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added MindData lite

This commit is contained in:
ervinzhang 2020-07-30 16:29:17 -04:00 committed by ervinzhang
parent 49c1eea1b2
commit 50dcb79bdf
21 changed files with 789 additions and 8 deletions
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10
.gitmodules vendored
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@ -24,3 +24,13 @@
[submodule "third_party/OpenCL-Headers"]
path = third_party/OpenCL-Headers
url = https://github.com/KhronosGroup/OpenCL-Headers.git
[submodule "third_party/opencv"]
path = third_party/opencv
url = https://github.com/opencv/opencv.git
[submodule "third_party/eigen"]
path = third_party/eigen
url = https://gitlab.com/libeigen/eigen.git
[submodule "third_party/libjpeg-turbo"]
path = third_party/libjpeg-turbo
url = https://github.com/libjpeg-turbo/libjpeg-turbo.git
ignore = dirty

46
build.sh
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@ -519,6 +519,50 @@ build_opencl() {
fi
}
build_opencv() {
cd ${BASEPATH}
if [[ "${INC_BUILD}" == "off" ]]; then
git submodule update --init --recursive third_party/opencv
cd ${BASEPATH}/third_party/opencv
rm -rf build && mkdir -p build && cd build && cmake ${CMAKE_MINDDATA_ARGS} -DBUILD_SHARED_LIBS=ON -DBUILD_ANDROID_PROJECTS=OFF \
-DBUILD_LIST=core,imgcodecs,imgproc -DBUILD_ZLIB=ON .. && make -j$THREAD_NUM
fi
}
build_jpeg_turbo() {
cd ${BASEPATH}
if [[ "${INC_BUILD}" == "off" ]]; then
git submodule update --init --recursive third_party/libjpeg-turbo
cd ${BASEPATH}/third_party/libjpeg-turbo
rm -rf build && mkdir -p build && cd build && cmake ${CMAKE_MINDDATA_ARGS} -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX="${BASEPATH}/third_party/libjpeg-turbo" .. && make -j$THREAD_NUM && make install
fi
}
build_eigen() {
cd ${BASEPATH}
git submodule update --init --recursive third_party/eigen
}
build_minddata_lite_deps()
{
echo "start build minddata lite project"
if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
CMAKE_MINDDATA_ARGS="-DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" -DANDROID_NATIVE_API_LEVEL="19" \
-DANDROID_NDK="${ANDROID_NDK}" -DANDROID_ABI="arm64-v8a" -DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \
-DANDROID_STL="c++_shared" -DCMAKE_BUILD_TYPE=${BUILD_TYPE}"
elif [[ "${LITE_PLATFORM}" == "arm32" ]]; then
CMAKE_MINDDATA_ARGS="-DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" -DANDROID_NATIVE_API_LEVEL="19" \
-DANDROID_NDK="${ANDROID_NDK}" -DANDROID_ABI="armeabi-v7a" -DANDROID_TOOLCHAIN_NAME="clang" \
-DANDROID_STL="c++_shared" -DCMAKE_BUILD_TYPE=${BUILD_TYPE}"
else
CMAKE_MINDDATA_ARGS="-DCMAKE_BUILD_TYPE=${BUILD_TYPE} "
fi
build_opencv
build_eigen
build_jpeg_turbo
}
build_lite()
{
echo "start build mindspore lite project"
@ -533,6 +577,8 @@ build_lite()
build_flatbuffer
build_gtest
build_minddata_lite_deps
cd "${BASEPATH}/mindspore/lite"
if [[ "${INC_BUILD}" == "off" ]]; then
rm -rf build

2
mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt
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@ -13,4 +13,6 @@ add_library(cpp-API OBJECT
iterator.cc
transforms.cc
samplers.cc
de_tensor.cc
execute.cc
)

188
mindspore/ccsrc/minddata/dataset/api/de_tensor.cc Normal file
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@ -0,0 +1,188 @@
#include "minddata/dataset/include/de_tensor.h"
#include "minddata/dataset/core/constants.h"
#include "minddata/dataset/core/data_type.h"
#include "mindspore/core/ir/dtype/type_id.h"
#include "utils/hashing.h"
#include "mindspore/lite/src/ir/tensor.h"
namespace mindspore {
namespace tensor {
dataset::DataType MSTypeToDEType(TypeId data_type) {
switch (data_type) {
case kNumberTypeBool:
return dataset::DataType(dataset::DataType::DE_BOOL);
case kNumberTypeInt8:
return dataset::DataType(dataset::DataType::DE_INT8);
case kNumberTypeUInt8:
return dataset::DataType(dataset::DataType::DE_UINT8);
case kNumberTypeInt16:
return dataset::DataType(dataset::DataType::DE_INT16);
case kNumberTypeUInt16:
return dataset::DataType(dataset::DataType::DE_UINT16);
case kNumberTypeInt32:
return dataset::DataType(dataset::DataType::DE_INT32);
case kNumberTypeUInt32:
return dataset::DataType(dataset::DataType::DE_UINT32);
case kNumberTypeInt64:
return dataset::DataType(dataset::DataType::DE_INT64);
case kNumberTypeUInt64:
return dataset::DataType(dataset::DataType::DE_UINT64);
case kNumberTypeFloat16:
return dataset::DataType(dataset::DataType::DE_FLOAT16);
case kNumberTypeFloat32:
return dataset::DataType(dataset::DataType::DE_FLOAT32);
case kNumberTypeFloat64:
return dataset::DataType(dataset::DataType::DE_FLOAT64);
default:
// maybe throw?
return dataset::DataType(dataset::DataType::DE_UNKNOWN);
}
}
TypeId DETypeToMSType(dataset::DataType data_type) {
switch (data_type.value()) {
case dataset::DataType::DE_BOOL:
return mindspore::TypeId::kNumberTypeBool;
case dataset::DataType::DE_INT8:
return mindspore::TypeId::kNumberTypeInt8;
case dataset::DataType::DE_UINT8:
return mindspore::TypeId::kNumberTypeUInt8;
case dataset::DataType::DE_INT16:
return mindspore::TypeId::kNumberTypeInt16;
case dataset::DataType::DE_UINT16:
return mindspore::TypeId::kNumberTypeUInt16;
case dataset::DataType::DE_INT32:
return mindspore::TypeId::kNumberTypeInt32;
case dataset::DataType::DE_UINT32:
return mindspore::TypeId::kNumberTypeUInt32;
case dataset::DataType::DE_INT64:
return mindspore::TypeId::kNumberTypeInt64;
case dataset::DataType::DE_UINT64:
return mindspore::TypeId::kNumberTypeUInt64;
case dataset::DataType::DE_FLOAT16:
return mindspore::TypeId::kNumberTypeFloat16;
case dataset::DataType::DE_FLOAT32:
return mindspore::TypeId::kNumberTypeFloat32;
case dataset::DataType::DE_FLOAT64:
return mindspore::TypeId::kNumberTypeFloat64;
default:
// maybe throw?
return kTypeUnknown;
}
}
MSTensor *DETensor::CreateTensor(TypeId data_type, const std::vector<int> &shape) {
return new DETensor(data_type, shape);
}
MSTensor *DETensor::CreateTensor(const std::string &path) {
std::shared_ptr<dataset::Tensor> t;
(void) dataset::Tensor::CreateFromFile(path, &t);
return new DETensor(std::move(t));
}
DETensor::DETensor(TypeId data_type, const std::vector<int> &shape) {
std::vector<dataset::dsize_t> t_shape;
t_shape.reserve(shape.size());
std::transform(shape.begin(), shape.end(),
std::back_inserter(t_shape),
[](int s) -> dataset::dsize_t {return static_cast<dataset::dsize_t>(s);});
dataset::Tensor::CreateEmpty(dataset::TensorShape(t_shape), MSTypeToDEType(data_type), &this->tensor_impl_);
}
DETensor::DETensor(std::shared_ptr<dataset::Tensor> tensor_ptr) { this->tensor_impl_ = std::move(tensor_ptr); }
MSTensor *DETensor::ConvertToLiteTensor() {
// static MSTensor::CreateTensor is only for the LiteTensor
MSTensor *tensor = MSTensor::CreateTensor(this->data_type(), this->shape());
MS_ASSERT(tensor->Size() == this->Size());
memcpy_s(tensor->MutableData(), tensor->Size(), this->MutableData(), this->Size());
return tensor;
}
std::shared_ptr<dataset::Tensor> DETensor::tensor() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
return this->tensor_impl_;
}
TypeId DETensor::data_type() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
return DETypeToMSType(this->tensor_impl_->type());
}
TypeId DETensor::set_data_type(TypeId data_type) {
MS_ASSERT(this->tensor_impl_ != nullptr);
if (data_type != this->data_type()) {
std::shared_ptr<dataset::Tensor> temp;
dataset::Tensor::CreateFromMemory(this->tensor_impl_->shape(), MSTypeToDEType(data_type), this->tensor_impl_->GetBuffer(), &temp);
this->tensor_impl_ = temp;
}
return data_type;
}
std::vector<int> DETensor::shape() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
std::vector<dataset::dsize_t> t_shape = this->tensor_impl_->shape().AsVector();
std::vector<int> shape;
shape.reserve(t_shape.size());
std::transform(t_shape.begin(), t_shape.end(),
std::back_inserter(shape),
[](dataset::dsize_t s) -> int {return static_cast<int>(s);});
return shape;
}
size_t DETensor::set_shape(const std::vector<int> &shape) {
MS_ASSERT(this->tensor_impl_ != nullptr);
std::vector<dataset::dsize_t> t_shape;
t_shape.reserve(shape.size());
std::transform(shape.begin(), shape.end(),
std::back_inserter(t_shape),
[](int s) -> dataset::dsize_t {return static_cast<dataset::dsize_t>(s);});
dataset::Status rc = this->tensor_impl_->Reshape(dataset::TensorShape(t_shape));
//TODO: what if t_shape has different size?
return shape.size();
}
int DETensor::DimensionSize(size_t index) const {
MS_ASSERT(this->tensor_impl_ != nullptr);
int dim_size = -1;
auto shape = this->shape();
if (index < shape.size()) {
dim_size = shape[index];
} else {
MS_LOG(ERROR) << "Dimension index is wrong: " << index;
}
return dim_size;
}
int DETensor::ElementsNum() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
return this->tensor_impl_->Size();
}
std::size_t DETensor::hash() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
auto shape = this->shape();
std::size_t hash_value = std::hash<int>{}(SizeToInt(this->data_type()));
hash_value = hash_combine(hash_value, std::hash<size_t>{}(shape.size()));
// hash all elements may costly, so only take at most 4 elements into account based on
// some experiments.
for (size_t i = 0; (i < shape.size()) && (i < 4); ++i) {
hash_value = hash_combine(hash_value, (std::hash<int>{}(shape[i])));
}
return hash_value;
}
size_t DETensor::Size() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
return this->tensor_impl_->SizeInBytes();
}
void *DETensor::MutableData() const {
MS_ASSERT(this->tensor_impl_ != nullptr);
// TODO: friend the DETensor?
return this->tensor_impl_->GetMutableBuffer();
}
} // namespace tensor
} // namespace mindspore

55
mindspore/ccsrc/minddata/dataset/api/execute.cc Normal file
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@ -0,0 +1,55 @@
/**
* 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/include/execute.h"
#include "minddata/dataset/include/de_tensor.h"
#include "minddata/dataset/include/tensor.h"
#include "minddata/dataset/kernels/tensor_op.h"
namespace mindspore {
namespace dataset {
namespace api {
Execute::Execute(const std::shared_ptr<TensorOperation> &op) : op_(std::move(op)) {}
std::shared_ptr<tensor::MSTensor> Execute::operator()(std::shared_ptr<tensor::MSTensor> input){
// Build the op
if (op_ == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation is not valid";
return nullptr;
}
std::shared_ptr<Tensor> de_input = std::dynamic_pointer_cast<tensor::DETensor>(input)->tensor();
if (de_input == nullptr) {
MS_LOG(ERROR) << "Input Tensor is not valid";
return nullptr;
}
std::shared_ptr<TensorOp> transform = op_->Build();
std::shared_ptr<Tensor> de_output;
Status rc = transform->Compute(de_input, &de_output);
if (rc.IsError()) {
// execution failed
MS_LOG(ERROR) << "Operation execution failed : " << rc.ToString();
return nullptr;
}
return std::shared_ptr<tensor::MSTensor>(new tensor::DETensor(std::move(de_output)));
}
} // namespace api
} // namespace dataset
} // namespace mindspore

3
mindspore/ccsrc/minddata/dataset/core/client.h
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@ -25,8 +25,11 @@
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/dataset_iterator.h"
#ifndef ENABLE_ANDROID
#include "minddata/dataset/engine/datasetops/source/mindrecord_op.h"
#include "minddata/dataset/engine/datasetops/source/tf_reader_op.h"
#endif
#ifdef ENABLE_PYTHON
#include "minddata/dataset/engine/datasetops/barrier_op.h"

4
mindspore/ccsrc/minddata/dataset/core/tensor.cc
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@ -213,6 +213,7 @@ Status Tensor::CreateFromNpArray(const py::array &arr, std::shared_ptr<Tensor> *
}
#endif
#ifndef ENABLE_ANDROID
Status Tensor::CreateFromByteList(const dataengine::BytesList &bytes_list, const TensorShape &shape, TensorPtr *out) {
const TensorAlloc *alloc = GlobalContext::Instance()->tensor_allocator();
*out = std::allocate_shared<Tensor>(*alloc, TensorShape({static_cast<dsize_t>(bytes_list.value_size())}),
@ -255,6 +256,7 @@ Status Tensor::CreateFromByteList(const dataengine::BytesList &bytes_list, const
(*out)->Reshape(shape);
return Status::OK();
}
#endif
Status Tensor::CreateFromFile(const std::string &path, std::shared_ptr<Tensor> *out) {
std::ifstream fs;
@ -269,6 +271,7 @@ Status Tensor::CreateFromFile(const std::string &path, std::shared_ptr<Tensor> *
return Status::OK();
}
#ifndef ENABLE_ANDROID
Status Tensor::CreateFromByteList(const dataengine::BytesList &bytes_list, const TensorShape &shape,
const DataType &type, dsize_t pad_size, TensorPtr *out) {
RETURN_IF_NOT_OK(Tensor::CreateEmpty(shape, type, out));
@ -298,6 +301,7 @@ Status Tensor::CreateFromByteList(const dataengine::BytesList &bytes_list, const
return Status::OK();
}
#endif
// Memcpy the given strided array's used part to consecutive memory
// Consider a 3-d array

9
mindspore/ccsrc/minddata/dataset/core/tensor.h
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@ -38,12 +38,18 @@
#include "minddata/dataset/core/data_type.h"
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/util/status.h"
#include "minddata/dataset/include/de_tensor.h"
#ifndef ENABLE_ANDROID
#include "proto/example.pb.h"
#endif
#ifdef ENABLE_PYTHON
namespace py = pybind11;
#endif
namespace mindspore {
namespace tensor {
class DETensor;
} // namespace tensor
namespace dataset {
class Tensor;
template <typename T>
@ -55,6 +61,7 @@ using offset_t = uint32_t; // type of offset va
using TensorPtr = std::shared_ptr<Tensor>;
class Tensor {
friend class tensor::DETensor;
public:
Tensor() = delete;
Tensor(const Tensor &other) = delete;
@ -117,6 +124,7 @@ class Tensor {
static Status CreateFromNpArray(const py::array &arr, TensorPtr *out);
#endif
#ifndef ENABLE_ANDROID
/// Create a tensor of type DE_STRING from a BytesList.
/// \param[in] bytes_list protobuf's Bytelist
/// \param[in] shape shape of the outout tensor
@ -134,6 +142,7 @@ class Tensor {
/// \return Status Code
static Status CreateFromByteList(const dataengine::BytesList &bytes_list, const TensorShape &shape,
const DataType &type, dsize_t pad_size, TensorPtr *out);
#endif
/// Create a Tensor from a given list of values.
/// \tparam type of the values to be inserted.

53
mindspore/ccsrc/minddata/dataset/include/de_tensor.h Normal file
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@ -0,0 +1,53 @@
#ifndef DATASET_INCLUDE_DETENSOR_H_
#define DATASET_INCLUDE_DETENSOR_H_
#include "include/ms_tensor.h"
#include "minddata/dataset/include/tensor.h"
#include "minddata/dataset/util/status.h"
namespace mindspore {
namespace tensor {
class DETensor : public MSTensor {
public:
// brief Create a MSTensor pointer.
//
// param data_type DataTypeId of tensor to be created.
// param shape Shape of tensor to be created.
// return MSTensor pointer.
static MSTensor *CreateTensor(TypeId data_type, const std::vector<int> &shape);
static MSTensor *CreateTensor(const std::string &path);
DETensor(TypeId data_type, const std::vector<int> &shape);
explicit DETensor(std::shared_ptr<dataset::Tensor> tensor_ptr);
~DETensor() = default;
MSTensor *ConvertToLiteTensor();
std::shared_ptr<dataset::Tensor> tensor() const;
TypeId data_type() const override;
TypeId set_data_type(const TypeId data_type) override;
std::vector<int> shape() const override;
size_t set_shape(const std::vector<int> &shape) override;
int DimensionSize(size_t index) const override;
int ElementsNum() const override;
std::size_t hash() const override;
size_t Size() const override;
void *MutableData() const override;
protected:
std::shared_ptr<dataset::Tensor> tensor_impl_;
};
} // namespace tensor
} // namespace mindspore
#endif // DATASET_INCLUDE_DETENSOR_H_

51
mindspore/ccsrc/minddata/dataset/include/execute.h Normal file
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@ -0,0 +1,51 @@
/**
* 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.
*/
#ifndef DATASET_API_EXECUTE_H_
#define DATASET_API_EXECUTE_H_
#include <vector>
#include <memory>
#include "minddata/dataset/core/constants.h"
#include "minddata/dataset/include/de_tensor.h"
#include "minddata/dataset/include/transforms.h"
namespace mindspore {
namespace dataset {
class TensorOp;
namespace api {
class Execute {
public:
/// \brief Constructor
Execute(const std::shared_ptr<TensorOperation> &op);
/// \brief callable function to execute the TensorOperation in eager mode
/// \param[inout] input - the tensor to be transformed
/// \return - the output tensor, nullptr if Compute fails
std::shared_ptr<tensor::MSTensor> operator()(std::shared_ptr<tensor::MSTensor> input);
private:
std::shared_ptr<TensorOperation> op_;
};
} // namespace api
} // namespace dataset
} // namespace mindspore
#endif // DATASET_API_EXECUTE_H_

24
mindspore/ccsrc/minddata/dataset/include/tensor.h
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@ -38,12 +38,18 @@
#include "minddata/dataset/core/data_type.h"
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/util/status.h"
#include "minddata/dataset/include/de_tensor.h"
#ifndef ENABLE_ANDROID
#include "proto/example.pb.h"
#endif
#ifdef ENABLE_PYTHON
namespace py = pybind11;
#endif
namespace mindspore {
namespace tensor {
class DETensor;
} // namespace tensor
namespace dataset {
class Tensor;
template <typename T>
@ -55,6 +61,7 @@ using offset_t = uint32_t; // type of offset va
using TensorPtr = std::shared_ptr<Tensor>;
class Tensor {
friend class tensor::DETensor;
public:
Tensor() = delete;
Tensor(const Tensor &other) = delete;
@ -117,6 +124,7 @@ class Tensor {
static Status CreateFromNpArray(const py::array &arr, TensorPtr *out);
#endif
#ifndef ENABLE_ANDROID
/// Create a tensor of type DE_STRING from a BytesList.
/// \param[in] bytes_list protobuf's Bytelist
/// \param[in] shape shape of the outout tensor
@ -134,6 +142,7 @@ class Tensor {
/// \return Status Code
static Status CreateFromByteList(const dataengine::BytesList &bytes_list, const TensorShape &shape,
const DataType &type, dsize_t pad_size, TensorPtr *out);
#endif
/// Create a Tensor from a given list of values.
/// \tparam type of the values to be inserted.
@ -649,13 +658,6 @@ class Tensor {
unsigned char *data_end_ = nullptr;
private:
#ifdef ENABLE_PYTHON
/// Helper function to create a tensor from Numpy array of strings
/// \param[in] arr Numpy array
/// \param[out] out Created Tensor
/// \return Status
static Status CreateFromNpString(py::array arr, TensorPtr *out);
#endif
/// Copy raw data of a array based on shape and strides to the destination pointer
/// \param dst [out] Pointer to the destination array where the content is to be copied
/// \param[in] src Pointer to the source of strided array to be copied
@ -668,6 +670,14 @@ class Tensor {
/// const of the size of the offset variable
static constexpr uint8_t kOffsetSize = sizeof(offset_t);
#ifdef ENABLE_PYTHON
/// Helper function to create a tensor from Numpy array of strings
/// \param[in] arr Numpy array
/// \param[out] out Created Tensor
/// \return Status
static Status CreateFromNpString(py::array arr, TensorPtr *out);
#endif
};
template <>
inline Tensor::TensorIterator<std::string_view> Tensor::end<std::string_view>() {

1
mindspore/ccsrc/minddata/dataset/kernels/image/resize_with_bbox_op.cc
View File

@ -20,7 +20,6 @@
#include "minddata/dataset/kernels/image/resize_op.h"
#include "minddata/dataset/kernels/image/image_utils.h"
#include "minddata/dataset/core/cv_tensor.h"
#include "minddata/dataset/core/pybind_support.h"
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/kernels/tensor_op.h"
#include "minddata/dataset/util/status.h"

27
mindspore/lite/CMakeLists.txt
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@ -33,6 +33,7 @@ option(BUILD_CONVERTER "if build converter" on)
option(ENABLE_FP16 "if build fp16 ops" off)
option(SUPPORT_GPU "if support gpu" off)
option(OFFLINE_COMPILE "if offline compile OpenCL kernel" off)
option(BUILD_MINDDATA "" on)
if (BUILD_DEVICE)
add_compile_definitions(BUILD_DEVICE)
@ -116,6 +117,32 @@ if (BUILD_DEVICE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=armv8.2-a+dotprod+fp16")
endif ()
endif()
endif()
if (BUILD_MINDDATA)
# opencv
set(OpenCV_DIR ${TOP_DIR}/third_party/opencv/build)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
# eigen
include_directories(${TOP_DIR}/third_party/eigen/)
# jpeg-turbo
add_library(jpeg-turbo SHARED IMPORTED)
set_target_properties(jpeg-turbo PROPERTIES
IMPORTED_LOCATION ${TOP_DIR}/third_party/libjpeg-turbo/lib/libturbojpeg.so
)
add_library(jpeg SHARED IMPORTED)
set_target_properties(jpeg PROPERTIES
IMPORTED_LOCATION ${TOP_DIR}/third_party/libjpeg-turbo/lib/libjpeg.so
)
include_directories(${TOP_DIR}/third_party/libjpeg-turbo/include)
add_compile_definitions(ENABLE_ANDROID)
add_compile_definitions(ENABLE_EAGER)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/minddata)
endif()
if (BUILD_DEVICE)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/src)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/tools/benchmark)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/test)

44
mindspore/lite/minddata/CMakeLists.txt Normal file
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@ -0,0 +1,44 @@
set(MINDDATA_DIR ${CCSRC_DIR}/minddata/dataset)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall -Wno-deprecated-declarations")
set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -s")
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/core MINDDATA_CORE_SRC_FILES)
list(REMOVE_ITEM MINDDATA_CORE_SRC_FILES "${MINDDATA_DIR}/core/client.cc")
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/kernels MINDDATA_KERNELS_SRC_FILES)
list(REMOVE_ITEM MINDDATA_KERNELS_SRC_FILES "${MINDDATA_DIR}/kernels/py_func_op.cc")
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/kernels/image MINDDATA_KERNELS_IMAGE_SRC_FILES)
AUX_SOURCE_DIRECTORY(${MINDDATA_DIR}/kernels/data MINDDATA_KERNELS_DATA_SRC_FILES)
add_library(minddata-eager OBJECT
${MINDDATA_DIR}/api/de_tensor.cc
${MINDDATA_DIR}/api/execute.cc
)
add_library(minddata-lite SHARED
${MINDDATA_CORE_SRC_FILES}
${MINDDATA_KERNELS_SRC_FILES}
${MINDDATA_KERNELS_IMAGE_SRC_FILES}
${MINDDATA_KERNELS_DATA_SRC_FILES}
${MINDDATA_DIR}/util/status.cc
${MINDDATA_DIR}/util/memory_pool.cc
${MINDDATA_DIR}/util/path.cc
${MINDDATA_DIR}/api/transforms.cc
${CORE_DIR}/utils/log_adapter.cc
${CCSRC_DIR}/gvar/logging_level.cc
)
target_link_libraries(minddata-lite
securec
jpeg-turbo
jpeg
opencv_core
opencv_imgcodecs
opencv_imgproc
mindspore::json
)

4
mindspore/lite/src/CMakeLists.txt
View File

@ -80,5 +80,9 @@ target_link_libraries(mindspore-lite
)
add_subdirectory(runtime/kernel/arm)
if (BUILD_MINDDATA)
target_link_libraries(mindspore-lite minddata-eager minddata-lite log)
endif ()
add_subdirectory(ops)

10
mindspore/lite/test/CMakeLists.txt
View File

@ -129,6 +129,15 @@ if (SUPPORT_GPU)
${LITE_DIR}/src/runtime/kernel/opencl/kernel/conv2d_transpose.cc
)
endif()
### minddata lite
if (BUILD_MINDDATA)
include_directories(${CCSRC_DIR}/minddata)
set(DATASET_TEST_DIR ${CMAKE_CURRENT_SOURCE_DIR}/dataset)
set(TEST_MINDDATA_SRC
${DATASET_TEST_DIR}/de_tensor_test.cc
${DATASET_TEST_DIR}/eager_test.cc
)
endif()
### runtime framework
file(GLOB_RECURSE OPS_SRC ${LITE_DIR}/src/ops/*.cc)
set(TEST_LITE_SRC
@ -245,6 +254,7 @@ file(GLOB_RECURSE TEST_CASE_KERNEL_SRC
set(TEST_SRC
${TEST_LITE_SRC}
${TEST_MINDDATA_SRC}
${TEST_CASE_KERNEL_SRC}
${TEST_DIR}/common/common_test.cc
${TEST_DIR}/main.cc

98
mindspore/lite/test/dataset/de_tensor_test.cc Normal file
View File

@ -0,0 +1,98 @@
/**
* 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.
*/
#include <memory>
#include <string>
#include "common/common_test.h"
#include "gtest/gtest.h"
#include "securec.h"
#include "dataset/core/tensor.h"
#include "dataset/core/cv_tensor.h"
#include "dataset/core/data_type.h"
#include "mindspore/lite/src/ir/tensor.h"
using namespace mindspore::dataset;
class MindDataTestTensorDE : public UT::Common {
public:
MindDataTestTensorDE() {}
};
TEST_F(MindDataTestTensorDE, MSTensorBasic) {
std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT32));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
ASSERT_EQ(t == std::dynamic_pointer_cast<mindspore::tensor::DETensor>(ms_tensor)->tensor(), true);
}
TEST_F(MindDataTestTensorDE, MSTensorConvertToLiteTensor) {
std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT32));
auto ms_tensor = std::shared_ptr<mindspore::tensor::DETensor>(new mindspore::tensor::DETensor(t));
std::shared_ptr<mindspore::tensor::MSTensor> lite_ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(
std::dynamic_pointer_cast<mindspore::tensor::DETensor>(ms_tensor)->ConvertToLiteTensor());
// check if the lite_ms_tensor is the derived LiteTensor
mindspore::tensor::LiteTensor * lite_tensor = static_cast<mindspore::tensor::LiteTensor *>(lite_ms_tensor.get());
ASSERT_EQ(lite_tensor != nullptr, true);
}
TEST_F(MindDataTestTensorDE, MSTensorShape) {
std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT32));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
ASSERT_EQ(ms_tensor->DimensionSize(0) == 2, true);
ASSERT_EQ(ms_tensor->DimensionSize(1) == 3, true);
ms_tensor->set_shape(std::vector<int>{3,2});
ASSERT_EQ(ms_tensor->DimensionSize(0) == 3, true);
ASSERT_EQ(ms_tensor->DimensionSize(1) == 2, true);
ms_tensor->set_shape(std::vector<int>{6});
ASSERT_EQ(ms_tensor->DimensionSize(0) == 6, true);
}
TEST_F(MindDataTestTensorDE, MSTensorSize) {
std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT32));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
ASSERT_EQ(ms_tensor->ElementsNum() == 6, true);
ASSERT_EQ(ms_tensor->Size() == 24, true);
}
TEST_F(MindDataTestTensorDE, MSTensorDataType) {
std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT32));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
ASSERT_EQ(ms_tensor->data_type() == mindspore::TypeId::kNumberTypeFloat32, true);
ms_tensor->set_data_type(mindspore::TypeId::kNumberTypeInt32);
ASSERT_EQ(ms_tensor->data_type() == mindspore::TypeId::kNumberTypeInt32, true);
ASSERT_EQ(std::dynamic_pointer_cast<mindspore::tensor::DETensor>(ms_tensor)->tensor()->type() == DataType::DE_INT32, true);
}
TEST_F(MindDataTestTensorDE, MSTensorMutableData) {
std::vector<float> x = {2.5, 2.5, 2.5, 2.5};
std::shared_ptr<Tensor> t;
Tensor::CreateTensor(&t, x, TensorShape({2, 2}));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
float *data = static_cast<float*>(ms_tensor->MutableData());
std::vector<float> tensor_vec(data, data + ms_tensor->ElementsNum());
ASSERT_EQ(x == tensor_vec, true);
// TODO: add set_data_type after implmenting it
}
TEST_F(MindDataTestTensorDE, MSTensorHash) {
std::vector<float> x = {2.5, 2.5, 2.5, 2.5};
std::shared_ptr<Tensor> t;
Tensor::CreateTensor(&t, x, TensorShape({2, 2}));
auto ms_tensor = std::shared_ptr<mindspore::tensor::MSTensor>(new mindspore::tensor::DETensor(t));
#ifdef ENABLE_ARM64
ASSERT_EQ(ms_tensor->hash() == 11093771382437, true); // arm64
#else
ASSERT_EQ(ms_tensor->hash() == 11093825635904, true);
#endif
}

165
mindspore/lite/test/dataset/eager_test.cc Normal file
View File

@ -0,0 +1,165 @@
/**
* 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 <chrono>
#include "common/common_test.h"
#include "gtest/gtest.h"
#include "securec.h"
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/core/config_manager.h"
#include "minddata/dataset/include/datasets.h"
#include "minddata/dataset/include/execute.h"
#include "minddata/dataset/util/path.h"
using namespace mindspore::dataset;
using namespace mindspore::dataset::api;
using namespace mindspore;
class MindDataTestEager : public UT::Common {
public:
MindDataTestEager() {}
};
TEST_F(MindDataTestEager, Test1) {
std::string in_dir = "/sdcard/data/testPK/data/class1";
Path base_dir = Path(in_dir);
MS_LOG(WARNING) << base_dir.toString() << ".";
if (!base_dir.IsDirectory() || !base_dir.Exists()) {
MS_LOG(INFO) << "Input dir is not a directory or doesn't exist" << ".";
}
auto t_start = std::chrono::high_resolution_clock::now();
// check if output_dir exists and create it if it does not exist
// iterate over in dir and create json for all images
auto dir_it = Path::DirIterator::OpenDirectory(&base_dir);
while (dir_it->hasNext()) {
Path v = dir_it->next();
MS_LOG(WARNING) << v.toString() << ".";
std::shared_ptr<tensor::MSTensor> image = std::shared_ptr<tensor::MSTensor>(tensor::DETensor::CreateTensor(v.toString()));
image = Execute(vision::Decode())(image);
EXPECT_TRUE(image != nullptr);
image = Execute(vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0}))(image);
EXPECT_TRUE(image != nullptr);
image = Execute(vision::Resize({224, 224}))(image);
EXPECT_TRUE(image != nullptr);
EXPECT_TRUE(image->DimensionSize(0) == 224);
EXPECT_TRUE(image->DimensionSize(1) == 224);
}
auto t_end = std::chrono::high_resolution_clock::now();
double elapsed_time_ms = std::chrono::duration<double, std::milli>(t_end-t_start).count();
MS_LOG(INFO) << "duration: " << elapsed_time_ms << " ms\n";
}
/*
TEST_F(MindDataTestEager, Test2) {
// string dir for image folder
std::string in_dir = datasets_root_path_ + "/testPK/data";
// run dataset with decode = on
std::shared_ptr<Dataset> ds = ImageFolder(in_dir, true, RandomSampler(false));
std::shared_ptr<TensorOperation> normalize_op = vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
EXPECT_TRUE(normalize_op != nullptr);
std::shared_ptr<TensorOperation> resize_op = vision::Resize({224, 224});
EXPECT_TRUE(resize_op != nullptr);
ds = ds->Map({normalize_op, resize_op});
EXPECT_TRUE(ds != nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_TRUE(iter != nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
MS_LOG(WARNING) << i << ".";
iter->Stop();
}
TEST_F(MindDataTestEager, Test3) {
// string dir for image folder
ConfigManager cm = ConfigManager();
cm.set_num_parallel_workers(1);
std::string in_dir = datasets_root_path_ + "/testPK/data";
// run dataset with decode = on
std::shared_ptr<Dataset> ds = ImageFolder(in_dir, true, RandomSampler(false));
std::shared_ptr<TensorOperation> normalize_op = vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
EXPECT_TRUE(normalize_op != nullptr);
std::shared_ptr<TensorOperation> resize_op = vision::Resize({224, 224});
EXPECT_TRUE(resize_op != nullptr);
ds = ds->Map({normalize_op, resize_op});
EXPECT_TRUE(ds != nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_TRUE(iter != nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
MS_LOG(WARNING) << i << ".";
iter->Stop();
}
TEST_F(MindDataTestEager, Test4) {
// string dir for image folder
ConfigManager cm = ConfigManager();
cm.set_num_parallel_workers(1);
std::string in_dir = datasets_root_path_ + "/testPK/data";
// run dataset with decode = on
std::shared_ptr<Dataset> ds = ImageFolder(in_dir, true, RandomSampler(false));
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_TRUE(iter != nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
image = Execute(vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0}))(image);
EXPECT_TRUE(image != nullptr);
image = Execute(vision::Resize({224, 224}))(image);
EXPECT_TRUE(image != nullptr);
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
MS_LOG(WARNING) << i << ".";
iter->Stop();
}
*/

1
third_party/eigen vendored Submodule

@ -0,0 +1 @@
Subproject commit daf9bbeca26e98da2eed0058835cbb04e0a30ad8

1
third_party/libjpeg-turbo vendored Submodule

@ -0,0 +1 @@
Subproject commit b443c541b9a6fdcac214f9f003de0aa13e480ac1

1
third_party/opencv vendored Submodule

@ -0,0 +1 @@
Subproject commit bda89a6469aa79ecd8713967916bd754bff1d931