def _guess_type_proto_str(data_type, dims):
# This could be moved to onnxconverter_common.
if data_type == "tensor(float)":
return FloatTensorType(dims)
if data_type == "tensor(double)":
return DoubleTensorType(dims)
if data_type == "tensor(string)":
return StringTensorType(dims)
if data_type == "tensor(int64)":
return Int64TensorType(dims)
if data_type == "tensor(int32)":
return Int32TensorType(dims)
if data_type == "tensor(bool)":
return BooleanTensorType(dims)
if data_type == "tensor(int8)":
return Int8TensorType(dims)
if data_type == "tensor(uint8)":
return UInt8TensorType(dims)
if Complex64TensorType is not None:
if data_type == "tensor(complex64)":
return Complex64TensorType(dims)
if data_type == "tensor(complex128)":
return Complex128TensorType(dims)
raise NotImplementedError(
"Unsupported data_type '{}'. You may raise an issue "
"at https://github.com/onnx/sklearn-onnx/issues."
"".format(data_type))
def _guess_type_proto(data_type, dims):
# This could be moved to onnxconverter_common.
for d in dims:
if d == 0:
raise RuntimeError("Dimension should not be null: {}.".format(
list(dims)))
if data_type == onnx_proto.TensorProto.FLOAT:
return FloatTensorType(dims)
if data_type == onnx_proto.TensorProto.DOUBLE:
return DoubleTensorType(dims)
if data_type == onnx_proto.TensorProto.STRING:
return StringTensorType(dims)
if data_type == onnx_proto.TensorProto.INT64:
return Int64TensorType(dims)
if data_type == onnx_proto.TensorProto.INT32:
return Int32TensorType(dims)
if data_type == onnx_proto.TensorProto.BOOL:
return BooleanTensorType(dims)
if data_type == onnx_proto.TensorProto.INT8:
return Int8TensorType(dims)
if data_type == onnx_proto.TensorProto.UINT8:
return UInt8TensorType(dims)
if Complex64TensorType is not None:
if data_type == onnx_proto.TensorProto.COMPLEX64:
return Complex64TensorType(dims)
if data_type == onnx_proto.TensorProto.COMPLEX128:
return Complex128TensorType(dims)
raise NotImplementedError(
"Unsupported data_type '{}'. You may raise an issue "
"at https://github.com/onnx/sklearn-onnx/issues."
"".format(data_type))
def _guess_type_proto(data_type, dims):
# This could be moved to onnxconverter_common.
if data_type == onnx_proto.TensorProto.FLOAT:
return FloatTensorType(dims)
elif data_type == onnx_proto.TensorProto.DOUBLE:
return DoubleTensorType(dims)
elif data_type == onnx_proto.TensorProto.STRING:
return StringTensorType(dims)
elif data_type == onnx_proto.TensorProto.INT64:
return Int64TensorType(dims)
elif data_type == onnx_proto.TensorProto.INT32:
return Int32TensorType(dims)
elif data_type == onnx_proto.TensorProto.BOOL:
return BooleanTensorType(dims)
else:
raise NotImplementedError(
"Unsupported data_type '{}'. You may raise an issue "
"at https://github.com/onnx/sklearn-onnx/issues."
"".format(data_type))
def _guess_numpy_type(data_type, dims):
# This could be moved to onnxconverter_common.
if data_type == np.float32:
return FloatTensorType(dims)
elif data_type in (np.str, str,
object) or str(data_type) in ('<U1', ): # noqa
return StringTensorType(dims)
elif data_type in (np.int64, np.uint64) or str(data_type) == '<U6':
return Int64TensorType(dims)
elif data_type in (np.int32,
np.uint32) or str(data_type) in ('<U4', ): # noqa
return Int32TensorType(dims)
elif data_type == np.bool:
return BooleanTensorType(dims)
else:
raise NotImplementedError(
"Unsupported data_type '{}'. You may raise an issue "
"at https://github.com/onnx/sklearn-onnx/issues."
"".format(data_type))
def _guess_numpy_type(data_type, dims):
# This could be moved to onnxconverter_common.
if data_type == np.float32:
return FloatTensorType(dims)
if data_type == np.float64:
return DoubleTensorType(dims)
if data_type in (np.str_, str, object) or str(data_type) in ('<U1', ) or (
hasattr(data_type, 'type') and data_type.type is np.str_): # noqa
return StringTensorType(dims)
if data_type in (np.int64, ) or str(data_type) == '<U6':
return Int64TensorType(dims)
if data_type in (np.int32, ) or str(data_type) in ('<U4', ): # noqa
return Int32TensorType(dims)
if data_type == np.uint8:
return UInt8TensorType(dims)
if data_type in (np.bool_, bool):
return BooleanTensorType(dims)
if data_type in (np.str_, str):
return StringTensorType(dims)
if data_type == np.int8:
return Int8TensorType(dims)
if data_type == np.int16:
return Int16TensorType(dims)
if data_type == np.uint64:
return UInt64TensorType(dims)
if data_type == np.uint32:
return UInt32TensorType(dims)
if data_type == np.uint16:
return UInt16TensorType(dims)
if data_type == np.float16:
return Float16TensorType(dims)
if Complex64TensorType is not None:
if data_type == np.complex64:
return Complex64TensorType(dims)
if data_type == np.complex128:
return Complex128TensorType(dims)
raise NotImplementedError(
"Unsupported data_type %r (type=%r). You may raise an issue "
"at https://github.com/onnx/sklearn-onnx/issues."
"" % (data_type, type(data_type)))
def from_pb(obj):
"""
Creates a data type from a protobuf object.
"""
def get_shape(tt):
return [
tt.shape.dim[i].dim_value for i in range(len(tt.shape.dim))
]
if hasattr(obj, 'extend'):
return [Variable.from_pb(o) for o in obj]
name = obj.name
if obj.type.tensor_type:
tt = obj.type.tensor_type
elem = tt.elem_type
shape = get_shape(tt)
if elem == onnx_proto.TensorProto.FLOAT:
ty = FloatTensorType(shape)
elif elem == onnx_proto.TensorProto.BOOL:
ty = BooleanTensorType(shape)
elif elem == onnx_proto.TensorProto.DOUBLE:
ty = DoubleTensorType(shape)
elif elem == onnx_proto.TensorProto.STRING:
ty = StringTensorType(shape)
elif elem == onnx_proto.TensorProto.INT64:
ty = Int64TensorType(shape)
elif elem == onnx_proto.TensorProto.INT32:
ty = Int32TensorType(shape)
else:
raise NotImplementedError("Unsupported type '{}' "
"(elem_type={}).".format(
type(obj.type.tensor_type),
elem))
else:
raise NotImplementedError("Unsupported type '{}' as "
"a string ({}).".format(type(obj), obj))
return Variable(name, name, None, ty)