forked from OSSInnovation/mindspore
fixed clang format
This commit is contained in:
ervinzhang
parent
6639149e58
commit
f92d68456d
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@ -8,169 +8,164 @@
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namespace mindspore {
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namespace tensor {
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dataset::DataType MSTypeToDEType(TypeId data_type) {
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switch (data_type) {
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case kNumberTypeBool:
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return dataset::DataType(dataset::DataType::DE_BOOL);
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case kNumberTypeInt8:
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return dataset::DataType(dataset::DataType::DE_INT8);
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case kNumberTypeUInt8:
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return dataset::DataType(dataset::DataType::DE_UINT8);
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case kNumberTypeInt16:
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return dataset::DataType(dataset::DataType::DE_INT16);
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case kNumberTypeUInt16:
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return dataset::DataType(dataset::DataType::DE_UINT16);
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case kNumberTypeInt32:
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return dataset::DataType(dataset::DataType::DE_INT32);
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case kNumberTypeUInt32:
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return dataset::DataType(dataset::DataType::DE_UINT32);
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case kNumberTypeInt64:
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return dataset::DataType(dataset::DataType::DE_INT64);
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case kNumberTypeUInt64:
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return dataset::DataType(dataset::DataType::DE_UINT64);
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case kNumberTypeFloat16:
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return dataset::DataType(dataset::DataType::DE_FLOAT16);
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case kNumberTypeFloat32:
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return dataset::DataType(dataset::DataType::DE_FLOAT32);
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case kNumberTypeFloat64:
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return dataset::DataType(dataset::DataType::DE_FLOAT64);
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default:
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// maybe throw?
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return dataset::DataType(dataset::DataType::DE_UNKNOWN);
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}
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switch (data_type) {
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case kNumberTypeBool:
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return dataset::DataType(dataset::DataType::DE_BOOL);
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case kNumberTypeInt8:
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return dataset::DataType(dataset::DataType::DE_INT8);
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case kNumberTypeUInt8:
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return dataset::DataType(dataset::DataType::DE_UINT8);
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case kNumberTypeInt16:
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return dataset::DataType(dataset::DataType::DE_INT16);
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case kNumberTypeUInt16:
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return dataset::DataType(dataset::DataType::DE_UINT16);
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case kNumberTypeInt32:
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return dataset::DataType(dataset::DataType::DE_INT32);
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case kNumberTypeUInt32:
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return dataset::DataType(dataset::DataType::DE_UINT32);
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case kNumberTypeInt64:
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return dataset::DataType(dataset::DataType::DE_INT64);
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case kNumberTypeUInt64:
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return dataset::DataType(dataset::DataType::DE_UINT64);
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case kNumberTypeFloat16:
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return dataset::DataType(dataset::DataType::DE_FLOAT16);
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case kNumberTypeFloat32:
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return dataset::DataType(dataset::DataType::DE_FLOAT32);
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case kNumberTypeFloat64:
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return dataset::DataType(dataset::DataType::DE_FLOAT64);
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default:
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return dataset::DataType(dataset::DataType::DE_UNKNOWN);
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}
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}
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TypeId DETypeToMSType(dataset::DataType data_type) {
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switch (data_type.value()) {
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case dataset::DataType::DE_BOOL:
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return mindspore::TypeId::kNumberTypeBool;
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case dataset::DataType::DE_INT8:
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return mindspore::TypeId::kNumberTypeInt8;
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case dataset::DataType::DE_UINT8:
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return mindspore::TypeId::kNumberTypeUInt8;
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case dataset::DataType::DE_INT16:
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return mindspore::TypeId::kNumberTypeInt16;
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case dataset::DataType::DE_UINT16:
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return mindspore::TypeId::kNumberTypeUInt16;
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case dataset::DataType::DE_INT32:
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return mindspore::TypeId::kNumberTypeInt32;
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case dataset::DataType::DE_UINT32:
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return mindspore::TypeId::kNumberTypeUInt32;
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case dataset::DataType::DE_INT64:
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return mindspore::TypeId::kNumberTypeInt64;
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case dataset::DataType::DE_UINT64:
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return mindspore::TypeId::kNumberTypeUInt64;
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case dataset::DataType::DE_FLOAT16:
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return mindspore::TypeId::kNumberTypeFloat16;
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case dataset::DataType::DE_FLOAT32:
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return mindspore::TypeId::kNumberTypeFloat32;
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case dataset::DataType::DE_FLOAT64:
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return mindspore::TypeId::kNumberTypeFloat64;
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default:
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// maybe throw?
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return kTypeUnknown;
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}
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switch (data_type.value()) {
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case dataset::DataType::DE_BOOL:
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return mindspore::TypeId::kNumberTypeBool;
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case dataset::DataType::DE_INT8:
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return mindspore::TypeId::kNumberTypeInt8;
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case dataset::DataType::DE_UINT8:
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return mindspore::TypeId::kNumberTypeUInt8;
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case dataset::DataType::DE_INT16:
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return mindspore::TypeId::kNumberTypeInt16;
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case dataset::DataType::DE_UINT16:
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return mindspore::TypeId::kNumberTypeUInt16;
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case dataset::DataType::DE_INT32:
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return mindspore::TypeId::kNumberTypeInt32;
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case dataset::DataType::DE_UINT32:
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return mindspore::TypeId::kNumberTypeUInt32;
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case dataset::DataType::DE_INT64:
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return mindspore::TypeId::kNumberTypeInt64;
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case dataset::DataType::DE_UINT64:
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return mindspore::TypeId::kNumberTypeUInt64;
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case dataset::DataType::DE_FLOAT16:
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return mindspore::TypeId::kNumberTypeFloat16;
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case dataset::DataType::DE_FLOAT32:
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return mindspore::TypeId::kNumberTypeFloat32;
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case dataset::DataType::DE_FLOAT64:
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return mindspore::TypeId::kNumberTypeFloat64;
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default:
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return kTypeUnknown;
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}
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}
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MSTensor *DETensor::CreateTensor(TypeId data_type, const std::vector<int> &shape) {
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return new DETensor(data_type, shape);
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return new DETensor(data_type, shape);
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}
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MSTensor *DETensor::CreateTensor(const std::string &path) {
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std::shared_ptr<dataset::Tensor> t;
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(void) dataset::Tensor::CreateFromFile(path, &t);
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return new DETensor(std::move(t));
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std::shared_ptr<dataset::Tensor> t;
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(void)dataset::Tensor::CreateFromFile(path, &t);
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return new DETensor(std::move(t));
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}
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DETensor::DETensor(TypeId data_type, const std::vector<int> &shape) {
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std::vector<dataset::dsize_t> t_shape;
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t_shape.reserve(shape.size());
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std::transform(shape.begin(), shape.end(),
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std::back_inserter(t_shape),
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[](int s) -> dataset::dsize_t {return static_cast<dataset::dsize_t>(s);});
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dataset::Tensor::CreateEmpty(dataset::TensorShape(t_shape), MSTypeToDEType(data_type), &this->tensor_impl_);
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std::vector<dataset::dsize_t> t_shape;
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t_shape.reserve(shape.size());
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std::transform(shape.begin(), shape.end(), std::back_inserter(t_shape),
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[](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
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dataset::Tensor::CreateEmpty(dataset::TensorShape(t_shape), MSTypeToDEType(data_type), &this->tensor_impl_);
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}
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DETensor::DETensor(std::shared_ptr<dataset::Tensor> tensor_ptr) { this->tensor_impl_ = std::move(tensor_ptr); }
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MSTensor *DETensor::ConvertToLiteTensor() {
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// static MSTensor::CreateTensor is only for the LiteTensor
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MSTensor *tensor = MSTensor::CreateTensor(this->data_type(), this->shape());
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MS_ASSERT(tensor->Size() == this->Size());
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memcpy_s(tensor->MutableData(), tensor->Size(), this->MutableData(), this->Size());
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return tensor;
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// static MSTensor::CreateTensor is only for the LiteTensor
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MSTensor *tensor = MSTensor::CreateTensor(this->data_type(), this->shape());
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MS_ASSERT(tensor->Size() == this->Size());
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memcpy_s(tensor->MutableData(), tensor->Size(), this->MutableData(), this->Size());
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return tensor;
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}
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std::shared_ptr<dataset::Tensor> DETensor::tensor() const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return this->tensor_impl_;
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return this->tensor_impl_;
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}
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TypeId DETensor::data_type() const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return DETypeToMSType(this->tensor_impl_->type());
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return DETypeToMSType(this->tensor_impl_->type());
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}
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TypeId DETensor::set_data_type(TypeId data_type) {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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if (data_type != this->data_type()) {
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std::shared_ptr<dataset::Tensor> temp;
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dataset::Tensor::CreateFromMemory(this->tensor_impl_->shape(), MSTypeToDEType(data_type),
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this->tensor_impl_->GetBuffer(), &temp);
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this->tensor_impl_ = temp;
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}
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return data_type;
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MS_ASSERT(this->tensor_impl_ != nullptr);
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if (data_type != this->data_type()) {
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std::shared_ptr<dataset::Tensor> temp;
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dataset::Tensor::CreateFromMemory(this->tensor_impl_->shape(), MSTypeToDEType(data_type),
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this->tensor_impl_->GetBuffer(), &temp);
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this->tensor_impl_ = temp;
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}
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return data_type;
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}
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std::vector<int> DETensor::shape() const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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std::vector<dataset::dsize_t> t_shape = this->tensor_impl_->shape().AsVector();
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std::vector<int> shape;
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shape.reserve(t_shape.size());
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std::transform(t_shape.begin(), t_shape.end(),
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std::back_inserter(shape),
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[](dataset::dsize_t s) -> int {return static_cast<int>(s);});
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return shape;
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MS_ASSERT(this->tensor_impl_ != nullptr);
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std::vector<dataset::dsize_t> t_shape = this->tensor_impl_->shape().AsVector();
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std::vector<int> shape;
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shape.reserve(t_shape.size());
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std::transform(t_shape.begin(), t_shape.end(), std::back_inserter(shape),
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[](dataset::dsize_t s) -> int { return static_cast<int>(s); });
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return shape;
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}
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size_t DETensor::set_shape(const std::vector<int> &shape) {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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std::vector<dataset::dsize_t> t_shape;
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t_shape.reserve(shape.size());
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std::transform(shape.begin(), shape.end(),
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std::back_inserter(t_shape),
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[](int s) -> dataset::dsize_t {return static_cast<dataset::dsize_t>(s);});
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dataset::Status rc = this->tensor_impl_->Reshape(dataset::TensorShape(t_shape));
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return shape.size();
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MS_ASSERT(this->tensor_impl_ != nullptr);
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std::vector<dataset::dsize_t> t_shape;
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t_shape.reserve(shape.size());
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std::transform(shape.begin(), shape.end(), std::back_inserter(t_shape),
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[](int s) -> dataset::dsize_t { return static_cast<dataset::dsize_t>(s); });
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dataset::Status rc = this->tensor_impl_->Reshape(dataset::TensorShape(t_shape));
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return shape.size();
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}
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int DETensor::DimensionSize(size_t index) const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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int dim_size = -1;
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auto shape = this->shape();
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if (index < shape.size()) {
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dim_size = shape[index];
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} else {
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MS_LOG(ERROR) << "Dimension index is wrong: " << index;
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}
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return dim_size;
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MS_ASSERT(this->tensor_impl_ != nullptr);
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int dim_size = -1;
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auto shape = this->shape();
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if (index < shape.size()) {
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dim_size = shape[index];
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} else {
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MS_LOG(ERROR) << "Dimension index is wrong: " << index;
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}
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return dim_size;
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}
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int DETensor::ElementsNum() const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return this->tensor_impl_->Size();
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MS_ASSERT(this->tensor_impl_ != nullptr);
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return this->tensor_impl_->Size();
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}
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std::size_t DETensor::hash() const {
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MS_ASSERT(this->tensor_impl_ != nullptr);
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auto shape = this->shape();
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std::size_t hash_value = std::hash<int>{}(SizeToInt(this->data_type()));
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hash_value = hash_combine(hash_value, std::hash<size_t>{}(shape.size()));
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// hash all elements may costly, so only take at most 4 elements into account based on
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// some experiments.
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for (size_t i = 0; (i < shape.size()) && (i < 4); ++i) {
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hash_value = hash_combine(hash_value, (std::hash<int>{}(shape[i])));
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}
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return hash_value;
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MS_ASSERT(this->tensor_impl_ != nullptr);
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auto shape = this->shape();
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std::size_t hash_value = std::hash<int>{}(SizeToInt(this->data_type()));
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hash_value = hash_combine(hash_value, std::hash<size_t>{}(shape.size()));
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// hash all elements may costly, so only take at most 4 elements into account based on
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// some experiments.
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for (size_t i = 0; (i < shape.size()) && (i < 4); ++i) {
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hash_value = hash_combine(hash_value, (std::hash<int>{}(shape[i])));
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}
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return hash_value;
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}
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size_t DETensor::Size() const {
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@ -49,7 +49,6 @@ std::shared_ptr<tensor::MSTensor> Execute::operator()(std::shared_ptr<tensor::MS
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return std::make_shared<tensor::DETensor>(std::move(de_output));
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}
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} // namespace api
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} // namespace dataset
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} // namespace mindspore
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@ -44,7 +44,6 @@ class Execute {
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std::shared_ptr<TensorOperation> op_;
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};
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} // namespace api
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} // namespace dataset
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} // namespace mindspore
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