Rename enum class `DTYPE` to `DataType` to conform to the naming rules.
Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>
namespace mir
{
-enum class DTYPE
+enum class DataType
{
UNKNOWN,
FLOAT32,
INT64
};
+// TODO Remove after chaning all uses.
+using DTYPE = DataType;
+
} // namespace mir
#endif //_MIR_DATA_TYPE_H_
/**
* @brief Class for Scalar values in modelIR
* @param data Data pointer
- * @param dtype Data type
+ * @param data_type Data type
* @param data_size Data size
*/
- explicit Scalar(const char *data, DTYPE dtype, unsigned data_size)
+ explicit Scalar(const char *data, DataType data_type, unsigned data_size)
{ // NOLINT(cppcoreguidelines-pro-type-member-init, hicpp-member-init)
assert(data_size <= _max_scalar_length);
- _data_type = dtype;
+ _data_type = data_type;
memcpy(_data, data, data_size);
}
/**
* @return Data type
*/
- DTYPE getDataType() { return _data_type; }
+ DataType getDataType() { return _data_type; }
/**
* @return Data size
{
switch (_data_type)
{
- case DTYPE::UNKNOWN:
+ case DataType::UNKNOWN:
return -1;
- case DTYPE::FLOAT32:
- case DTYPE::INT32:
+ case DataType::FLOAT32:
+ case DataType::INT32:
return 4;
- case DTYPE::INT64:
+ case DataType::INT64:
return 8;
default:
assert(false);
private:
static const unsigned int _max_scalar_length = 8;
- DTYPE _data_type;
+ DataType _data_type;
char _data[_max_scalar_length];
};
class TensorVariant
{
public:
- TensorVariant(DTYPE dtype, const Shape &shape);
+ TensorVariant(DataType data_type, const Shape &shape);
- TensorVariant(DTYPE dtype, const Shape &shape, const void *data);
+ TensorVariant(DataType data_type, const Shape &shape, const void *data);
TensorVariant(const TensorVariant &t_old, const Shape &shape);
}
const Shape &getShape() const { return _shape; }
- DTYPE getDataType() const { return _dtype; }
+ DataType getDataType() const { return _data_type; }
size_t getElementSize() const { return _element_size; }
private:
- DTYPE _dtype;
+ DataType _data_type;
std::shared_ptr<char> _data;
adt::small_vector<int_fast32_t, MAX_DIMENSION_COUNT> _strides;
Shape _shape;
namespace mir
{
-TensorVariant::TensorVariant(DTYPE dtype, const Shape &shape)
- : _dtype(dtype), _strides(shape.rank()), _shape(shape)
+TensorVariant::TensorVariant(DataType data_type, const Shape &shape)
+ : _data_type(data_type), _strides(shape.rank()), _shape(shape)
{
- switch (dtype)
+ switch (data_type)
{
- case DTYPE::FLOAT32:
+ case DataType::FLOAT32:
_element_size = sizeof(float);
break;
- case DTYPE::FLOAT64:
+ case DataType::FLOAT64:
_element_size = sizeof(double);
break;
- case DTYPE::INT32:
+ case DataType::INT32:
_element_size = sizeof(int32_t);
break;
- case DTYPE::INT64:
+ case DataType::INT64:
_element_size = sizeof(int64_t);
break;
default:
}
}
-TensorVariant::TensorVariant(DTYPE dtype, const Shape &shape, const void *data)
- : TensorVariant(dtype, shape)
+TensorVariant::TensorVariant(DataType data_type, const Shape &shape, const void *data)
+ : TensorVariant(data_type, shape)
{
std::size_t data_size = _shape.numElements() * _element_size;
std::memcpy(_data.get(), data, data_size);
* @param shape shape to broadcast to
*/
TensorVariant::TensorVariant(const TensorVariant &t_old, const Shape &shape)
- : _dtype(t_old._dtype), _data(t_old._data), _strides(static_cast<size_t>(shape.rank())),
+ : _data_type(t_old._data_type), _data(t_old._data), _strides(static_cast<size_t>(shape.rank())),
_shape(shape), _element_size(t_old._element_size)
{
int axis_old = t_old._shape.rank() - 1;
TEST(TensorVariant, BasicTest)
{
Shape shape{2, 2};
- TensorVariant t(DTYPE::FLOAT32, shape);
+ TensorVariant t(DataType::FLOAT32, shape);
ASSERT_EQ(t.getShape(), shape);
ASSERT_EQ(t.getOffset({0, 0}), 0u);
TEST(TensorVariant, ElementSizeDeductionTest)
{
Shape shape{2, 2, 2};
- TensorVariant t(DTYPE::FLOAT32, shape);
+ TensorVariant t(DataType::FLOAT32, shape);
ASSERT_EQ(t.getElementSize(), sizeof(float));
ASSERT_EQ((float *)t.at({1, 1, 1}), (float *)t.at({0, 0, 0}) + 7);