diff --git a/_unittests/onnx-numpy-skips.txt b/_unittests/onnx-numpy-skips.txt
index eef3e70..62de43f 100644
--- a/_unittests/onnx-numpy-skips.txt
+++ b/_unittests/onnx-numpy-skips.txt
@@ -5,7 +5,6 @@ array_api_tests/test_creation_functions.py::test_asarray_arrays
array_api_tests/test_creation_functions.py::test_empty
array_api_tests/test_creation_functions.py::test_empty_like
array_api_tests/test_creation_functions.py::test_eye
-array_api_tests/test_creation_functions.py::test_full
array_api_tests/test_creation_functions.py::test_full_like
array_api_tests/test_creation_functions.py::test_linspace
array_api_tests/test_creation_functions.py::test_meshgrid
diff --git a/_unittests/test_array_api.sh b/_unittests/test_array_api.sh
index cb32fe4..9464ee6 100644
--- a/_unittests/test_array_api.sh
+++ b/_unittests/test_array_api.sh
@@ -1,4 +1,4 @@
export ARRAY_API_TESTS_MODULE=onnx_array_api.array_api.onnx_numpy
-# pytest ../array-api-tests/array_api_tests/test_creation_functions.py::test_asarray_arrays || exit 1
+pytest ../array-api-tests/array_api_tests/test_creation_functions.py::test_asarray_scalars || exit 1
# pytest ../array-api-tests/array_api_tests/test_creation_functions.py --help
pytest ../array-api-tests/array_api_tests/test_creation_functions.py --hypothesis-explain --skips-file=_unittests/onnx-numpy-skips.txt || exit 1
\ No newline at end of file
diff --git a/_unittests/ut_array_api/test_array_apis.py b/_unittests/ut_array_api/test_array_apis.py
index c72700c..9a8dd7c 100644
--- a/_unittests/ut_array_api/test_array_apis.py
+++ b/_unittests/ut_array_api/test_array_apis.py
@@ -13,7 +13,7 @@ class TestArraysApis(ExtTestCase):
def test_zeros_numpy_1(self):
c = xpn.zeros(1)
d = c.numpy()
- self.assertEqualArray(np.array([0], dtype=np.float32), d)
+ self.assertEqualArray(np.array([0], dtype=np.float64), d)
def test_zeros_ort_1(self):
c = xpo.zeros(1)
diff --git a/_unittests/ut_array_api/test_onnx_numpy.py b/_unittests/ut_array_api/test_onnx_numpy.py
index bd79ecf..4cb7544 100644
--- a/_unittests/ut_array_api/test_onnx_numpy.py
+++ b/_unittests/ut_array_api/test_onnx_numpy.py
@@ -19,6 +19,40 @@ def test_zeros(self):
a = xp.absolute(mat)
self.assertEqualArray(np.absolute(mat.numpy()), a.numpy())
+ def test_zeros_none(self):
+ c = EagerTensor(np.array([4, 5], dtype=np.int64))
+ mat = xp.zeros(c)
+ matnp = mat.numpy()
+ self.assertEqual(matnp.shape, (4, 5))
+ self.assertNotEmpty(matnp[0, 0])
+ self.assertEqualArray(matnp, np.zeros((4, 5)))
+
+ def test_ones_none(self):
+ c = EagerTensor(np.array([4, 5], dtype=np.int64))
+ mat = xp.ones(c)
+ matnp = mat.numpy()
+ self.assertEqual(matnp.shape, (4, 5))
+ self.assertNotEmpty(matnp[0, 0])
+ self.assertEqualArray(matnp, np.ones((4, 5)))
+
+ def test_full(self):
+ c = EagerTensor(np.array([4, 5], dtype=np.int64))
+ mat = xp.full(c, fill_value=5, dtype=xp.int64)
+ matnp = mat.numpy()
+ self.assertEqual(matnp.shape, (4, 5))
+ self.assertNotEmpty(matnp[0, 0])
+ a = xp.absolute(mat)
+ self.assertEqualArray(np.absolute(mat.numpy()), a.numpy())
+
+ def test_full_bool(self):
+ c = EagerTensor(np.array([4, 5], dtype=np.int64))
+ mat = xp.full(c, fill_value=False)
+ matnp = mat.numpy()
+ self.assertEqual(matnp.shape, (4, 5))
+ self.assertNotEmpty(matnp[0, 0])
+ self.assertEqualArray(matnp, np.full((4, 5), False))
+
if __name__ == "__main__":
+ TestOnnxNumpy().test_zeros_none()
unittest.main(verbosity=2)
diff --git a/_unittests/ut_npx/test_npx.py b/_unittests/ut_npx/test_npx.py
index 93f2b5e..17b5863 100644
--- a/_unittests/ut_npx/test_npx.py
+++ b/_unittests/ut_npx/test_npx.py
@@ -710,8 +710,8 @@ def impl(
keys = list(sorted(f.onxs))
self.assertIsInstance(f.onxs[keys[0]], ModelProto)
k = keys[-1]
- self.assertEqual(len(k), 3)
- self.assertEqual(k[1:], ("axis", 0))
+ self.assertEqual(len(k), 4)
+ self.assertEqual(k[1:], ("axis", int, 0))
def test_numpy_topk(self):
f = topk(Input("X"), Input("K"))
@@ -2416,6 +2416,7 @@ def compute_labels(X, centers, use_sqrt=False):
(DType(TensorProto.DOUBLE), 2),
(DType(TensorProto.DOUBLE), 2),
"use_sqrt",
+ bool,
True,
)
self.assertEqual(f.available_versions, [key])
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index ca24462..c449f2e 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -48,7 +48,7 @@ jobs:
vmImage: 'ubuntu-latest'
strategy:
matrix:
- Python310-Linux:
+ Python311-Linux:
python.version: '3.11'
maxParallel: 3
@@ -96,7 +96,7 @@ jobs:
strategy:
matrix:
Python310-Linux:
- python.version: '3.11'
+ python.version: '3.10'
maxParallel: 3
steps:
@@ -149,7 +149,7 @@ jobs:
vmImage: 'ubuntu-latest'
strategy:
matrix:
- Python310-Linux:
+ Python311-Linux:
python.version: '3.11'
maxParallel: 3
@@ -202,7 +202,7 @@ jobs:
vmImage: 'windows-latest'
strategy:
matrix:
- Python310-Windows:
+ Python311-Windows:
python.version: '3.11'
maxParallel: 3
@@ -235,7 +235,7 @@ jobs:
vmImage: 'macOS-latest'
strategy:
matrix:
- Python310-Mac:
+ Python311-Mac:
python.version: '3.11'
maxParallel: 3
diff --git a/onnx_array_api/_helpers.py b/onnx_array_api/_helpers.py
index 6191c92..f9808ca 100644
--- a/onnx_array_api/_helpers.py
+++ b/onnx_array_api/_helpers.py
@@ -39,7 +39,7 @@ def np_dtype_to_tensor_dtype(dtype: Any):
elif dtype is int:
dt = TensorProto.INT64
elif dtype is float:
- dt = TensorProto.FLOAT64
+ dt = TensorProto.DOUBLE
else:
raise KeyError(f"Unable to guess type for dtype={dtype}.")
return dt
diff --git a/onnx_array_api/array_api/_onnx_common.py b/onnx_array_api/array_api/_onnx_common.py
index 6553137..f832b72 100644
--- a/onnx_array_api/array_api/_onnx_common.py
+++ b/onnx_array_api/array_api/_onnx_common.py
@@ -44,7 +44,7 @@ def template_asarray(
except OverflowError:
v = TEagerTensor(np.asarray(a, dtype=np.uint64))
elif isinstance(a, float):
- v = TEagerTensor(np.array(a, dtype=np.float32))
+ v = TEagerTensor(np.array(a, dtype=np.float64))
elif isinstance(a, bool):
v = TEagerTensor(np.array(a, dtype=np.bool_))
elif isinstance(a, str):
diff --git a/onnx_array_api/array_api/onnx_numpy.py b/onnx_array_api/array_api/onnx_numpy.py
index 2cd4bfd..425418f 100644
--- a/onnx_array_api/array_api/onnx_numpy.py
+++ b/onnx_array_api/array_api/onnx_numpy.py
@@ -3,7 +3,6 @@
"""
from typing import Any, Optional
import numpy as np
-from onnx import TensorProto
from ..npx.npx_functions import (
all,
abs,
@@ -16,10 +15,11 @@
reshape,
take,
)
+from ..npx.npx_functions import full as generic_full
from ..npx.npx_functions import ones as generic_ones
from ..npx.npx_functions import zeros as generic_zeros
from ..npx.npx_numpy_tensors import EagerNumpyTensor
-from ..npx.npx_types import DType, ElemType, TensorType, OptParType
+from ..npx.npx_types import DType, ElemType, TensorType, OptParType, ParType, Scalar
from ._onnx_common import template_asarray
from . import _finalize_array_api
@@ -31,6 +31,7 @@
"astype",
"empty",
"equal",
+ "full",
"isdtype",
"isfinite",
"isnan",
@@ -58,7 +59,7 @@ def asarray(
def ones(
shape: TensorType[ElemType.int64, "I", (None,)],
- dtype: OptParType[DType] = DType(TensorProto.FLOAT),
+ dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if isinstance(shape, tuple):
@@ -76,7 +77,7 @@ def ones(
def empty(
shape: TensorType[ElemType.int64, "I", (None,)],
- dtype: OptParType[DType] = DType(TensorProto.FLOAT),
+ dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
raise RuntimeError(
@@ -87,7 +88,7 @@ def empty(
def zeros(
shape: TensorType[ElemType.int64, "I", (None,)],
- dtype: OptParType[DType] = DType(TensorProto.FLOAT),
+ dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
if isinstance(shape, tuple):
@@ -103,6 +104,32 @@ def zeros(
return generic_zeros(shape, dtype=dtype, order=order)
+def full(
+ shape: TensorType[ElemType.int64, "I", (None,)],
+ fill_value: ParType[Scalar] = None,
+ dtype: OptParType[DType] = None,
+ order: OptParType[str] = "C",
+) -> TensorType[ElemType.numerics, "T"]:
+ if fill_value is None:
+ raise TypeError("fill_value cannot be None")
+ value = fill_value
+ if isinstance(shape, tuple):
+ return generic_full(
+ EagerNumpyTensor(np.array(shape, dtype=np.int64)),
+ fill_value=value,
+ dtype=dtype,
+ order=order,
+ )
+ if isinstance(shape, int):
+ return generic_full(
+ EagerNumpyTensor(np.array([shape], dtype=np.int64)),
+ fill_value=value,
+ dtype=dtype,
+ order=order,
+ )
+ return generic_full(shape, fill_value=value, dtype=dtype, order=order)
+
+
def _finalize():
"""
Adds common attributes to Array API defined in this modules
diff --git a/onnx_array_api/npx/npx_core_api.py b/onnx_array_api/npx/npx_core_api.py
index 05cb0bb..548a40a 100644
--- a/onnx_array_api/npx/npx_core_api.py
+++ b/onnx_array_api/npx/npx_core_api.py
@@ -169,7 +169,7 @@ def wrapper(*inputs, **kwargs):
new_inputs.append(i)
elif isinstance(i, (int, float)):
new_inputs.append(
- np.array([i], dtype=np.int64 if isinstance(i, int) else np.float32)
+ np.array([i], dtype=np.int64 if isinstance(i, int) else np.float64)
)
elif isinstance(i, str):
new_inputs.append(Input(i))
diff --git a/onnx_array_api/npx/npx_functions.py b/onnx_array_api/npx/npx_functions.py
index 29a4481..98e37f4 100644
--- a/onnx_array_api/npx/npx_functions.py
+++ b/onnx_array_api/npx/npx_functions.py
@@ -15,6 +15,7 @@
SequenceType,
TensorType,
TupleType,
+ Scalar,
)
from .npx_var import Var
@@ -22,7 +23,7 @@
def _cstv(x):
if isinstance(x, Var):
return x
- if isinstance(x, (int, float, np.ndarray)):
+ if isinstance(x, (int, float, bool, np.ndarray)):
return cst(x)
raise TypeError(f"Unexpected constant type {type(x)}.")
@@ -376,6 +377,42 @@ def expit(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.numerics
return var(x, op="Sigmoid")
+@npxapi_inline
+def full(
+ shape: TensorType[ElemType.int64, "I", (None,)],
+ dtype: OptParType[DType] = None,
+ fill_value: ParType[Scalar] = None,
+ order: OptParType[str] = "C",
+) -> TensorType[ElemType.numerics, "T"]:
+ """
+ Implements :func:`numpy.full`.
+ """
+ if order != "C":
+ raise RuntimeError(f"order={order!r} != 'C' not supported.")
+ if fill_value is None:
+ raise TypeError("fill_value cannot be None.")
+ if dtype is None:
+ if isinstance(fill_value, bool):
+ dtype = DType(TensorProto.BOOL)
+ elif isinstance(fill_value, int):
+ dtype = DType(TensorProto.INT64)
+ elif isinstance(fill_value, float):
+ dtype = DType(TensorProto.DOUBLE)
+ else:
+ raise TypeError(
+ f"Unexpected type {type(fill_value)} for fill_value={fill_value!r}."
+ )
+ if isinstance(fill_value, (float, int, bool)):
+ value = make_tensor(
+ name="cst", data_type=dtype.code, dims=[1], vals=[fill_value]
+ )
+ else:
+ raise NotImplementedError(
+ f"Unexpected type ({type(fill_value)} for fill_value={fill_value!r}."
+ )
+ return var(shape, value=value, op="ConstantOfShape")
+
+
@npxapi_inline
def floor(x: TensorType[ElemType.numerics, "T"]) -> TensorType[ElemType.numerics, "T"]:
"See :func:`numpy.floor`."
@@ -464,7 +501,7 @@ def matmul(
@npxapi_inline
def ones(
shape: TensorType[ElemType.int64, "I", (None,)],
- dtype: OptParType[DType] = DType(TensorProto.FLOAT),
+ dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
@@ -473,7 +510,7 @@ def ones(
if order != "C":
raise RuntimeError(f"order={order!r} != 'C' not supported.")
if dtype is None:
- dtype = DType(TensorProto.FLOAT)
+ dtype = DType(TensorProto.DOUBLE)
return var(
shape,
value=make_tensor(name="one", data_type=dtype.code, dims=[1], vals=[1]),
@@ -674,7 +711,7 @@ def where(
@npxapi_inline
def zeros(
shape: TensorType[ElemType.int64, "I", (None,)],
- dtype: OptParType[DType] = DType(TensorProto.FLOAT),
+ dtype: OptParType[DType] = None,
order: OptParType[str] = "C",
) -> TensorType[ElemType.numerics, "T"]:
"""
@@ -683,7 +720,7 @@ def zeros(
if order != "C":
raise RuntimeError(f"order={order!r} != 'C' not supported.")
if dtype is None:
- dtype = DType(TensorProto.FLOAT)
+ dtype = DType(TensorProto.DOUBLE)
return var(
shape,
value=make_tensor(name="zero", data_type=dtype.code, dims=[1], vals=[0]),
diff --git a/onnx_array_api/npx/npx_graph_builder.py b/onnx_array_api/npx/npx_graph_builder.py
index d41b91c..ff02843 100644
--- a/onnx_array_api/npx/npx_graph_builder.py
+++ b/onnx_array_api/npx/npx_graph_builder.py
@@ -784,7 +784,7 @@ def to_onnx(
node_inputs.append(input_name)
continue
- if isinstance(i, (int, float)):
+ if isinstance(i, (int, float, bool)):
ni = np.array(i)
c = Cst(ni)
input_name = self._unique(var._prefix)
diff --git a/onnx_array_api/npx/npx_jit_eager.py b/onnx_array_api/npx/npx_jit_eager.py
index 35ff9af..c222f01 100644
--- a/onnx_array_api/npx/npx_jit_eager.py
+++ b/onnx_array_api/npx/npx_jit_eager.py
@@ -131,7 +131,8 @@ def make_key(*values, **kwargs):
for iv, v in enumerate(values):
if isinstance(v, (Var, EagerTensor, JitTensor)):
res.append(v.key)
- elif isinstance(v, (int, float, DType)):
+ elif isinstance(v, (int, float, bool, DType)):
+ res.append(type(v))
res.append(v)
elif isinstance(v, slice):
res.append(("slice", v.start, v.stop, v.step))
@@ -153,8 +154,9 @@ def make_key(*values, **kwargs):
)
if kwargs:
for k, v in sorted(kwargs.items()):
- if isinstance(v, (int, float, str, type, DType)):
+ if isinstance(v, (int, float, str, type, bool, DType)):
res.append(k)
+ res.append(type(v))
res.append(v)
elif isinstance(v, tuple):
newv = []
@@ -169,7 +171,8 @@ def make_key(*values, **kwargs):
newv.append(t)
res.append(tuple(newv))
elif v is None and k in {"dtype"}:
- continue
+ res.append(k)
+ res.append(v)
else:
raise TypeError(
f"Type {type(v)} is not yet supported, "
@@ -543,12 +546,12 @@ def _preprocess_constants(self, *args):
elif isinstance(n, np.ndarray):
new_args.append(self.tensor_class(n))
modified = True
- elif isinstance(n, (int, float)):
+ elif isinstance(n, (int, float, bool)):
new_args.append(self.tensor_class(np.array(n)))
modified = True
elif isinstance(n, DType):
new_args.append(n)
- elif n in (int, float):
+ elif n in (int, float, bool):
# usually used to cast
new_args.append(n)
elif n is None:
@@ -586,6 +589,7 @@ def __call__(self, *args, already_eager=False, **kwargs):
EagerTensor,
Cst,
int,
+ bool,
float,
tuple,
slice,
@@ -616,12 +620,13 @@ def __call__(self, *args, already_eager=False, **kwargs):
else:
# tries to call the version
try:
- res = self.f(*values)
+ res = self.f(*values, **kwargs)
except (AttributeError, TypeError) as e:
inp1 = ", ".join(map(str, map(type, args)))
inp2 = ", ".join(map(str, map(type, values)))
raise TypeError(
- f"Unexpected types, input types are {inp1} " f"and {inp2}."
+ f"Unexpected types, input types are {inp1} "
+ f"and {inp2}, kwargs={kwargs}."
) from e
if isinstance(res, EagerTensor) or (
diff --git a/onnx_array_api/npx/npx_numpy_tensors_ops.py b/onnx_array_api/npx/npx_numpy_tensors_ops.py
index 5278019..b4639ae 100644
--- a/onnx_array_api/npx/npx_numpy_tensors_ops.py
+++ b/onnx_array_api/npx/npx_numpy_tensors_ops.py
@@ -7,7 +7,9 @@ class ConstantOfShape(OpRun):
@staticmethod
def _process(value):
cst = value[0] if isinstance(value, np.ndarray) else value
- if isinstance(cst, int):
+ if isinstance(cst, bool):
+ cst = np.bool_(cst)
+ elif isinstance(cst, int):
cst = np.int64(cst)
elif isinstance(cst, float):
cst = np.float64(cst)
diff --git a/onnx_array_api/npx/npx_tensors.py b/onnx_array_api/npx/npx_tensors.py
index b0e92c2..9286ae2 100644
--- a/onnx_array_api/npx/npx_tensors.py
+++ b/onnx_array_api/npx/npx_tensors.py
@@ -133,7 +133,7 @@ def _generic_method_operator(self, method_name, *args: Any, **kwargs: Any) -> An
for a in args:
if isinstance(a, np.ndarray):
new_args.append(self.__class__(a.astype(self.dtype.np_dtype)))
- elif isinstance(a, (int, float)):
+ elif isinstance(a, (int, float, bool)):
new_args.append(
self.__class__(np.array([a]).astype(self.dtype.np_dtype))
)
diff --git a/onnx_array_api/npx/npx_types.py b/onnx_array_api/npx/npx_types.py
index 6063e64..0f7f6dc 100644
--- a/onnx_array_api/npx/npx_types.py
+++ b/onnx_array_api/npx/npx_types.py
@@ -292,6 +292,19 @@ def get_set_name(cls, dtypes):
return None
+class Scalar:
+ """
+ Defines a scalar.
+ """
+
+ def __init__(self, value: Union[float, int, bool]):
+ self.value = value
+
+ def __repr__(self):
+ "usual"
+ return f"Scalar({self.value!r})"
+
+
class ParType(WrapperType):
"""
Defines a parameter type.
@@ -300,11 +313,18 @@ class ParType(WrapperType):
:param optional: is optional or not
"""
- map_names = {int: "int", float: "float", str: "str", DType: "DType"}
+ map_names = {
+ int: "int",
+ float: "float",
+ str: "str",
+ DType: "DType",
+ bool: "bool",
+ Scalar: "Scalar",
+ }
@classmethod
def __class_getitem__(cls, dtype):
- if isinstance(dtype, (int, float)):
+ if isinstance(dtype, (int, float, bool)):
msg = str(dtype)
else:
msg = getattr(dtype, "__name__", str(dtype))
@@ -331,6 +351,8 @@ def onnx_type(cls):
return AttributeProto.INT
if cls.dtype == float:
return AttributeProto.FLOAT
+ if cls.dtype == bool:
+ return AttributeProto.BOOL
if cls.dtype == str:
return AttributeProto.STRING
raise RuntimeError(
@@ -347,7 +369,7 @@ class OptParType(ParType):
@classmethod
def __class_getitem__(cls, dtype):
- if isinstance(dtype, (int, float)):
+ if isinstance(dtype, (int, float, bool)):
msg = str(dtype)
else:
msg = dtype.__name__
diff --git a/onnx_array_api/npx/npx_var.py b/onnx_array_api/npx/npx_var.py
index 2759f4c..a4802e3 100644
--- a/onnx_array_api/npx/npx_var.py
+++ b/onnx_array_api/npx/npx_var.py
@@ -12,7 +12,7 @@ class Par:
Defines a named parameter.
:param name: parameter name
- :param dtype: parameter type (int, str, float)
+ :param dtype: parameter type (bool, int, str, float)
:param value: value of the parameter if known
:param parent_op: node type it belongs to
"""
@@ -233,7 +233,7 @@ def __call__(self, new_values):
def _setitem1_where(self, index, new_values):
cst, var = Var.get_cst_var()
- if isinstance(new_values, (int, float)):
+ if isinstance(new_values, (int, float, bool)):
new_values = np.array(new_values)
if isinstance(new_values, np.ndarray):
value = var(cst(new_values), self.parent, op="CastLike")
@@ -446,7 +446,7 @@ def _get_vars(self):
cst = Var.get_cst_var()[0]
replacement_cst[id(i)] = cst(i)
continue
- if isinstance(i, (int, float)):
+ if isinstance(i, (int, float, bool)):
cst = Var.get_cst_var()[0]
replacement_cst[id(i)] = cst(np.array(i))
continue
@@ -595,13 +595,13 @@ def __iter__(self):
def _binary_op(self, ov: "Var", op_name: str, **kwargs) -> "Var":
var = Var.get_cst_var()[1]
- if isinstance(ov, (int, float, np.ndarray, Cst)):
+ if isinstance(ov, (int, float, bool, np.ndarray, Cst)):
return var(self.self_var, var(ov, self.self_var, op="CastLike"), op=op_name)
return var(self.self_var, ov, op=op_name, **kwargs)
def _binary_op_right(self, ov: "Var", op_name: str, **kwargs) -> "Var":
var = Var.get_cst_var()[1]
- if isinstance(ov, (int, float, np.ndarray, Cst)):
+ if isinstance(ov, (int, float, bool, np.ndarray, Cst)):
return var(var(ov, self.self_var, op="CastLike"), self.self_var, op=op_name)
return var(ov, self.self_var, op=op_name, **kwargs)
@@ -1108,14 +1108,18 @@ class Cst(Var):
def __init__(self, cst: Any):
if isinstance(cst, np.ndarray):
Var.__init__(self, cst, op="Identity")
+ elif isinstance(cst, bool):
+ Var.__init__(self, np.array(cst, dtype=np.bool_), op="Identity")
elif isinstance(cst, int):
Var.__init__(self, np.array(cst, dtype=np.int64), op="Identity")
elif isinstance(cst, float):
- Var.__init__(self, np.array(cst, dtype=np.float32), op="Identity")
+ Var.__init__(self, np.array(cst, dtype=np.float64), op="Identity")
elif isinstance(cst, list):
- if all(map(lambda t: isinstance(t, int), cst)):
+ if all(map(lambda t: isinstance(t, bool), cst)):
+ Var.__init__(self, np.array(cst, dtype=np.bool_), op="Identity")
+ elif all(map(lambda t: isinstance(t, (int, bool)), cst)):
Var.__init__(self, np.array(cst, dtype=np.int64), op="Identity")
- elif all(map(lambda t: isinstance(t, (float, int)), cst)):
+ elif all(map(lambda t: isinstance(t, (float, int, bool)), cst)):
Var.__init__(self, np.array(cst, dtype=np.float64), op="Identity")
else:
raise ValueError(
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