTokenizerLoadType::kFile)
|
||||
.value("DE_SPIECE_TOKENIZER_LOAD_KMODEL", SPieceTokenizerLoadType::kModel)
|
||||
.export_values();
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
/**
|
||||
* 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 "pybind11/pybind11.h"
|
||||
#include "pybind11/stl_bind.h"
|
||||
|
||||
#include "minddata/dataset/api/python/pybind_register.h"
|
||||
|
||||
#include "minddata/dataset/util/random.h"
|
||||
#include "minddata/mindrecord/include/shard_distributed_sample.h"
|
||||
#include "minddata/mindrecord/include/shard_operator.h"
|
||||
#include "minddata/mindrecord/include/shard_pk_sample.h"
|
||||
#include "minddata/mindrecord/include/shard_sample.h"
|
||||
#include "minddata/mindrecord/include/shard_sequential_sample.h"
|
||||
#include "minddata/mindrecord/include/shard_shuffle.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PYBIND_REGISTER(ShardOperator, 0, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardOperator>>(
|
||||
*m, "ShardOperator")
|
||||
.def("add_child",
|
||||
[](std::shared_ptr<mindrecord::ShardOperator> self,
|
||||
std::shared_ptr<mindrecord::ShardOperator> child) { self->SetChildOp(child); });
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ShardDistributedSample, 1, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardDistributedSample, mindrecord::ShardSample,
|
||||
std::shared_ptr<mindrecord::ShardDistributedSample>>(*m,
|
||||
"MindrecordDistributedSampler")
|
||||
.def(py::init<int64_t, int64_t, bool, uint32_t, int64_t>());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
ShardPkSample, 1, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardPkSample, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardPkSample>>(
|
||||
*m, "MindrecordPkSampler")
|
||||
.def(py::init([](int64_t kVal, std::string kColumn, bool shuffle) {
|
||||
if (shuffle == true) {
|
||||
return std::make_shared<mindrecord::ShardPkSample>(kColumn, kVal, std::numeric_limits<int64_t>::max(),
|
||||
GetSeed());
|
||||
} else {
|
||||
return std::make_shared<mindrecord::ShardPkSample>(kColumn, kVal);
|
||||
}
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
ShardSample, 0, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardSample, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardSample>>(
|
||||
*m, "MindrecordSubsetRandomSampler")
|
||||
.def(py::init<std::vector<int64_t>, uint32_t>(), py::arg("indices"), py::arg("seed") = GetSeed());
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(ShardSequentialSample, 0, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardSequentialSample, mindrecord::ShardSample,
|
||||
std::shared_ptr<mindrecord::ShardSequentialSample>>(*m,
|
||||
"MindrecordSequentialSampler")
|
||||
.def(py::init([](int num_samples, int start_index) {
|
||||
return std::make_shared<mindrecord::ShardSequentialSample>(num_samples, start_index);
|
||||
}));
|
||||
}));
|
||||
|
||||
PYBIND_REGISTER(
|
||||
ShardShuffle, 1, ([](const py::module *m) {
|
||||
(void)py::class_<mindrecord::ShardShuffle, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardShuffle>>(
|
||||
*m, "MindrecordRandomSampler")
|
||||
.def(py::init([](int64_t num_samples, bool replacement, bool reshuffle_each_epoch) {
|
||||
return std::make_shared<mindrecord::ShardShuffle>(GetSeed(), num_samples, replacement, reshuffle_each_epoch);
|
||||
}));
|
||||
}));
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "minddata/dataset/api/de_pipeline.h"
|
||||
#include "minddata/dataset/api/python/de_pipeline.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <set>
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
/**
|
||||
* 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 "minddata/dataset/api/python/pybind_register.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
|
||||
PybindDefinedFunctionRegister &PybindDefinedFunctionRegister::GetSingleton() {
|
||||
static PybindDefinedFunctionRegister instance;
|
||||
return instance;
|
||||
}
|
||||
|
||||
// This is where we externalize the C logic as python modules
|
||||
PYBIND11_MODULE(_c_dataengine, m) {
|
||||
m.doc() = "pybind11 for _c_dataengine";
|
||||
|
||||
auto all_fns = mindspore::dataset::PybindDefinedFunctionRegister::AllFunctions();
|
||||
|
||||
for (auto &item : all_fns) {
|
||||
for (auto &func : item.second) {
|
||||
func.second(&m);
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
/**
|
||||
* Copyright 2019 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.
|
||||
*/
|
||||
|
||||
#ifndef API_PYBIND_API_H_
|
||||
#define API_PYBIND_API_H_
|
||||
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <memory>
|
||||
#include <functional>
|
||||
#include <utility>
|
||||
|
||||
#include "pybind11/pybind11.h"
|
||||
#include "pybind11/stl.h"
|
||||
|
||||
namespace py = pybind11;
|
||||
namespace mindspore {
|
||||
|
||||
namespace dataset {
|
||||
#define THROW_IF_ERROR(s) \
|
||||
do { \
|
||||
Status rc = std::move(s); \
|
||||
if (rc.IsError()) throw std::runtime_error(rc.ToString()); \
|
||||
} while (false)
|
||||
|
||||
using PybindDefineFunc = std::function<void(py::module *)>;
|
||||
|
||||
class PybindDefinedFunctionRegister {
|
||||
public:
|
||||
static void Register(const std::string &name, const uint8_t &priority, const PybindDefineFunc &fn) {
|
||||
return GetSingleton().RegisterFn(name, priority, fn);
|
||||
}
|
||||
|
||||
PybindDefinedFunctionRegister(const PybindDefinedFunctionRegister &) = delete;
|
||||
|
||||
PybindDefinedFunctionRegister &operator=(const PybindDefinedFunctionRegister &) = delete;
|
||||
|
||||
static std::map<uint8_t, std::map<std::string, PybindDefineFunc>> &AllFunctions() {
|
||||
return GetSingleton().module_fns_;
|
||||
}
|
||||
std::map<uint8_t, std::map<std::string, PybindDefineFunc>> module_fns_;
|
||||
|
||||
protected:
|
||||
PybindDefinedFunctionRegister() = default;
|
||||
|
||||
virtual ~PybindDefinedFunctionRegister() = default;
|
||||
|
||||
static PybindDefinedFunctionRegister &GetSingleton();
|
||||
|
||||
void RegisterFn(const std::string &name, const uint8_t &priority, const PybindDefineFunc &fn) {
|
||||
module_fns_[priority][name] = fn;
|
||||
}
|
||||
};
|
||||
|
||||
class PybindDefineRegisterer {
|
||||
public:
|
||||
PybindDefineRegisterer(const std::string &name, const uint8_t &priority, const PybindDefineFunc &fn) {
|
||||
PybindDefinedFunctionRegister::Register(name, priority, fn);
|
||||
}
|
||||
~PybindDefineRegisterer() = default;
|
||||
};
|
||||
|
||||
#ifdef ENABLE_PYTHON
|
||||
#define PYBIND_REGISTER(name, priority, define) PybindDefineRegisterer g_pybind_define_f_##name(#name, priority, define)
|
||||
#endif
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
#endif // API_PYBIND_API_H_
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -4,16 +4,16 @@ file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc"
|
|||
set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
|
||||
if (ENABLE_PYTHON)
|
||||
add_library(kernels OBJECT
|
||||
compose_op.cc
|
||||
random_apply_op.cc
|
||||
random_choice_op.cc
|
||||
data/compose_op.cc
|
||||
data/random_apply_op.cc
|
||||
data/random_choice_op.cc
|
||||
py_func_op.cc
|
||||
tensor_op.cc)
|
||||
target_include_directories(kernels PRIVATE ${pybind11_INCLUDE_DIRS})
|
||||
else()
|
||||
add_library(kernels OBJECT
|
||||
compose_op.cc
|
||||
random_apply_op.cc
|
||||
random_choice_op.cc
|
||||
data/compose_op.cc
|
||||
data/random_apply_op.cc
|
||||
data/random_choice_op.cc
|
||||
tensor_op.cc)
|
||||
endif()
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "minddata/dataset/kernels/compose_op.h"
|
||||
#include "minddata/dataset/kernels/data/compose_op.h"
|
||||
|
||||
#include <vector>
|
||||
|
||||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "minddata/dataset/kernels/random_apply_op.h"
|
||||
#include "minddata/dataset/kernels/data/random_apply_op.h"
|
||||
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
|
@ -24,7 +24,7 @@
|
|||
#include <vector>
|
||||
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/kernels/compose_op.h"
|
||||
#include "minddata/dataset/kernels/data/compose_op.h"
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/util/random.h"
|
||||
|
||||
|
|
@ -13,7 +13,7 @@
|
|||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
#include "minddata/dataset/kernels/random_choice_op.h"
|
||||
#include "minddata/dataset/kernels/data/random_choice_op.h"
|
||||
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
|
@ -25,7 +25,7 @@
|
|||
|
||||
#include "minddata/dataset/core/tensor.h"
|
||||
#include "minddata/dataset/kernels/tensor_op.h"
|
||||
#include "minddata/dataset/kernels/compose_op.h"
|
||||
#include "minddata/dataset/kernels/data/compose_op.h"
|
||||
#include "minddata/dataset/util/random.h"
|
||||
|
||||
namespace mindspore {
|
||||
|
|
@ -29,7 +29,6 @@ namespace mindspore {
|
|||
namespace dataset {
|
||||
class RandomVerticalFlipWithBBoxOp : public TensorOp {
|
||||
public:
|
||||
// Default values, also used by python_bindings.cc
|
||||
static const float kDefProbability;
|
||||
// Constructor for RandomVerticalFlipWithBBoxOp
|
||||
// @param probability: Probablity of Image flipping, 0.5 by default
|
||||
|
|
|
|||
Loading…
Reference in New Issue