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From: Diego Herrera <dherrera@redhat.com>
To: git-commits@fedoraproject.org
Subject: [rpms/onnx] test-1.21.0: Reorganize patches
Date: Wed, 01 Jul 2026 15:50:53 GMT [thread overview]
Message-ID: <178292105384.1.952699640524323405.rpms-onnx-351e8aaa0791@fedoraproject.org> (raw)
A new commit has been pushed.
Repo : rpms/onnx
Branch : test-1.21.0
Commit : 351e8aaa07916fe2add96d84f24d7c9d1327770c
Author : Diego Herrera <dherrera@redhat.com>
Date : 2026-07-01T00:34:32-04:00
Stats : +5981/-5814 in 10 file(s)
URL : https://src.fedoraproject.org/rpms/onnx/c/351e8aaa07916fe2add96d84f24d7c9d1327770c?branch=test-1.21.0
Log:
Reorganize patches
---
diff --git a/0000-Build-shared-libraries-and-fix-install-location.patch b/0000-Build-shared-libraries-and-fix-install-location.patch
deleted file mode 100644
index 5423755..0000000
--- a/0000-Build-shared-libraries-and-fix-install-location.patch
+++ /dev/null
@@ -1,72 +0,0 @@
-From a48f42b1e3dd972cf2c5ad9b9227da9e2b1c9fab Mon Sep 17 00:00:00 2001
-From: Alejandro Alvarez Ayllon <a.alvarezayllon@gmail.com>
-Date: Sat, 24 Feb 2024 14:48:45 +0100
-Subject: [PATCH 0/2] Build shared libraries and fix install location
-
-This patch was updated to be able to be used on modern versions of the library.
-
-Co-authored-by: Diego Herrera <dherrera@redhat.com>
-
----
-
-diff --git a/CMakeLists.txt b/CMakeLists.txt
-index 044996e1b..3a08d3289 100644
---- a/CMakeLists.txt
-+++ b/CMakeLists.txt
-@@ -414,19 +414,21 @@ if(ONNX_USE_LITE_PROTO)
- set(LINKED_PROTOBUF_TARGET protobuf::libprotobuf-lite)
- endif()
- endif()
--add_library(onnx_proto ${ONNX_PROTO_SRCS})
-+add_library(onnx_proto SHARED ${ONNX_PROTO_SRCS})
- add_onnx_global_defines(onnx_proto)
--
--add_library(onnx ${ONNX_SRCS} ${ONNX_PROTO_SRCS})
-+set_target_properties(onnx_proto PROPERTIES VERSION ${ONNX_VERSION}
-+ SOVERSION ${ONNX_VERSION})
-+add_library(onnx SHARED ${ONNX_SRCS} ${ONNX_PROTO_SRCS})
- add_dependencies(onnx onnx_proto)
- set_source_files_properties(${ONNX_SRCS} PROPERTIES CXX_VISIBILITY_PRESET hidden)
- add_onnx_global_defines(onnx)
--
-+set_target_properties(onnx PROPERTIES VERSION ${ONNX_VERSION}
-+ SOVERSION ${ONNX_VERSION})
- if(ONNX_BUILD_PYTHON)
- # find system nanobind
- if(NOT DEFINED nanobind_DIR OR "${nanobind_DIR}" STREQUAL "")
- execute_process(
-- COMMAND ${Python_EXECUTABLE} -m nanobind --cmake-dir
-+ COMMAND ${Python_EXECUTABLE} -m nanobind --cmake_dir
- RESULT_VARIABLE NANOBIND_CMAKE_RESULT
- OUTPUT_VARIABLE NANOBIND_CMAKE_DIR
- OUTPUT_STRIP_TRAILING_WHITESPACE
-@@ -491,6 +493,16 @@ if(ONNX_INSTALL)
- DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
- FILES_MATCHING
- PATTERN "*.h")
-+ install(DIRECTORY ${ONNX_ROOT}/onnx
-+ DESTINATION "${PY_SITEARCH}"
-+ FILES_MATCHING
-+ PATTERN "*.py"
-+ PATTERN "test/*" EXCLUDE )
-+ install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/onnx
-+ DESTINATION "${PY_SITEARCH}"
-+ FILES_MATCHING
-+ PATTERN "*.py"
-+ PATTERN "test/*" EXCLUDE )
- endif()
-
- configure_file(
-@@ -544,6 +556,9 @@ if(ONNX_INSTALL)
- install(TARGETS
- onnx onnx_proto
- EXPORT ONNXTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
-+ install(TARGETS
-+ onnx_cpp2py_export
-+ DESTINATION "${PY_SITEARCH}/onnx")
-
- install(EXPORT ONNXTargets
- DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/ONNX"
---
-2.54.0
-
diff --git a/0001-Build-shared-libraries.patch b/0001-Build-shared-libraries.patch
new file mode 100644
index 0000000..555e9a3
--- /dev/null
+++ b/0001-Build-shared-libraries.patch
@@ -0,0 +1,35 @@
+From db66f3612f159bdaf01b374493484e193e79d459 Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:01:44 -0400
+Subject: [PATCH 1/7] Build shared libraries
+
+This patch is part of the "Build shared libraries and fix install location" patch by Alejandro Alvarez Ayllon <a.alvarezayllon@gmail.com> - Sat, 24 Feb 2024 14:48:45 +0100
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 044996e1b..2d8ea3df0 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -414,14 +414,16 @@ if(ONNX_USE_LITE_PROTO)
+ set(LINKED_PROTOBUF_TARGET protobuf::libprotobuf-lite)
+ endif()
+ endif()
+-add_library(onnx_proto ${ONNX_PROTO_SRCS})
++add_library(onnx_proto SHARED ${ONNX_PROTO_SRCS})
+ add_onnx_global_defines(onnx_proto)
+-
+-add_library(onnx ${ONNX_SRCS} ${ONNX_PROTO_SRCS})
++set_target_properties(onnx_proto PROPERTIES VERSION ${ONNX_VERSION}
++ SOVERSION ${ONNX_VERSION})
++add_library(onnx SHARED ${ONNX_SRCS} ${ONNX_PROTO_SRCS})
+ add_dependencies(onnx onnx_proto)
+ set_source_files_properties(${ONNX_SRCS} PROPERTIES CXX_VISIBILITY_PRESET hidden)
+ add_onnx_global_defines(onnx)
+-
++set_target_properties(onnx PROPERTIES VERSION ${ONNX_VERSION}
++ SOVERSION ${ONNX_VERSION})
+ if(ONNX_BUILD_PYTHON)
+ # find system nanobind
+ if(NOT DEFINED nanobind_DIR OR "${nanobind_DIR}" STREQUAL "")
+--
+2.54.0
+
diff --git a/0002-Fix-install-location.patch b/0002-Fix-install-location.patch
new file mode 100644
index 0000000..96cfff4
--- /dev/null
+++ b/0002-Fix-install-location.patch
@@ -0,0 +1,41 @@
+From aae958b4505f060dc58d15e41599d5eff1b9eee2 Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:02:39 -0400
+Subject: [PATCH 2/7] Fix install location
+
+This patch is part of the "Build shared libraries and fix install location" patch by Alejandro Alvarez Ayllon <a.alvarezayllon@gmail.com> - Sat, 24 Feb 2024 14:48:45 +0100
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 2d8ea3df0..094078160 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -493,6 +493,16 @@ if(ONNX_INSTALL)
+ DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
+ FILES_MATCHING
+ PATTERN "*.h")
++ install(DIRECTORY ${ONNX_ROOT}/onnx
++ DESTINATION "${PY_SITEARCH}"
++ FILES_MATCHING
++ PATTERN "*.py"
++ PATTERN "test/*" EXCLUDE )
++ install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/onnx
++ DESTINATION "${PY_SITEARCH}"
++ FILES_MATCHING
++ PATTERN "*.py"
++ PATTERN "test/*" EXCLUDE )
+ endif()
+
+ configure_file(
+@@ -546,6 +556,9 @@ if(ONNX_INSTALL)
+ install(TARGETS
+ onnx onnx_proto
+ EXPORT ONNXTargets DESTINATION ${CMAKE_INSTALL_LIBDIR})
++ install(TARGETS
++ onnx_cpp2py_export
++ DESTINATION "${PY_SITEARCH}/onnx")
+
+ install(EXPORT ONNXTargets
+ DESTINATION "${CMAKE_INSTALL_LIBDIR}/cmake/ONNX"
+--
+2.54.0
+
diff --git a/0002-Remove-python-parameterized-dependency.patch b/0002-Remove-python-parameterized-dependency.patch
deleted file mode 100644
index b111cab..0000000
--- a/0002-Remove-python-parameterized-dependency.patch
+++ /dev/null
@@ -1,5739 +0,0 @@
-From 231b8e5093c24cdc6242d2c8565c1adb526eeba7 Mon Sep 17 00:00:00 2001
-From: Diego Herrera <dherrera@redhat.com>
-Date: Fri, 29 May 2026 18:47:26 -0400
-Subject: [PATCH 2/2] Remove python-parameterized dependency
-
-
-diff --git a/onnx/test/basic_test.py b/onnx/test/basic_test.py
-index 9713e4d1f..6d6c6c90e 100644
---- a/onnx/test/basic_test.py
-+++ b/onnx/test/basic_test.py
-@@ -11,7 +11,6 @@ import unittest
-
- import google.protobuf.message
- import google.protobuf.text_format
--import parameterized
-
- import onnx
- from onnx import serialization
-@@ -34,97 +33,6 @@ def _simple_tensor() -> onnx.TensorProto:
- )
-
-
--@parameterized.parameterized_class(
-- [
-- {"format": "protobuf"},
-- {"format": "textproto"},
-- {"format": "json"},
-- {"format": "onnxtxt"},
-- ]
--)
--class TestIO(unittest.TestCase):
-- format: str
--
-- def test_load_model_when_input_is_bytes(self) -> None:
-- proto = _simple_model()
-- proto_string = serialization.registry.get(self.format).serialize_proto(proto)
-- loaded_proto = onnx.load_model_from_string(proto_string, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_model_when_input_has_read_function(self) -> None:
-- proto = _simple_model()
-- # When the proto is a bytes representation provided to `save_model`,
-- # it should always be a serialized binary protobuf representation. Aka. format="protobuf"
-- # The saved file format is specified by the `format` argument.
-- proto_string = serialization.registry.get("protobuf").serialize_proto(proto)
-- f = io.BytesIO()
-- onnx.save_model(proto_string, f, format=self.format)
-- loaded_proto = onnx.load_model(io.BytesIO(f.getvalue()), format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_model_when_input_is_file_name(self) -> None:
-- proto = _simple_model()
-- with tempfile.TemporaryDirectory() as temp_dir:
-- model_path = os.path.join(temp_dir, "model.onnx")
-- onnx.save_model(proto, model_path, format=self.format)
-- loaded_proto = onnx.load_model(model_path, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_model_when_input_is_pathlike(self) -> None:
-- proto = _simple_model()
-- with tempfile.TemporaryDirectory() as temp_dir:
-- model_path = pathlib.Path(temp_dir, "model.onnx")
-- onnx.save_model(proto, model_path, format=self.format)
-- loaded_proto = onnx.load_model(model_path, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
--
--@parameterized.parameterized_class(
-- [
-- {"format": "protobuf"},
-- {"format": "textproto"},
-- {"format": "json"},
-- # The onnxtxt format does not support saving/loading tensors yet
-- ]
--)
--class TestIOTensor(unittest.TestCase):
-- """Test loading and saving of TensorProto."""
--
-- format: str
--
-- def test_load_tensor_when_input_is_bytes(self) -> None:
-- proto = _simple_tensor()
-- proto_string = serialization.registry.get(self.format).serialize_proto(proto)
-- loaded_proto = onnx.load_tensor_from_string(proto_string, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_tensor_when_input_has_read_function(self) -> None:
-- # Test if input has a read function
-- proto = _simple_tensor()
-- f = io.BytesIO()
-- onnx.save_tensor(proto, f, format=self.format)
-- loaded_proto = onnx.load_tensor(io.BytesIO(f.getvalue()), format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_tensor_when_input_is_file_name(self) -> None:
-- # Test if input is a file name
-- proto = _simple_tensor()
-- with tempfile.TemporaryDirectory() as temp_dir:
-- model_path = os.path.join(temp_dir, "model.onnx")
-- onnx.save_tensor(proto, model_path, format=self.format)
-- loaded_proto = onnx.load_tensor(model_path, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
-- def test_save_and_load_tensor_when_input_is_pathlike(self) -> None:
-- # Test if input is a file name
-- proto = _simple_tensor()
-- with tempfile.TemporaryDirectory() as temp_dir:
-- model_path = pathlib.Path(temp_dir, "model.onnx")
-- onnx.save_tensor(proto, model_path, format=self.format)
-- loaded_proto = onnx.load_tensor(model_path, format=self.format)
-- self.assertEqual(proto, loaded_proto)
--
--
- class TestSaveAndLoadFileExtensions(unittest.TestCase):
- def test_save_model_picks_correct_format_from_extension(self) -> None:
- proto = _simple_model()
-diff --git a/onnx/test/helper_test.py b/onnx/test/helper_test.py
-index 27793f561..91f7739aa 100644
---- a/onnx/test/helper_test.py
-+++ b/onnx/test/helper_test.py
-@@ -12,7 +12,6 @@ from typing import Any
- import ml_dtypes
- import numpy as np
- import numpy.typing as npt
--import parameterized
- import pytest
-
- from onnx import (
-@@ -636,63 +635,6 @@ class TestHelperTensorFunctions(unittest.TestCase):
- expected.view(np.uint8),
- )
-
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT4, TensorProto.INT4),
-- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
-- )
-- )
-- def test_make_4bit_tensor(self, dtype, dims) -> None:
-- type_range = {
-- TensorProto.UINT4: (0, 15),
-- TensorProto.INT4: (-8, 7),
-- }
-- data = np.random.randint(
-- type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
-- )
-- y = helper.make_tensor("y", dtype, data.shape, data)
--
-- # Check the expected size of int32_data in bytes
-- expected_data_size = math.ceil(np.prod(data.shape) / 2.0)
-- actual_data_size = len(bytes(y.int32_data))
-- np.testing.assert_equal(actual_data_size, expected_data_size)
--
-- # Check the expected data values.
-- ynp = numpy_helper.to_array(y)
-- np.testing.assert_equal(ynp, data)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
-- )
-- )
-- def test_4bit_tensor_size(self, dims) -> None:
-- # A bug caused negative int4 values to inflate tensor size.
-- # So, test negative values here.
-- num_elems = np.prod(dims)
-- data = np.array([-4] * num_elems, dtype=np.int8).reshape(dims)
-- y = helper.make_tensor("y", TensorProto.INT4, data.shape, data)
--
-- # Check the expected size of int32_data in bytes
-- expected_data_size = math.ceil(num_elems / 2.0)
-- actual_data_size = len(bytes(y.int32_data))
-- np.testing.assert_equal(actual_data_size, expected_data_size)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT4, TensorProto.INT4), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
-- )
-- )
-- def test_make_4bit_raw_tensor(self, dtype, dims) -> None:
-- data = np.random.randint(0, high=16, size=dims, dtype=np.uint8)
-- packed_data = _pack_4bit(data)
--
-- y = helper.make_tensor(
-- "packed_int4", dtype, dims, packed_data.tobytes(), raw=True
-- )
-- ynp = numpy_helper.to_array(y)
-- np.testing.assert_equal(ynp.view(np.uint8), data)
--
- def test_make_float4e2m1_raw_tensor(self) -> None:
- data = np.array([0, 0.5, 1, 240, 10, -2], dtype=ml_dtypes.float4_e2m1fn)
- expected = data.view(np.uint8)
-@@ -707,63 +649,6 @@ class TestHelperTensorFunctions(unittest.TestCase):
- ynp = numpy_helper.to_array(y)
- np.testing.assert_equal(ynp.view(np.uint8), expected)
-
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT2, TensorProto.INT2),
-- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
-- )
-- )
-- def test_make_2bit_tensor(self, dtype, dims) -> None:
-- type_range = {
-- TensorProto.UINT2: (0, 3),
-- TensorProto.INT2: (-2, 1),
-- }
-- data = np.random.randint(
-- type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
-- )
-- y = helper.make_tensor("y", dtype, data.shape, data)
--
-- # Check the expected size of int32_data in bytes
-- expected_data_size = math.ceil(np.prod(data.shape) / 4.0)
-- actual_data_size = len(bytes(y.int32_data))
-- np.testing.assert_equal(actual_data_size, expected_data_size)
--
-- # Check the expected data values.
-- ynp = numpy_helper.to_array(y)
-- np.testing.assert_equal(ynp, data)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
-- )
-- )
-- def test_2bit_tensor_size(self, dims) -> None:
-- # A bug caused negative int2 values to inflate tensor size.
-- # So, test negative values here.
-- num_elems = np.prod(dims)
-- data = np.array([-2] * num_elems, dtype=np.int8).reshape(dims)
-- y = helper.make_tensor("y", TensorProto.INT2, data.shape, data)
--
-- # Check the expected size of int32_data in bytes
-- expected_data_size = math.ceil(num_elems / 4.0)
-- actual_data_size = len(bytes(y.int32_data))
-- np.testing.assert_equal(actual_data_size, expected_data_size)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT2, TensorProto.INT2), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
-- )
-- )
-- def test_make_2bit_raw_tensor(self, dtype, dims) -> None:
-- data = np.random.randint(0, high=4, size=dims, dtype=np.uint8)
-- packed_data = _pack_2bit(data)
--
-- y = helper.make_tensor(
-- "packed_int2", dtype, dims, packed_data.tobytes(), raw=True
-- )
-- ynp = numpy_helper.to_array(y)
-- np.testing.assert_equal(ynp.view(np.uint8), data)
--
- def test_make_float4e2m1_tensor(self) -> None:
- y = helper.make_tensor(
- "zero_point",
-@@ -1105,28 +990,6 @@ class TestHelperMappingFunctions(unittest.TestCase):
-
-
- class TestAttrTypeToStr(unittest.TestCase):
-- @parameterized.parameterized.expand(
-- [
-- (AttributeProto.AttributeType.FLOAT, "FLOAT"),
-- (AttributeProto.AttributeType.INT, "INT"),
-- (AttributeProto.AttributeType.STRING, "STRING"),
-- (AttributeProto.AttributeType.TENSOR, "TENSOR"),
-- (AttributeProto.AttributeType.GRAPH, "GRAPH"),
-- (AttributeProto.AttributeType.SPARSE_TENSOR, "SPARSE_TENSOR"),
-- (AttributeProto.AttributeType.TYPE_PROTO, "TYPE_PROTO"),
-- (AttributeProto.AttributeType.FLOATS, "FLOATS"),
-- (AttributeProto.AttributeType.INTS, "INTS"),
-- (AttributeProto.AttributeType.STRINGS, "STRINGS"),
-- (AttributeProto.AttributeType.TENSORS, "TENSORS"),
-- (AttributeProto.AttributeType.GRAPHS, "GRAPHS"),
-- (AttributeProto.AttributeType.SPARSE_TENSORS, "SPARSE_TENSORS"),
-- (AttributeProto.AttributeType.TYPE_PROTOS, "TYPE_PROTOS"),
-- ]
-- )
-- def test_attr_type_to_str(self, attr_type, expected_str):
-- result = helper._attr_type_to_str(attr_type)
-- self.assertEqual(result, expected_str)
--
- def test_attr_type_to_str_undefined(self):
- result = helper._attr_type_to_str(9999)
- self.assertEqual(result, "UNDEFINED")
-diff --git a/onnx/test/node_shape_inference_test.py b/onnx/test/node_shape_inference_test.py
-index ece776e48..63252b922 100644
---- a/onnx/test/node_shape_inference_test.py
-+++ b/onnx/test/node_shape_inference_test.py
-@@ -5,34 +5,9 @@ from __future__ import annotations
-
- import unittest
-
--import parameterized
--
- import onnx.helper
- import onnx.shape_inference
-
-
--class NodeInferenceTest(unittest.TestCase):
-- @parameterized.parameterized.expand(
-- [
-- ("GreaterOrEqual",),
-- ("LessOrEqual",),
-- ]
-- )
-- def test_comparison_op(self, op_type):
-- node = onnx.helper.make_node(op_type, ["x", "y"], ["z"])
-- schema = onnx.defs.get_schema(node.op_type, 23, "")
-- xtype = onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT32, [1, 10])
-- ytype = onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT32, [10, 1])
-- result = onnx.shape_inference.infer_node_outputs(
-- schema, node, {"x": xtype, "y": ytype}
-- )
-- self.assertEqual(list(result.keys()), ["z"])
-- self.assertEqual(result["z"].tensor_type.elem_type, onnx.TensorProto.BOOL)
-- self.assertEqual(
-- [dim.dim_value for dim in result["z"].tensor_type.shape.dim],
-- [10, 10],
-- )
--
--
- if __name__ == "__main__":
- unittest.main()
-diff --git a/onnx/test/numpy_helper_test.py b/onnx/test/numpy_helper_test.py
-index 568000550..2265008d2 100644
---- a/onnx/test/numpy_helper_test.py
-+++ b/onnx/test/numpy_helper_test.py
-@@ -6,7 +6,6 @@ from __future__ import annotations
- import unittest
-
- import numpy as np
--import parameterized
-
- import onnx
- import onnx.reference
-@@ -148,38 +147,6 @@ class TestNumpyHelper(unittest.TestCase):
- self.assertEqual(tp.SerializeToString(), again.SerializeToString())
- self.assertEqual(tp.data_type, helper.np_dtype_to_tensor_dtype(back.dtype))
-
-- @parameterized.parameterized.expand(
-- [
-- ("FLOAT", onnx.TensorProto.FLOAT),
-- ("UINT8", onnx.TensorProto.UINT8),
-- ("INT8", onnx.TensorProto.INT8),
-- ("UINT16", onnx.TensorProto.UINT16),
-- ("INT16", onnx.TensorProto.INT16),
-- ("INT32", onnx.TensorProto.INT32),
-- ("INT64", onnx.TensorProto.INT64),
-- ("BOOL", onnx.TensorProto.BOOL),
-- ("FLOAT16", onnx.TensorProto.FLOAT16),
-- ("DOUBLE", onnx.TensorProto.DOUBLE),
-- ("UINT32", onnx.TensorProto.UINT32),
-- ("UINT64", onnx.TensorProto.UINT64),
-- ("COMPLEX64", onnx.TensorProto.COMPLEX64),
-- ("COMPLEX128", onnx.TensorProto.COMPLEX128),
-- ("BFLOAT16", onnx.TensorProto.BFLOAT16),
-- ("FLOAT8E4M3FN", onnx.TensorProto.FLOAT8E4M3FN),
-- ("FLOAT8E4M3FNUZ", onnx.TensorProto.FLOAT8E4M3FNUZ),
-- ("FLOAT8E5M2", onnx.TensorProto.FLOAT8E5M2),
-- ("FLOAT8E5M2FNUZ", onnx.TensorProto.FLOAT8E5M2FNUZ),
-- ("FLOAT8E8M0", onnx.TensorProto.FLOAT8E8M0),
-- ("UINT4", onnx.TensorProto.UINT4),
-- ("INT4", onnx.TensorProto.INT4),
-- ("UINT2", onnx.TensorProto.UINT2),
-- ("INT2", onnx.TensorProto.INT2),
-- ("FLOAT4E2M1", onnx.TensorProto.FLOAT4E2M1),
-- ]
-- )
-- def test_to_array_from_array(self, _: str, data_type: onnx.TensorProto.DataType):
-- self._to_array_from_array(data_type)
--
- def test_to_array_from_array_string(self):
- self._to_array_from_array(onnx.TensorProto.STRING, False)
-
-diff --git a/onnx/test/parser_test.py b/onnx/test/parser_test.py
-index 19ee40792..9e22f19ae 100644
---- a/onnx/test/parser_test.py
-+++ b/onnx/test/parser_test.py
-@@ -5,8 +5,6 @@ from __future__ import annotations
-
- import unittest
-
--from parameterized import parameterized
--
- import onnx
- from onnx import GraphProto, OperatorSetIdProto, TensorProto, checker
-
-@@ -120,94 +118,6 @@ class TestBasicFunctions(unittest.TestCase):
- model = onnx.parser.parse_model(input)
- checker.check_model(model)
-
-- @parameterized.expand(
-- [
-- (
-- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu(x) }",
-- {},
-- ),
-- (
-- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0>(x) }",
-- {"alpha": 2.0},
-- ),
-- (
-- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<gamma=3.0>(x) }",
-- {"gamma": 3.0},
-- ),
-- (
-- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x) }",
-- {"alpha": 2.0, "gamma": 3.0},
-- ),
-- ]
-- )
-- def test_composite_parse_function_with_attributes(
-- self, graph_text: str, expected_attribute: dict
-- ) -> None:
-- default_alpha = 1.67326319217681884765625
-- default_gamma = 1.05070102214813232421875
--
-- def expect_custom_node_attribute(node, attributes):
-- for key in attributes:
-- match_attr = [attr for attr in node.attribute if attr.name == key]
-- assert len(match_attr) == 1
-- assert match_attr[0].f == attributes[key]
--
-- def expect_model_function_attribute(model):
-- assert len(model.functions[0].attribute_proto) == 2
-- attr_proto_alpha = [
-- attr_proto
-- for attr_proto in model.functions[0].attribute_proto
-- if attr_proto.name == "alpha"
-- ]
-- assert len(attr_proto_alpha) == 1 and attr_proto_alpha[0].f == default_alpha
-- attr_proto_gamma = [
-- attr_proto
-- for attr_proto in model.functions[0].attribute_proto
-- if attr_proto.name == "gamma"
-- ]
-- assert len(attr_proto_gamma) == 1 and attr_proto_gamma[0].f == default_gamma
--
-- function_text = f"""
-- <
-- domain: "custom_domain",
-- opset_import: [ "" : 15],
-- doc_string: "Test function proto"
-- >
-- Selu
-- <alpha: float={default_alpha}, gamma: float={default_gamma}>
-- (X) => (C)
-- {{
-- constant_alpha = Constant<value_float: float=@alpha>()
-- constant_gamma = Constant<value_float: float=@gamma>()
-- alpha_x = CastLike(constant_alpha, X)
-- gamma_x = CastLike(constant_gamma, X)
-- exp_x = Exp(X)
-- alpha_x_exp_x = Mul(alpha_x, exp_x)
-- alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
-- neg = Mul(gamma_x, alpha_x_exp_x_)
-- pos = Mul(gamma_x, X)
-- _zero = Constant<value_float=0.0>()
-- zero = CastLike(_zero, X)
-- less_eq = LessOrEqual(X, zero)
-- C = Where(less_eq, neg, pos)
-- }}
-- """
--
-- functions = [onnx.parser.parse_function(function_text)]
-- graph = onnx.parser.parse_graph(graph_text)
-- opset_imports = [
-- OperatorSetIdProto(domain="", version=15),
-- OperatorSetIdProto(domain="custom_domain", version=1),
-- ]
--
-- model = onnx.helper.make_model(
-- graph, functions=functions, opset_imports=opset_imports
-- )
-- checker.check_model(model)
--
-- expect_model_function_attribute(model)
-- expect_custom_node_attribute(model.graph.node[0], expected_attribute)
--
- def test_parse_node(self):
- node = onnx.parser.parse_node(
- "out1, out2 = SomeDomain.SomeOp <attr1 = 1> (in1, in2)"
-@@ -242,101 +152,6 @@ class TestBasicFunctions(unittest.TestCase):
- self.assertEqual(list(node.input), ["", "y"])
- self.assertEqual(list(node.output), ["", "x"])
-
-- @parameterized.expand(
-- [
-- ("not_a_good_float", True),
-- ("inf1", True),
-- ("-inf1", True),
-- ("nan0", True),
-- ("-nan0", True),
-- ("naninf", True),
-- ("inf", False),
-- ("-inf", False),
-- ("infinity", False),
-- ("-infinity", False),
-- ("nan", False),
-- ("-NaN", False),
-- ]
-- )
-- def test_parse_various_float_values(self, test_literal, expect_exception):
-- model_text = f"""
-- <
-- ir_version: 8,
-- opset_import: ["" : 18, "this" : 1],
-- producer_name: "FunctionProtoTest",
-- producer_version: "1.0"
-- >
-- _func () => ()
-- {{
-- tmp = Constant <value_float = {test_literal}>()
-- }}
-- """
-- if expect_exception:
-- self.assertRaises(
-- onnx.parser.ParseError, lambda: onnx.parser.parse_model(model_text)
-- )
-- else:
-- model = onnx.parser.parse_model(model_text)
-- self.assertEqual(model.ir_version, 8)
-- self.assertEqual(model.producer_name, "FunctionProtoTest")
-- self.assertEqual(model.producer_version, "1.0")
-- self.assertEqual(len(model.graph.node), 1)
-- self.assertEqual(len(model.graph.node[0].attribute), 1)
-- self.assertEqual(model.graph.node[0].attribute[0].name, "value_float")
-- self.assertEqual(
-- model.graph.node[0].attribute[0].type, onnx.AttributeProto.FLOAT
-- )
-- self.assertEqual(
-- str(model.graph.node[0].attribute[0].f), str(float(test_literal))
-- )
--
-- @parameterized.expand(
-- [
-- ("bfloat16", TensorProto.BFLOAT16),
-- ("bool", TensorProto.BOOL),
-- ("complex64", TensorProto.COMPLEX64),
-- ("complex128", TensorProto.COMPLEX128),
-- ("double", TensorProto.DOUBLE),
-- ("float16", TensorProto.FLOAT16),
-- ("float", TensorProto.FLOAT),
-- ("float8e4m3fn", TensorProto.FLOAT8E4M3FN),
-- ("float8e4m3fnuz", TensorProto.FLOAT8E4M3FNUZ),
-- ("float8e5m2", TensorProto.FLOAT8E5M2),
-- ("float8e5m2fnuz", TensorProto.FLOAT8E5M2FNUZ),
-- ("int2", TensorProto.INT2),
-- ("int4", TensorProto.INT4),
-- ("int8", TensorProto.INT8),
-- ("int16", TensorProto.INT16),
-- ("int32", TensorProto.INT32),
-- ("int64", TensorProto.INT64),
-- ("string", TensorProto.STRING),
-- ("uint2", TensorProto.UINT2),
-- ("uint4", TensorProto.UINT4),
-- ("uint8", TensorProto.UINT8),
-- ("uint16", TensorProto.UINT16),
-- ("uint32", TensorProto.UINT32),
-- ("uint64", TensorProto.UINT64),
-- ("float4e2m1", TensorProto.FLOAT4E2M1),
-- ]
-- )
-- def test_parse_graph_types(self, name, itype) -> None:
-- w = '{"0"}' if itype == TensorProto.STRING else "{0}"
-- text_graph = f"""
-- <
-- ir_version: 10,
-- opset_import: [ "" : 19]
-- >
-- agraph (float[N] X) => ({name}[N] C)
-- <
-- {name}[1] weight = {w}
-- >
-- {{
-- C = Cast<to={itype}>(X)
-- }}
-- """
-- graph = onnx.parser.parse_model(text_graph)
-- self.assertEqual(len(graph.graph.node), 1)
--
-
- if __name__ == "__main__":
- unittest.main(verbosity=2)
-diff --git a/onnx/test/reference_evaluator_ml_test.py b/onnx/test/reference_evaluator_ml_test.py
-index 7dad96703..1c4276482 100644
---- a/onnx/test/reference_evaluator_ml_test.py
-+++ b/onnx/test/reference_evaluator_ml_test.py
-@@ -11,7 +11,6 @@ import unittest
-
- import numpy as np
- from numpy.testing import assert_allclose
--from parameterized import parameterized
-
- import onnx
- from onnx import ONNX_ML, TensorProto, TypeProto, ValueInfoProto
-@@ -893,118 +892,6 @@ class TestReferenceEvaluatorAiOnnxMl(unittest.TestCase):
- onnx.checker.check_model(model)
- return model
-
-- @parameterized.expand(
-- tuple(
-- itertools.chain.from_iterable(
-- (
-- (
-- AggregationFunction.SUM if opset5 else "SUM",
-- np.array(
-- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- (
-- AggregationFunction.AVERAGE if opset5 else "AVERAGE",
-- np.array(
-- [[0.288462], [0.288462], [0.288462]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- (
-- AggregationFunction.MIN if opset5 else "MIN",
-- np.array(
-- [[0.076923], [0.076923], [0.076923]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- (
-- AggregationFunction.MAX if opset5 else "MAX",
-- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
-- opset5,
-- ),
-- )
-- for opset5 in [True, False]
-- )
-- )
-- )
-- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
-- def test_tree_ensemble_regressor_aggregation_functions(
-- self, aggregate_function, expected_result, opset5
-- ):
-- x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
-- model_factory = (
-- self._get_test_tree_ensemble_opset_latest
-- if opset5
-- else self._get_test_tree_ensemble_regressor
-- )
-- model_proto = model_factory(
-- aggregate_function,
-- )
-- sess = ReferenceEvaluator(model_proto)
-- (actual,) = sess.run(None, {"X": x})
-- assert_allclose(actual, expected_result, atol=1e-6)
--
-- @parameterized.expand(
-- tuple(
-- itertools.chain.from_iterable(
-- (
-- (
-- Mode.LEQ if opset5 else "BRANCH_LEQ",
-- np.array(
-- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- (
-- Mode.GT if opset5 else "BRANCH_GT",
-- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
-- opset5,
-- ),
-- (
-- Mode.LT if opset5 else "BRANCH_LT",
-- np.array(
-- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- (
-- Mode.GTE if opset5 else "BRANCH_GTE",
-- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
-- opset5,
-- ),
-- (
-- Mode.EQ if opset5 else "BRANCH_EQ",
-- np.array([[1.0], [1.0], [1.0]], dtype=np.float32),
-- opset5,
-- ),
-- (
-- Mode.NEQ if opset5 else "BRANCH_NEQ",
-- np.array(
-- [[0.076923], [0.076923], [0.076923]], dtype=np.float32
-- ),
-- opset5,
-- ),
-- )
-- for opset5 in [True, False]
-- )
-- )
-- )
-- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
-- def test_tree_ensemble_regressor_rule(self, rule, expected, opset5):
-- x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
-- model_factory = (
-- self._get_test_tree_ensemble_opset_latest
-- if opset5
-- else self._get_test_tree_ensemble_regressor
-- )
-- aggregate_function = AggregationFunction.SUM if opset5 else "SUM"
--
-- model_proto = model_factory(aggregate_function, rule)
-- sess = ReferenceEvaluator(model_proto)
-- (actual,) = sess.run(None, {"X": x})
-- assert_allclose(actual, expected, atol=1e-6)
--
- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
- def test_tree_ensemble_regressor_2_targets_opset3(self):
- X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
-@@ -1118,23 +1005,6 @@ class TestReferenceEvaluatorAiOnnxMl(unittest.TestCase):
- assert_allclose(got[0], expected, atol=1e-6)
- self.assertIn("op_type=TreeEnsembleRegressor", str(sess.rt_nodes_[0]))
-
-- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
-- @parameterized.expand(
-- [(input_type,) for input_type in [TensorProto.FLOAT, TensorProto.DOUBLE]]
-- )
-- def test_tree_ensemble_missing_opset5(self, input_type):
-- model = self._get_test_tree_ensemble_opset_latest(
-- AggregationFunction.SUM, Mode.LEQ, True, input_type
-- )
-- np_dtype = onnx.helper.tensor_dtype_to_np_dtype(input_type)
-- x = np.arange(9).reshape((-1, 3)).astype(np_dtype) / 10 - 0.5
-- x[2, 0] = 5
-- x[1, :] = np.nan
-- expected = np.array([[100001.0], [100100.0], [100100.0]], dtype=np_dtype)
-- session = ReferenceEvaluator(model)
-- (actual,) = session.run(None, {"X": x})
-- assert_allclose(actual, expected, atol=1e-6)
--
- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
- def test_tree_ensemble_regressor_missing_opset5_float16(self):
- model = self._get_test_tree_ensemble_opset_latest(
-diff --git a/onnx/test/reference_evaluator_test.py b/onnx/test/reference_evaluator_test.py
-index 85cc4cc0e..d0a255fc8 100644
---- a/onnx/test/reference_evaluator_test.py
-+++ b/onnx/test/reference_evaluator_test.py
-@@ -25,7 +25,6 @@ from typing import TYPE_CHECKING
-
- import ml_dtypes
- import numpy as np
--import parameterized
- import version_utils
- from numpy.testing import assert_allclose
-
-@@ -2998,163 +2997,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- # model
- return make_model(graph, opset_imports=[make_opsetid("", opset)])
-
-- @parameterized.parameterized.expand(
-- itertools.product(
-- [
-- (
-- "ReduceMin",
-- [
-- np.array(
-- [[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
-- ),
-- np.array([[2, 15], [10, 4]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceL1",
-- [
-- np.array(
-- [[2.2367053, 2.3516612], [4.076292, 4.2970634]],
-- dtype=np.float32,
-- ),
-- np.array([[18, 6], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceL2",
-- [
-- np.array(
-- [[1.80155, 1.8169948], [2.9928076, 3.1205883]],
-- dtype=np.float32,
-- ),
-- np.array([[11, 18], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceLogSum",
-- [
-- np.array(
-- [[0.9497848, 1.1872643], [1.6764175, 1.70759]],
-- dtype=np.float32,
-- ),
-- np.array([[6, 18], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceLogSumExp",
-- [
-- np.array(
-- [[1.6005973, 1.7445935], [2.5616229, 2.6539795]],
-- dtype=np.float32,
-- ),
-- np.array([[13, 6], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceMax",
-- [
-- np.array(
-- [[1.4217108, 1.5069536], [2.453826, 2.5041783]],
-- dtype=np.float32,
-- ),
-- np.array([[13, 11], [13, 11]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceMean",
-- [
-- np.array(
-- [[0.39247903, 0.78497636], [2.038146, 2.1485317]],
-- dtype=np.float32,
-- ),
-- np.array([[13, 6], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceSumSquare",
-- [
-- np.array(
-- [[3.2455828, 3.3014696], [8.956896, 9.7380705]],
-- dtype=np.float32,
-- ),
-- np.array([[11, 18], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- (
-- "ReduceProd",
-- [
-- np.array(
-- [[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
-- ),
-- np.array([[2, 15], [13, 6]], dtype=np.int64),
-- ],
-- ),
-- ],
-- [17, 18],
-- )
-- )
-- def test_op_reduce(self, reduce_op_expected, opset: int):
-- reduce_op, expected = reduce_op_expected
-- X = np.arange(8).reshape((-1, 4)).astype(np.float32)
--
-- results = {}
--
-- model = self._cdist_model(opset, reduce_op)
-- sess = ReferenceEvaluator(model)
-- got = sess.run(None, {"input": X})
-- results["ref", opset] = got
--
-- cl = [
-- n
-- for n in sess.rt_nodes_[0].body.rt_nodes_
-- if n.__class__.__name__.startswith(reduce_op)
-- ]
-- schema = cl[0]._schema
-- new_cl = type(reduce_op, (cl[0].__class__,), {"op_schema": schema})
-- sess = ReferenceEvaluator(model, new_ops=[new_cl])
-- got = sess.run(None, {"input": X})
-- results["ref_cl", opset] = got
--
-- baseline = "constant"
-- for k, v in results.items():
-- for a, b in zip(reversed(expected), reversed(v), strict=True):
-- if a.shape != b.shape:
-- raise AssertionError(
-- f"Shape mismatch for {reduce_op!r}, {baseline}:{a.shape} != {k}:{b.shape}."
-- )
-- diff = np.abs(a - b).max()
-- if diff > 1e-6:
-- raise AssertionError(
-- f"Discrepancies (max={diff}) for {reduce_op!r}, {baseline} != {k}\n{a}\n!=\n{b}"
-- )
--
-- @parameterized.parameterized.expand(
-- [
-- (13,),
-- (17,),
-- (18,),
-- ]
-- )
-- def test_mvn(self, opset: int, ref_opset: int = 13):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
-- nodes = [
-- make_node("MeanVarianceNormalization", ["X"], ["Y"]),
-- ]
-- graph = make_graph(nodes, "g", [X], [Y])
-- x = np.random.rand(3, 3, 3, 1).astype(np.float32)
--
-- onnx_model = make_model(graph, opset_imports=[make_opsetid("", opset)])
-- ref = ReferenceEvaluator(onnx_model)
-- got = ref.run(None, {"X": x})[0]
--
-- ref_onnx_model = make_model(graph, opset_imports=[make_opsetid("", ref_opset)])
-- ref_expected = ReferenceEvaluator(ref_onnx_model)
-- expected = ref_expected.run(None, {"X": x})[0]
--
-- self.assertEqual(got.shape, expected.shape)
-- assert_allclose(got, expected)
--
- def test_concat_in_a_function(self):
- def create_model():
- nodes = []
-@@ -3829,211 +3671,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- got = ref.run(None, {"X": data})
- assert_allclose(got[0], expected)
-
-- @parameterized.parameterized.expand(
-- [
-- (
-- 4 * np.arange(12).reshape(3, 4),
-- np.arange(1, 7).reshape(3, 2),
-- np.zeros((3, 2)),
-- 1,
-- 2,
-- [[0, 4, 4, 6], [5, 7, 6, 7], [6, 7, 7, 7]],
-- ),
-- (
-- 4 * np.arange(12).reshape(3, 4),
-- np.arange(1, 7).reshape(3, 2),
-- np.ones((3, 2)),
-- 1,
-- 2,
-- [[1, 5, 5, 7], [6, 8, 7, 8], [7, 8, 8, 8]],
-- ),
-- (
-- np.arange(24).reshape(3, 8),
-- [[0.25, 0.5, 1], [0.25, 0.5, 1], [0.25, 0.5, 1]],
-- np.zeros((3, 3)),
-- 1,
-- 3,
-- [
-- [0, 4, 8, 6, 8, 10, 6, 7],
-- [32, 36, 40, 22, 24, 26, 14, 15],
-- [64, 68, 72, 38, 40, 42, 22, 23],
-- ],
-- ),
-- (
-- np.arange(6),
-- [0.25, 0.5],
-- [-1, -2],
-- 0,
-- 3,
-- [-1, 3, 7, 4, 6, 8],
-- ),
-- (
-- np.ones((9, 12)),
-- np.ones((3, 4)),
-- np.zeros((3, 4)),
-- 0,
-- 3,
-- None, # Blocked quantization is defined for 1-D blocks only
-- ),
-- (
-- np.ones((3, 4, 5, 6)),
-- np.ones((3, 4)),
-- np.zeros((3, 4)),
-- 2,
-- 2,
-- None, # Scale and ZP must have the same rank as the input
-- ),
-- ]
-- )
-- def test_blocked_quantize_linear(
-- self, x, scale, zero_point, axis, block_size, expected
-- ):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
-- Y = make_tensor_value_info("Y", TensorProto.INT8, [None])
--
-- scale_data = np.array(scale, dtype=np.float32)
-- zp_data = np.array(zero_point, dtype=np.int8)
-- model = make_model(
-- make_graph(
-- [
-- make_node(
-- "QuantizeLinear",
-- ["X", "scale", "zero"],
-- ["Y"],
-- axis=axis,
-- block_size=block_size,
-- ),
-- ],
-- "g",
-- [X],
-- [Y],
-- [
-- make_tensor(
-- "scale", TensorProto.FLOAT, scale_data.shape, scale_data
-- ),
-- make_tensor("zero", TensorProto.INT8, scale_data.shape, zp_data),
-- ],
-- )
-- )
-- ref = ReferenceEvaluator(model)
--
-- data = np.array(x, dtype=np.float32)
--
-- if expected is not None:
-- expected = np.array(expected, dtype=np.int8)
-- got = ref.run(None, {"X": data})
-- assert_allclose(got[0], expected)
-- else:
-- with self.assertRaises(ValueError):
-- ref.run(None, {"X": data})
--
-- @parameterized.parameterized.expand(
-- [
-- (
-- np.arange(12).reshape(3, 4),
-- np.arange(1, 7).reshape(3, 2),
-- np.zeros((3, 2)),
-- 1,
-- 2,
-- [[0, 1, 4, 6], [12, 15, 24, 28], [40, 45, 60, 66]],
-- ),
-- (
-- np.arange(12).reshape(3, 4),
-- np.arange(1, 7).reshape(3, 2),
-- np.ones((3, 2)),
-- 1,
-- 2,
-- [[-1, 0, 2, 4], [9, 12, 20, 24], [35, 40, 54, 60]],
-- ),
-- (
-- np.dstack([np.arange(4).reshape(2, 2)] * 4),
-- np.dstack([np.array([[1, 1], [2, 3]]), np.array([[4, 5], [6, 7]])]),
-- np.zeros((2, 2, 2)),
-- 2,
-- 2,
-- [[[0, 0, 0, 0], [1, 1, 5, 5]], [[4, 4, 12, 12], [9, 9, 21, 21]]],
-- ),
-- (
-- np.arange(24).reshape(3, 8),
-- [[2, 1, 3], [2, 1, 3], [2, 1, 3]],
-- np.zeros((3, 3)),
-- 1,
-- 3,
-- [
-- [0, 2, 4, 3, 4, 5, 18, 21],
-- [16, 18, 20, 11, 12, 13, 42, 45],
-- [32, 34, 36, 19, 20, 21, 66, 69],
-- ],
-- ),
-- (
-- np.arange(
-- 6,
-- ),
-- [2, 3],
-- [1, 2],
-- 0,
-- 3,
-- [-2, 0, 2, 3, 6, 9],
-- ),
-- (
-- np.ones((9, 12)),
-- np.ones((3, 4)),
-- np.zeros((3, 4)),
-- 0,
-- 3,
-- None, # Blocked quantization is defined for 1-D blocks only
-- ),
-- (
-- np.ones((3, 4, 5, 6)),
-- np.ones((3, 4)),
-- np.zeros((3, 4)),
-- 2,
-- 2,
-- None, # Scale and ZP must have the same rank as the input
-- ),
-- ]
-- )
-- def test_blocked_dequantize_linear(
-- self, x, scale, zero_point, axis, block_size, expected
-- ):
-- X = make_tensor_value_info("X", TensorProto.INT8, [None])
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
--
-- scale_data = np.array(scale, dtype=np.float32)
-- zp_data = np.array(zero_point, dtype=np.int8)
-- model = make_model(
-- make_graph(
-- [
-- make_node(
-- "DequantizeLinear",
-- ["X", "scale", "zero"],
-- ["Y"],
-- axis=axis,
-- block_size=block_size,
-- ),
-- ],
-- "g",
-- [X],
-- [Y],
-- [
-- make_tensor(
-- "scale", TensorProto.FLOAT, scale_data.shape, scale_data
-- ),
-- make_tensor("zero", TensorProto.INT8, scale_data.shape, zp_data),
-- ],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- data = np.array(x, dtype=np.int8)
--
-- if expected is not None:
-- expected = np.array(expected, dtype=np.float32)
-- got = ref.run(None, {"X": data})
-- assert_allclose(got[0], expected)
-- else:
-- with self.assertRaises(ValueError):
-- ref.run(None, {"X": data})
--
- def test_lrn(self):
- def _expected(x, alpha, beta, bias, size):
- square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
-@@ -4217,52 +3854,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- got = _conv_implementation_im2col(**feeds, **kwargs)
- assert_allclose(got, expected)
-
-- @parameterized.parameterized.expand(
-- [
-- ("ReduceSum",),
-- ("ReduceL1",),
-- ("ReduceL2",),
-- ("ReduceMin",),
-- ("ReduceMax",),
-- ("ReduceProd",),
-- ("ReduceSumSquare",),
-- ]
-- )
-- def test_reduce_op_no_axis(self, op):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, None)
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
-- data = np.arange(6).reshape((1, 3, 2)).astype(np.float32)
-- nodes = [make_node(op, ["X"], ["Y"], keepdims=0)]
-- model = make_model(make_graph(nodes, "g", [X], [Y]))
-- ref = ReferenceEvaluator(model)
-- got = ref.run(None, {"X": data})
-- r = got[0]
-- self.assertIsInstance(r, np.ndarray)
-- self.assertEqual(r.shape, ())
--
-- @parameterized.parameterized.expand([(1,), (2,), (3,), (4,), (5,), (6,)])
-- def test_pad(self, dim):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, None)
-- P = make_tensor_value_info("P", TensorProto.INT64, None)
-- V = make_tensor_value_info("V", TensorProto.FLOAT, None)
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
-- value = np.array([-5], dtype=np.float32)
--
-- node = make_node("Pad", inputs=["X", "P", "V"], outputs=["Y"], mode="constant")
-- model = make_model(make_graph([node], "g", [X, P, V], [Y]))
-- ref = ReferenceEvaluator(model)
-- x = np.array([1], dtype=np.float32).reshape((1,) * dim)
--
-- p = np.array([1, 1] * dim, dtype=np.int64)
-- got = ref.run(None, {"X": x, "P": p, "V": value})[0]
-- self.assertEqual(got.shape, (3,) * dim)
-- self.assertEqual(got.dtype, np.float32)
--
-- p = np.repeat([7, 3], dim).astype(np.int64)
-- got = ref.run(None, {"X": x, "P": p, "V": value})[0]
-- self.assertEqual(got.shape, (11,) * dim)
-- self.assertEqual(got.dtype, np.float32)
--
- def test_constant_of_shape(self):
- X = make_tensor_value_info("X", TensorProto.FLOAT, None)
- Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
-@@ -5373,124 +4964,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- for i in range(2, -1, -1):
- assert_allclose(got[i], expected[i])
-
-- @parameterized.parameterized.expand(
-- [
-- (["abc", "def"], [".com", ".net"], ["abc.com", "def.net"], (2,)),
-- (["cat", "dog", "snake"], ["s"], ["cats", "dogs", "snakes"], (3,)),
-- ("cat", "s", "cats", ()),
-- (["a", "ß", "y"], ["a", "ß", "y"], ["aa", "ßß", "yy"], (3,)),
-- ]
-- )
-- def test_string_concat(self, a, b, expected, expected_shape):
-- A = make_tensor_value_info("A", TensorProto.STRING, None)
-- B = make_tensor_value_info("B", TensorProto.STRING, None)
-- Y = make_tensor_value_info("Y", TensorProto.STRING, None)
-- node = make_node("StringConcat", inputs=["A", "B"], outputs=["Y"])
-- model = make_model(make_graph([node], "g", [A, B], [Y]))
-- ref = ReferenceEvaluator(model)
-- result, *_ = ref.run(None, {"A": np.array(a), "B": np.array(b)})
-- np.testing.assert_array_equal(result, expected)
-- self.assertIn(result.dtype.kind, {"O", "U"})
-- self.assertEqual(result.shape, expected_shape)
--
-- @parameterized.parameterized.expand(
-- [
-- (
-- ["1,2,3", "4,5,6"],
-- ",",
-- None,
-- [["1", "2", "3"], ["4", "5", "6"]],
-- [3, 3],
-- ),
-- (
-- ["1,", "4,6", ""],
-- ",",
-- None,
-- [["1", ""], ["4", "6"], ["", ""]],
-- [2, 2, 1],
-- ),
-- (
-- ["1", "4,6", "4,5,6"],
-- ",",
-- 1,
-- [["1", ""], ["4", "6"], ["4", "5,6"]],
-- [1, 2, 2],
-- ),
-- (
-- [["1,", "4,6", "4,5,6"], ["1,", "4,6", "4,5,6"]],
-- ",",
-- None,
-- [
-- [["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
-- [["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
-- ],
-- [[2, 2, 3], [2, 2, 3]],
-- ),
-- (
-- ["hello world !", " hello world !", " hello world ! "],
-- None,
-- None,
-- [
-- ["hello", "world", "!"],
-- ["hello", "world", "!"],
-- ["hello", "world", "!"],
-- ],
-- [3, 3, 3],
-- ),
-- (
-- ["hello world !", " hello world !", " hello world ! "],
-- "",
-- None,
-- [
-- ["hello", "world", "!"],
-- ["hello", "world", "!"],
-- ["hello", "world", "!"],
-- ],
-- [3, 3, 3],
-- ),
-- (
-- ["o-n-n--x-", "o-n----nx"],
-- "-",
-- None,
-- [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]],
-- [6, 6],
-- ),
-- (
-- [],
-- " ",
-- 2,
-- np.array([]).reshape((0, 0)),
-- [],
-- ),
-- ]
-- )
-- def test_string_split(
-- self,
-- x,
-- delimiter,
-- maxsplit,
-- expected_split,
-- expected_num_splits,
-- ):
-- X = make_tensor_value_info("X", TensorProto.STRING, (None))
-- Splits = make_tensor_value_info("Splits", TensorProto.STRING, (None))
-- MaxSplits = make_tensor_value_info("MaxSplits", TensorProto.INT32, (None))
-- node = make_node(
-- "StringSplit",
-- inputs=["X"],
-- outputs=["Splits", "MaxSplits"],
-- delimiter=delimiter,
-- maxsplit=maxsplit,
-- )
-- model = make_model(make_graph([node], "g", [X], [Splits, MaxSplits]))
-- ref = ReferenceEvaluator(model)
-- x = np.array(x, dtype=object)
-- result, num_splits, *_ = ref.run(None, {"X": x})
-- np.testing.assert_array_equal(result, np.array(expected_split, dtype=object))
-- np.testing.assert_array_equal(
-- num_splits, np.array(expected_num_splits, dtype=np.int64)
-- )
--
- def test_qlinearconv_int8(self):
- node = make_node(
- "QLinearMatMul",
-@@ -5561,44 +5034,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- np.array([[41, -12, -9], [1, -75, 20]], dtype=np.int8), got[0]
- )
-
-- @parameterized.parameterized.expand(
-- [
-- (
-- ["www.google.com", "www.facebook.com", "www.bbc.co.uk"],
-- r"www\.[\w.-]+\.\bcom\b",
-- [True, True, False],
-- (3,),
-- ),
-- (
-- [["Onnx", "tensorflow", "Numpy"], ["Pytorch", "Cython", "numba"]],
-- r"^[A-Z][a-z]*$",
-- [[True, False, True], [True, True, False]],
-- (2, 3),
-- ),
-- (
-- [
-- "account@gmail.com",
-- "account@hotmail.com",
-- "not email",
-- "account2@yahoo.com",
-- ],
-- r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
-- [True, False, False, True],
-- (4,),
-- ),
-- ]
-- )
-- def test_regex_full_match(self, x, pattern, expected, expected_shape):
-- X = make_tensor_value_info("X", TensorProto.STRING, None)
-- Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
-- node = make_node("RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=pattern)
-- model = make_model(make_graph([node], "g", [X], [Y]))
-- ref = ReferenceEvaluator(model)
-- result, *_ = ref.run(None, {"X": np.array(x)})
-- np.testing.assert_array_equal(result, expected)
-- self.assertEqual(result.dtype.kind, "b")
-- self.assertEqual(result.shape, expected_shape)
--
- def test_regex_invalid_pattern(self):
- X = make_tensor_value_info("X", TensorProto.STRING, None)
- Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
-@@ -5608,220 +5043,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- with self.assertRaises(ValueError):
- ref.run(None, {"X": np.array(["x"])})
-
-- @parameterized.parameterized.expand(
-- [
-- (
-- TensorProto.UINT2,
-- [-1, 0, 1.5, 2, 3.3, 10, 20, 40],
-- [0, 0, 2, 2, 4, 6, 6, 6],
-- ),
-- (TensorProto.UINT2, [-1, 0, 1.5, 2, 3.3, 10, 40], [0, 0, 2, 2, 4, 6, 6]),
-- (TensorProto.UINT2, [0], [0]),
-- (
-- TensorProto.INT2,
-- [-20, -14.5, 0, 1.5, 2, 3.3, 10, 20],
-- [-4, -4, 0, 2, 2, 2, 2, 2],
-- ),
-- (
-- TensorProto.INT2,
-- [-20, -14.5, 0, 1.5, 2, 3.3, 10],
-- [-4, -4, 0, 2, 2, 2, 2],
-- ),
-- (TensorProto.INT2, [0], [0]),
-- ]
-- )
-- def test_quantize_linear_int2(self, qtype, data, expected):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node(
-- "Constant",
-- [],
-- ["scale"],
-- value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
-- ),
-- make_node(
-- "Constant",
-- [],
-- ["zero"],
-- value=make_tensor("zero", qtype, [1], [0]),
-- ),
-- make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
-- make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- got = ref.run(None, {"X": np.asarray(data)})
-- assert_allclose(got[0], expected)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.FLOAT, TensorProto.FLOAT16),
-- (TensorProto.UINT2, TensorProto.INT2),
-- )
-- )
-- def test_cast_int2_output(self, cast_from, cast_to):
-- X = make_tensor_value_info("X", cast_from, [None])
-- Y = make_tensor_value_info("Y", cast_to, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Y"], to=cast_to),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- data = np.array(
-- [0, 1, 2.4, 2.6, 4, 10],
-- dtype=onnx.helper.tensor_dtype_to_np_dtype(cast_from),
-- )
-- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_to))
-- got = ref.run(None, {"X": data})
-- self.assertEqual(got[0].tolist(), expected.tolist())
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT2, TensorProto.INT2),
-- (TensorProto.FLOAT, TensorProto.FLOAT16),
-- )
-- )
-- def test_cast_int2_input(
-- self, cast_from: TensorProto.DataType, cast_to: TensorProto.DataType
-- ):
-- X = make_tensor_value_info("X", cast_from, [None])
-- Y = make_tensor_value_info("Y", cast_to, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Y"], to=TensorProto.FLOAT),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- data = np.array(range(2), dtype=np.float32)
-- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_from))
-- got = ref.run(None, {"X": data})
-- np.testing.assert_array_equal(got[0], expected)
--
-- @parameterized.parameterized.expand(
-- [
-- (
-- TensorProto.UINT4,
-- [-1, 0, 1.5, 2, 3.3, 10, 20, 40],
-- [0, 0, 2, 2, 4, 10, 20, 30],
-- ),
-- (TensorProto.UINT4, [-1, 0, 1.5, 2, 3.3, 10, 40], [0, 0, 2, 2, 4, 10, 30]),
-- (TensorProto.UINT4, [0], [0]),
-- (
-- TensorProto.INT4,
-- [-20, -14.5, 0, 1.5, 2, 3.3, 10, 20],
-- [-16, -14, 0, 2, 2, 4, 10, 14],
-- ),
-- (
-- TensorProto.INT4,
-- [-20, -14.5, 0, 1.5, 2, 3.3, 10],
-- [-16, -14, 0, 2, 2, 4, 10],
-- ),
-- (TensorProto.INT4, [0], [0]),
-- ]
-- )
-- def test_quantize_linear_int4(self, qtype, data, expected):
-- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
-- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node(
-- "Constant",
-- [],
-- ["scale"],
-- value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
-- ),
-- make_node(
-- "Constant",
-- [],
-- ["zero"],
-- value=make_tensor("zero", qtype, [1], [0]),
-- ),
-- make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
-- make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- got = ref.run(None, {"X": np.asarray(data)})
-- assert_allclose(got[0], expected)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.FLOAT, TensorProto.FLOAT16),
-- (TensorProto.UINT4, TensorProto.INT4),
-- )
-- )
-- def test_cast_int4_output(self, cast_from, cast_to):
-- X = make_tensor_value_info("X", cast_from, [None])
-- Y = make_tensor_value_info("Y", cast_to, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Y"], to=cast_to),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- data = np.array(
-- [0, 1, 2.4, 2.6, 4, 10],
-- dtype=onnx.helper.tensor_dtype_to_np_dtype(cast_from),
-- )
-- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_to))
-- got = ref.run(None, {"X": data})
-- self.assertEqual(got[0].tolist(), expected.tolist())
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (TensorProto.UINT4, TensorProto.INT4),
-- (TensorProto.FLOAT, TensorProto.FLOAT16),
-- )
-- )
-- def test_cast_int4_input(
-- self, cast_from: TensorProto.DataType, cast_to: TensorProto.DataType
-- ):
-- X = make_tensor_value_info("X", cast_from, [None])
-- Y = make_tensor_value_info("Y", cast_to, [None])
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Y"], to=TensorProto.FLOAT),
-- ],
-- "g",
-- [X],
-- [Y],
-- )
-- )
-- ref = ReferenceEvaluator(model)
-- data = np.array(range(7), dtype=np.float32)
-- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_from))
-- got = ref.run(None, {"X": data})
-- np.testing.assert_array_equal(got[0], expected)
--
- def test_a_function_calling_a_function_once(self):
- X = make_tensor_value_info("X", TensorProto.FLOAT, ["N"])
- output = make_tensor_value_info("output", TensorProto.FLOAT, ["N"])
-@@ -6054,94 +5275,6 @@ class TestReferenceEvaluator(unittest.TestCase):
- for v in oinf.functions_.values():
- self.assertIsInstance(v, MyReferenceEvaluator)
-
-- @parameterized.parameterized.expand(
-- [
-- ("FLOAT8E4M3FN", 0.23),
-- ("FLOAT8E4M3FNUZ", 0.23),
-- ("FLOAT8E5M2", 0.85),
-- ("FLOAT8E5M2FNUZ", 0.85),
-- ("DOUBLE", 0),
-- ("FLOAT", 0),
-- ("FLOAT16", 2e-3),
-- ("BFLOAT16", 2e-2),
-- ]
-- )
-- def test_add_custom_dtype(self, stype: str, atol: float):
-- itype = getattr(TensorProto, stype)
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Xc"], to=itype),
-- make_node("Cast", ["Y"], ["Yc"], to=itype),
-- make_node("Add", ["Xc", "Yc"], ["Zc"]),
-- make_node("Cast", ["Zc"], ["Z"], to=TensorProto.FLOAT),
-- ],
-- "nd",
-- [
-- make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None]),
-- make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None]),
-- ],
-- [make_tensor_value_info("Z", TensorProto.FLOAT, [None, None, None])],
-- ),
-- opset_imports=[make_opsetid("", 18)],
-- ir_version=9,
-- )
--
-- ref = ReferenceEvaluator(model, verbose=0)
--
-- x = (np.arange(18) / 6).reshape((2, 3, 3)).astype(np.float32)
-- y = (np.arange(18) / 9).reshape((2, 3, 3)).astype(np.float32)
-- feeds = dict(X=x, Y=y)
-- expected = x + y
-- got = ref.run(None, feeds)[0]
-- assert_allclose(got, expected, atol=atol)
--
-- @parameterized.parameterized.expand(
-- [
-- ("DOUBLE",),
-- ("FLOAT",),
-- ("FLOAT16",),
-- ("BFLOAT16",),
-- ("FLOAT8E4M3FN",),
-- ("FLOAT8E4M3FNUZ",),
-- ("FLOAT8E5M2",),
-- ("FLOAT8E5M2FNUZ",),
-- ("INT4",),
-- ("UINT4",),
-- ("INT2",),
-- ("UINT2",),
-- ]
-- )
-- def test_cmp_custom_dtype(self, stype):
-- itype = getattr(TensorProto, stype)
-- model = make_model(
-- make_graph(
-- [
-- make_node("Cast", ["X"], ["Xc"], to=itype),
-- make_node("Cast", ["Y"], ["Yc"], to=itype),
-- make_node("GreaterOrEqual", ["Xc", "Yc"], ["Zc"]),
-- make_node("Cast", ["Zc"], ["Z"], to=TensorProto.BOOL),
-- ],
-- "nd",
-- [
-- make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None]),
-- make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None]),
-- ],
-- [make_tensor_value_info("Z", TensorProto.FLOAT, [None, None, None])],
-- ),
-- opset_imports=[make_opsetid("", 18)],
-- ir_version=9,
-- )
--
-- ref = ReferenceEvaluator(model)
--
-- x = (np.arange(18) / 18).reshape((2, 3, 3)).astype(np.float32)
-- y = ((np.arange(18) - 9) / 18).reshape((2, 3, 3)).astype(np.float32)
-- feeds = dict(X=x, Y=y)
-- expected = x >= y
-- got = ref.run(None, feeds)[0]
-- np.testing.assert_equal(got, expected)
--
- def test_scatter_elements_4d(self):
- model = make_model(
- make_graph(
-diff --git a/onnx/test/schema_test.py b/onnx/test/schema_test.py
-index f8b3f8a65..710b602a3 100644
---- a/onnx/test/schema_test.py
-+++ b/onnx/test/schema_test.py
-@@ -7,8 +7,6 @@ import contextlib
- import unittest
- from typing import TYPE_CHECKING
-
--import parameterized
--
- import onnx
- from onnx import defs
-
-@@ -247,34 +245,6 @@ class TestFormalParameter(unittest.TestCase):
- )
-
-
--class TestTypeConstraintParam(unittest.TestCase):
-- @parameterized.parameterized.expand(
-- [
-- ("single_type", "T", ["tensor(float)"], "Test description"),
-- (
-- "double_types",
-- "T",
-- ["tensor(float)", "tensor(int64)"],
-- "Test description",
-- ),
-- ("tuple", "T", ("tensor(float)", "tensor(int64)"), "Test description"),
-- ]
-- )
-- def test_init(
-- self,
-- _: str,
-- type_param_str: str,
-- allowed_types: Sequence[str],
-- description: str,
-- ) -> None:
-- type_constraint = defs.OpSchema.TypeConstraintParam(
-- type_param_str, allowed_types, description
-- )
-- self.assertEqual(type_constraint.description, description)
-- self.assertEqual(type_constraint.allowed_type_strs, list(allowed_types))
-- self.assertEqual(type_constraint.type_param_str, type_param_str)
--
--
- class TestAttribute(unittest.TestCase):
- def test_init(self):
- name = "test_attr"
-@@ -297,169 +267,5 @@ class TestAttribute(unittest.TestCase):
- self.assertEqual("attr1 description", attribute.description)
-
-
--@parameterized.parameterized_class(
-- [
-- # register to exist domain
-- {
-- "op_type": "CustomOp",
-- "op_version": 5,
-- "op_domain": "",
-- "trap_op_version": [1, 2, 6, 7],
-- },
-- # register to new domain
-- {
-- "op_type": "CustomOp",
-- "op_version": 5,
-- "op_domain": "test",
-- "trap_op_version": [1, 2, 6, 7],
-- },
-- ]
--)
--class TestOpSchemaRegister(unittest.TestCase):
-- op_type: str
-- op_version: int
-- op_domain: str
-- # register some fake schema to check behavior
-- trap_op_version: list[int]
--
-- def setUp(self) -> None:
-- # Ensure the schema is unregistered
-- self.assertFalse(onnx.defs.has(self.op_type, self.op_domain))
--
-- def tearDown(self) -> None:
-- # Clean up the registered schema
-- for version in [*self.trap_op_version, self.op_version]:
-- with contextlib.suppress(onnx.defs.SchemaError):
-- onnx.defs.deregister_schema(self.op_type, version, self.op_domain)
--
-- def test_register_multi_schema(self):
-- for version in [*self.trap_op_version, self.op_version]:
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- version,
-- )
-- onnx.defs.register_schema(op_schema)
-- self.assertTrue(onnx.defs.has(self.op_type, version, self.op_domain))
-- for version in [*self.trap_op_version, self.op_version]:
-- # Also make sure the `op_schema` is accessible after register
-- registered_op = onnx.defs.get_schema(
-- op_schema.name, version, op_schema.domain
-- )
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- version,
-- )
-- self.assertEqual(str(registered_op), str(op_schema))
--
-- def test_using_the_specified_version_in_onnx_check(self):
-- input = f"""
-- <
-- ir_version: 7,
-- opset_import: [
-- "{self.op_domain}" : {self.op_version}
-- ]
-- >
-- agraph (float[N, 128] X, int32 Y) => (float[N] Z)
-- {{
-- Z = {self.op_domain}.{self.op_type}<attr1=[1,2]>(X, Y)
-- }}
-- """
-- model = onnx.parser.parse_model(input)
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- self.op_version,
-- inputs=[
-- defs.OpSchema.FormalParameter("input1", "T"),
-- defs.OpSchema.FormalParameter("input2", "int32"),
-- ],
-- outputs=[
-- defs.OpSchema.FormalParameter("output1", "T"),
-- ],
-- type_constraints=[("T", ["tensor(float)"], "")],
-- attributes=[
-- defs.OpSchema.Attribute(
-- "attr1", defs.OpSchema.AttrType.INTS, "attr1 description"
-- )
-- ],
-- )
-- with self.assertRaises(onnx.checker.ValidationError):
-- onnx.checker.check_model(model, check_custom_domain=True)
-- onnx.defs.register_schema(op_schema)
-- # The fake schema will raise check exception if selected in checker
-- for version in self.trap_op_version:
-- onnx.defs.register_schema(
-- defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- version,
-- outputs=[
-- defs.OpSchema.FormalParameter("output1", "int32"),
-- ],
-- )
-- )
-- onnx.checker.check_model(model, check_custom_domain=True)
--
-- def test_register_schema_raises_error_when_registering_a_schema_twice(self):
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- self.op_version,
-- )
-- onnx.defs.register_schema(op_schema)
-- with self.assertRaises(onnx.defs.SchemaError):
-- onnx.defs.register_schema(op_schema)
--
-- def test_deregister_the_specified_schema(self):
-- for version in [*self.trap_op_version, self.op_version]:
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- version,
-- )
-- onnx.defs.register_schema(op_schema)
-- self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
-- onnx.defs.deregister_schema(op_schema.name, self.op_version, op_schema.domain)
-- for version in self.trap_op_version:
-- self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
-- # Maybe has lesser op version in trap list
-- if onnx.defs.has(op_schema.name, self.op_version, op_schema.domain):
-- schema = onnx.defs.get_schema(
-- op_schema.name, self.op_version, op_schema.domain
-- )
-- self.assertLess(schema.since_version, self.op_version)
--
-- def test_deregister_schema_raises_error_when_opschema_does_not_exist(self):
-- with self.assertRaises(onnx.defs.SchemaError):
-- onnx.defs.deregister_schema(self.op_type, self.op_version, self.op_domain)
--
-- def test_legacy_schema_accessible_after_deregister(self):
-- op_schema = defs.OpSchema(
-- self.op_type,
-- self.op_domain,
-- self.op_version,
-- )
-- onnx.defs.register_schema(op_schema)
-- schema_a = onnx.defs.get_schema(
-- op_schema.name, op_schema.since_version, op_schema.domain
-- )
-- schema_b = onnx.defs.get_schema(op_schema.name, op_schema.domain)
--
-- def filter_schema(schemas):
-- return [op for op in schemas if op.name == op_schema.name]
--
-- schema_c = filter_schema(onnx.defs.get_all_schemas())
-- schema_d = filter_schema(onnx.defs.get_all_schemas_with_history())
-- self.assertEqual(len(schema_c), 1)
-- self.assertEqual(len(schema_d), 1)
-- # Avoid memory residue and access storage as much as possible
-- self.assertEqual(str(schema_a), str(op_schema))
-- self.assertEqual(str(schema_b), str(op_schema))
-- self.assertEqual(str(schema_c[0]), str(op_schema))
-- self.assertEqual(str(schema_d[0]), str(op_schema))
--
--
- if __name__ == "__main__":
- unittest.main()
-diff --git a/onnx/test/shape_inference_test.py b/onnx/test/shape_inference_test.py
-index ee22c8ffb..849ae042a 100644
---- a/onnx/test/shape_inference_test.py
-+++ b/onnx/test/shape_inference_test.py
-@@ -12,7 +12,6 @@ from typing import TYPE_CHECKING, Any
- import numpy as np
- import pytest
- from google.protobuf import text_format
--from parameterized import parameterized
-
- import onnx.shape_inference
- from onnx import (
-@@ -244,86 +243,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- graph, [make_tensor_value_info("y", TensorProto.FLOAT, (30, 4, 5))]
- )
-
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose(self, _, version) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_preexisting(self, _, version) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
-- [make_tensor_value_info("Y", TensorProto.FLOAT, None)],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_scalar(self, _, version) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, ())],
-- [make_node("Transpose", ["X"], ["Y"])],
-- [],
-- )
--
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("Y", TensorProto.FLOAT, ())],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_partial(self, _, version) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
-- [make_tensor_value_info("Y", TensorProto.UNDEFINED, (3, "a", "b"))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_preexisting_incorrect_shape(self, *_) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
-- [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5))],
-- )
-- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_preexisting_incorrect_type(self, *_) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
-- [make_tensor_value_info("Y", TensorProto.STRING, (3, 2, 4))],
-- )
-- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
--
-- @parameterized.expand(all_versions_for("Transpose"))
-- def test_transpose_incorrect_repeated_perm(self, *_) -> None:
-- graph = self._make_graph(
-- [("X", TensorProto.FLOAT, (2, 3, 4))],
-- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 1])],
-- [],
-- )
-- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
--
- def _make_matmul_test_all_dims_known(
- self, version, shape1: Sequence[int], shape2: Sequence[int]
- ) -> None:
-@@ -342,21 +261,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
- )
-
-- @parameterized.expand(all_versions_for("MatMul"))
-- def test_matmul_all_dims_known(self, _, version) -> None:
-- self._make_matmul_test_all_dims_known(version, (2,), (2,))
--
-- self._make_matmul_test_all_dims_known(version, (4, 2), (2, 4))
-- self._make_matmul_test_all_dims_known(version, (5, 2), (2, 4))
-- self._make_matmul_test_all_dims_known(version, (5, 2), (2, 1))
-- self._make_matmul_test_all_dims_known(version, (1, 2), (2, 3))
-- self._make_matmul_test_all_dims_known(version, (2,), (2, 3))
-- self._make_matmul_test_all_dims_known(version, (4, 2), (2,))
-- self._make_matmul_test_all_dims_known(version, (1, 4, 2), (3, 2, 3))
-- self._make_matmul_test_all_dims_known(version, (3, 4, 2), (3, 2, 3))
-- self._make_matmul_test_all_dims_known(version, (5, 1, 4, 2), (1, 3, 2, 3))
-- self._make_matmul_test_all_dims_known(version, (4, 2), (3, 2, 3))
--
- def _make_matmul_test_allow_unknown(
- self, version, shape1: Any, shape2: Any, expected_out_shape: Any
- ) -> None:
-@@ -367,1318 +271,39 @@ class TestShapeInference(TestShapeInferenceHelper):
- )
- self._assert_inferred(
- graph,
-- [make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("MatMul"))
-- def test_matmul_allow_unknown(self, _, version) -> None:
-- self._make_matmul_test_allow_unknown(version, (None,), (None,), ())
-- self._make_matmul_test_allow_unknown(version, (3,), (None,), ())
-- self._make_matmul_test_allow_unknown(version, (2,), (2, "a"), ("a",))
-- self._make_matmul_test_allow_unknown(version, (4, 2), (2, "a"), (4, "a"))
-- self._make_matmul_test_allow_unknown(version, (4, None), (2, "a"), (4, "a"))
-- self._make_matmul_test_allow_unknown(version, (4, None), (None, "a"), (4, "a"))
-- self._make_matmul_test_allow_unknown(
-- version, (1, 4, 2), ("a", 2, 5), ("a", 4, 5)
-- )
-- self._make_matmul_test_allow_unknown(
-- version, (1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)
-- )
-- self._make_matmul_test_allow_unknown(version, (3,), None, None)
-- self._make_matmul_test_allow_unknown(version, None, None, None)
--
-- @parameterized.expand(all_versions_for("Cast"))
-- def test_cast(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Cast", ["x"], ["y"], to=TensorProto.UINT8)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (2, 4, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Cast"))
-- @unittest.skip(
-- "Issue #5960"
-- ) # FIXME(#5960) propagateElemTypeFromAttributeToOutput does not validate against output type constraints
-- def test_cast_to_complex(self, _, version) -> None: # noqa: ARG002
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Cast", ["x"], ["y"], to=TensorProto.COMPLEX128)],
-- [],
-- )
--
-- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
--
-- @parameterized.expand(all_versions_for("CastLike"))
-- def test_cast_like(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3)), ("t", TensorProto.FLOAT16, ("N",))],
-- [make_node("CastLike", ["x", "t"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT16, (2, 4, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_same_size(self, _, version) -> None:
-- # Test bitcast between types of same size (float32 -> int32)
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT32)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_scalar(self, _, version) -> None:
-- # Test bitcast with scalar input (same size)
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, ())],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT32)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, ())],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_1d(self, _, version) -> None:
-- # Test bitcast with 1D tensor (float32 -> uint32, same size)
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (8,))],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.UINT32)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT32, (8,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_double_to_int64(self, _, version) -> None:
-- # Test bitcast between 64-bit types (double -> int64)
-- graph = self._make_graph(
-- [("x", TensorProto.DOUBLE, (3, 5))],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT64)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (3, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_int8_to_uint8(self, _, version) -> None:
-- # Test bitcast between 8-bit types (int8 -> uint8)
-- graph = self._make_graph(
-- [("x", TensorProto.INT8, (4, 6))],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.UINT8)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (4, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("BitCast"))
-- def test_bitcast_float16_to_int16(self, _, version) -> None:
-- # Test bitcast between 16-bit types (float16 -> int16)
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT16, (2, 3))],
-- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT16)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT16, (2, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Col2Im"))
-- def test_col2im(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("input", TensorProto.FLOAT, (1, 5, 5)),
-- ("output_shape", TensorProto.INT64, (2,)),
-- ("kernel_shape", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
-- )
-- ],
-- [],
-- initializer=[
-- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
-- make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Col2Im"))
-- def test_col2im_strides(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("input", TensorProto.FLOAT, (1, 9, 4)),
-- ("output_shape", TensorProto.INT64, (2,)),
-- ("kernel_shape", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Col2Im",
-- ["input", "output_shape", "kernel_shape"],
-- ["output"],
-- strides=[2, 2],
-- )
-- ],
-- [],
-- initializer=[
-- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
-- make_tensor("kernel_shape", TensorProto.INT64, (2,), (3, 3)),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Col2Im"))
-- def test_col2im_pads(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("input", TensorProto.FLOAT, (1, 5, 15)),
-- ("output_shape", TensorProto.INT64, (2,)),
-- ("kernel_shape", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Col2Im",
-- ["input", "output_shape", "kernel_shape"],
-- ["output"],
-- pads=[0, 1, 0, 1],
-- )
-- ],
-- [],
-- initializer=[
-- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
-- make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Col2Im"))
-- def test_col2im_dilations(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("input", TensorProto.FLOAT, (1, 4, 5)),
-- ("output_shape", TensorProto.INT64, (2,)),
-- ("kernel_shape", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Col2Im",
-- ["input", "output_shape", "kernel_shape"],
-- ["output"],
-- dilations=[1, 5],
-- )
-- ],
-- [],
-- initializer=[
-- make_tensor("output_shape", TensorProto.INT64, (2,), (6, 6)),
-- make_tensor("kernel_shape", TensorProto.INT64, (2,), (2, 2)),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 6, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Col2Im"))
-- def test_col2im_5d(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("input", TensorProto.FLOAT, (1, 10, 12)),
-- ("output_shape", TensorProto.INT64, (3,)),
-- ("kernel_shape", TensorProto.INT64, (3,)),
-- ],
-- [
-- make_node(
-- "Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
-- )
-- ],
-- [],
-- initializer=[
-- make_tensor("output_shape", TensorProto.INT64, (3,), (3, 4, 5)),
-- make_tensor("kernel_shape", TensorProto.INT64, (3,), (1, 1, 5)),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 2, 3, 4, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Concat"))
-- def test_concat(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3)), ("y", TensorProto.FLOAT, (7, 4, 3))],
-- [make_node("Concat", ["x", "y"], ["z"], axis=0)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.FLOAT, (9, 4, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Concat"))
-- def test_concat_missing_shape(self, *_) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (2, 4, 3)),
-- "y",
-- ("z", TensorProto.FLOAT, (None, None, None)),
-- ],
-- [make_node("Concat", ["x", "y", "z"], ["out"], axis=0)],
-- [],
-- )
-- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
--
-- @parameterized.expand(all_versions_for("Concat"))
-- def test_concat_3d_axis_2(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 2, 2)), ("y", TensorProto.FLOAT, (2, 2, 2))],
-- [make_node("Concat", ["x", "y"], ["z"], axis=2)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.FLOAT, (2, 2, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Concat"))
-- def test_concat_param(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, ("a", 2)), ("y", TensorProto.FLOAT, ("a", 3))],
-- [make_node("Concat", ["x", "y"], ["z"], axis=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.FLOAT, ("a", 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Concat"))
-- def test_concat_param_single_input(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, ("a", 2))],
-- [make_node("Concat", ["x"], ["z"], axis=0)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.FLOAT, ("a", 2))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_dynamic_shape_known_rank(self, _, version) -> None:
-- self.skipIf(version < 14, "Rank inference is added from Version 14")
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (None, None))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_dynamic_shape_symbolic(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, ("M",))],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, None)],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_dynamic_unknown_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, None)],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, None)],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_static_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (2,), (3, 8))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (3, 8))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_static_shape_inferred(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (3,))],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 3, -1))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (2, 3, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_static_shape_zero(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (1, 1, 1)), ("shape", TensorProto.INT64, (3,))],
-- [make_node("Reshape", ["x", "shape"], ["y"])],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (1, 1, 1))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_static_shape_allowzero(self, _, version) -> None:
-- self.skipIf(version < 14, "allowzero is added from Version 14")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.UINT8, (1, 0, 0)),
-- ("shape", TensorProto.INT64, (3,)),
-- ],
-- [make_node("Reshape", ["x", "shape"], ["y"], allowzero=1)],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.UINT8, (0, 1, 1))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Reshape"))
-- def test_reshape_static_shape_constant(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.UINT8, (2, 4, 3))],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["shape"],
-- value=make_tensor("shape", TensorProto.INT64, (2,), (3, 8)),
-- ),
-- make_node("Reshape", ["x", "shape"], ["y"]),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("shape", TensorProto.INT64, (2,)),
-- make_tensor_value_info("y", TensorProto.UINT8, (3, 8)),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Upsample"))
-- def test_upsample(self, _, version) -> None:
-- if version == 7:
-- graph = self._make_graph(
-- [("x", TensorProto.INT32, (2, 4, 3, 5))],
-- [make_node("Upsample", ["x"], ["y"], scales=[1.0, 1.1, 1.3, 1.9])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
-- else:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ],
-- [make_node("Upsample", ["x", "scales"], ["y"])],
-- [],
-- initializer=[
-- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
-- ],
-- )
--
-- def call_inference():
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- if version == 9:
-- call_inference()
-- else:
-- # Upsample is deprecated since Version 10.
-- with self.assertRaises(onnx.checker.ValidationError) as cm:
-- call_inference()
-- exception = cm.exception
-- assert "Upsample is deprecated" in str(exception)
--
-- @parameterized.expand(all_versions_for("Upsample"))
-- def test_upsample_raw_data(self, _, version) -> None:
-- if version == 7:
-- graph = self._make_graph(
-- [("x", TensorProto.INT32, (1, 3, 4, 5))],
-- [make_node("Upsample", ["x"], ["y"], scales=[2.0, 1.1, 2.3, 1.9])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
-- else:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ],
-- [make_node("Upsample", ["x", "scales"], ["y"])],
-- [],
-- initializer=[
-- make_tensor(
-- "scales",
-- TensorProto.FLOAT,
-- (4,),
-- vals=np.array([1.0, 1.1, 1.3, 1.9], dtype="<f4").tobytes(),
-- raw=True,
-- )
-- ],
-- ) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
--
-- def call_inference():
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- if version == 9:
-- call_inference()
-- else:
-- # Upsample is deprecated since Version 10.
-- with self.assertRaises(onnx.checker.ValidationError) as cm:
-- call_inference()
-- exception = cm.exception
-- assert "Upsample is deprecated" in str(exception)
--
-- @parameterized.expand(all_versions_for("Expand"))
-- def test_expand(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (3,))],
-- [make_node("Expand", ["x", "shape"], ["y"])],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (2, 1, 6))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Expand"))
-- def test_expand_scalar_input(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.INT32, ()), ("shape", TensorProto.INT64, (2,))],
-- [make_node("Expand", ["x", "shape"], ["y"])],
-- [],
-- initializer=[make_tensor("shape", TensorProto.INT64, (2,), (4, 8))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (4, 8))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Expand"))
-- def test_expand_raw_data(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (2,))],
-- [make_node("Expand", ["x", "shape"], ["y"])],
-- [],
-- initializer=[
-- make_tensor(
-- "shape",
-- TensorProto.INT64,
-- (2,),
-- vals=np.array([3, 4], dtype="<i8").tobytes(),
-- raw=True,
-- )
-- ],
-- ) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (3, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Expand"))
-- def test_expand_dynamic_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (1, 2, None)),
-- ("shape", TensorProto.INT64, (3,)),
-- ],
-- [make_node("Expand", ["x", "shape"], ["y"])],
-- [],
-- initializer=[],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (None, 2, None))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Expand"))
-- def test_expand_symbolic_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (1, 2, None)),
-- ("shape", TensorProto.INT64, ("unk__0",)),
-- ],
-- [make_node("Expand", ["x", "shape"], ["y"])],
-- [],
-- initializer=[],
-- )
-- # if giving a symbolic shape, Expand should not infer any shape or rank inference
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, None)],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size(self, _, version) -> None:
-- if version == 10:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ],
-- [make_node("Resize", ["x", "scales"], ["y"])],
-- [],
-- initializer=[
-- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
-- elif version == 11:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (4,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
-- [],
-- initializer=[
-- make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
-- else:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("sizes", TensorProto.INT64, (4,)),
-- ],
-- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"])],
-- [],
-- initializer=[
-- make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("RMSNormalization"))
-- def test_rms_normalization(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("X", TensorProto.FLOAT, ("N", "C", "H", "W")),
-- ("scale", TensorProto.FLOAT, ("H", "W")),
-- ],
-- [make_node("RMSNormalization", ["X", "scale"], ["y"], axis=2)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, ("N", "C", "H", "W"))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_axes_2_3(self, _, version) -> None:
-- self.skipIf(version < 18, "axes is from Version 18")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(2, 3))],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 7))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_axes_3_2(self, _, version) -> None:
-- self.skipIf(version < 18, "axes is from Version 18")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(3, 2))],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 7, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_not_larger(self, _, version) -> None:
-- self.skipIf(
-- version < 18,
-- "keep_aspect_ratio_policy is from Version 18",
-- )
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Resize",
-- ["x", "roi", "", "sizes"],
-- ["y"],
-- keep_aspect_ratio_policy="not_larger",
-- )
-- ],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (4, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_axes_2_3_not_larger(self, _, version) -> None:
-- self.skipIf(
-- version < 18,
-- "axes & keep_aspect_ratio_policy are from Version 18",
-- )
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Resize",
-- ["x", "roi", "", "sizes"],
-- ["y"],
-- axes=(2, 3),
-- keep_aspect_ratio_policy="not_larger",
-- )
-- ],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 4, 6))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_not_smaller(self, _, version) -> None:
-- self.skipIf(
-- version < 18,
-- "keep_aspect_ratio_policy is from Version 18",
-- )
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Resize",
-- ["x", "roi", "", "sizes"],
-- ["y"],
-- keep_aspect_ratio_policy="not_smaller",
-- )
-- ],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (6, 10))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_size_axes_2_3_not_smaller(self, _, version) -> None:
-- self.skipIf(
-- version < 18,
-- "axes & keep_aspect_ratio_policy are from Version 18",
-- )
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (2,)),
-- ],
-- [
-- make_node(
-- "Resize",
-- ["x", "roi", "", "sizes"],
-- ["y"],
-- axes=(2, 3),
-- keep_aspect_ratio_policy="not_smaller",
-- )
-- ],
-- [],
-- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 10))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_scale(self, _, version) -> None:
-- self.skipIf(version < 11, "roi input is from Version 11")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales"], ["y"])],
-- [],
-- initializer=[
-- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_scale_axes_2_3(self, _, version) -> None:
-- self.skipIf(version < 18, "axes is from Version 18")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (2,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(2, 3))],
-- [],
-- initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.3, 1.9))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_scale_axes_3_2(self, _, version) -> None:
-- self.skipIf(version < 18, "axes is from Version 18")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (2, 4, 3, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (2,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(3, 2))],
-- [],
-- initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.9, 1.3))],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_scale_raw_data(self, _, version) -> None:
-- self.skipIf(version < 11, "roi input is from Version 11")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (1, 3, 4, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales"], ["y"])],
-- [],
-- initializer=[
-- make_tensor(
-- "scales",
-- TensorProto.FLOAT,
-- (4,),
-- vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
-- raw=True,
-- )
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_scale_and_size_but_one_is_empty(self, _, version) -> None:
-- self.skipIf(version < 11, "roi input is from Version 11")
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (1, 3, 4, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (4,)),
-- ("sizes", TensorProto.INT64, (0,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
-- [],
-- initializer=[
-- make_tensor(
-- "scales",
-- TensorProto.FLOAT,
-- (4,),
-- vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
-- raw=True,
-- ),
-- make_tensor(
-- "sizes",
-- TensorProto.INT64,
-- (0,),
-- vals=np.array([], dtype="<i8").tobytes(),
-- raw=True,
-- ),
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Resize"))
-- def test_resize_opset11_scales_is_empty(self, _, version) -> None:
-- self.skipIf(version != 11, "This test only works for Version 11")
-- # "scales" input in Resize in opset11 is not optional. It must be an empty tensor
-- # if sizes is needed. Shape inference for Resize shall handle this case.
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.INT32, (1, 3, 4, 5)),
-- ("roi", TensorProto.FLOAT, (8,)),
-- ("scales", TensorProto.FLOAT, (0,)),
-- ("sizes", TensorProto.INT64, (4,)),
-- ],
-- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
-- [],
-- initializer=[
-- make_tensor(
-- "sizes",
-- TensorProto.INT64,
-- (4,),
-- vals=np.array(
-- [2, 6, 8, 10], dtype="<i8"
-- ).tobytes(), # double in all dimensions
-- raw=True,
-- ),
-- ],
-- )
--
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT32, (2, 6, 8, 10))],
-- opset_imports=[helper.make_opsetid("", version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape_start_1(self, _, version) -> None:
-- self.skipIf(version < 15, "start and end are from Version 15")
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"], start=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (2,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape_end_1(self, _, version) -> None:
-- self.skipIf(version < 15, "start and end are from Version 15")
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"], end=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (1,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape_negative_start(self, _, version) -> None:
-- self.skipIf(version < 15, "start and end are from Version 15")
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"], start=-1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (1,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape_clip1(self, _, version) -> None:
-- self.skipIf(version < 15, "start and end are from Version 15")
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"], start=-5)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Shape"))
-- def test_shape_clip2(self, _, version) -> None:
-- self.skipIf(version < 15, "start and end are from Version 15")
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))],
-- [make_node("Shape", ["x"], ["y"], end=10)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Size"))
-- def test_size(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 4, 3))], [make_node("Size", ["x"], ["y"])], []
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, ())],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Gather"))
-- def test_gather(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (4, 3)), ("i", TensorProto.INT64, (2,))],
-- [make_node("Gather", ["x", "i"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Gather"))
-- def test_gather_axis1(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (4, 3, 5)), ("i", TensorProto.INT64, (1, 2))],
-- [make_node("Gather", ["x", "i"], ["y"], axis=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (4, 1, 2, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Gather"))
-- def test_gather_into_scalar(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (3,)), ("i", TensorProto.INT64, ())],
-- [make_node("Gather", ["x", "i"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, ())],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("GatherElements"))
-- def test_gather_elements(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2, 2)), ("i", TensorProto.INT64, (2, 2))],
-- [make_node("GatherElements", ["x", "i"], ["y"], axis=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("GatherElements"))
-- def test_gather_elements_axis0(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (3, 3)), ("i", TensorProto.INT64, (2, 3))],
-- [make_node("GatherElements", ["x", "i"], ["y"], axis=0)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Scatter"))
-- def test_scatter(self, _, version) -> None:
-- if version >= 11:
-- # Scatter is deprecated in domain_version of 11.
-- with self.assertRaises(onnx.checker.ValidationError) as cm:
-- self._test_scatter(version)
-- exception = cm.exception
-- assert "Scatter is deprecated" in str(exception)
-- else:
-- self._test_scatter(version)
--
-- def _test_scatter(self, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (3, 3)),
-- ("i", TensorProto.INT64, (2, 3)),
-- ("u", TensorProto.FLOAT, (2, 3)),
-- ],
-- [make_node("Scatter", ["x", "i", "u"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("Scatter"))
-- def test_scatter_axis1(self, _, version) -> None:
-- if version >= 11:
-- # Scatter is deprecated in domain_version of 11.
-- with self.assertRaises(onnx.checker.ValidationError) as cm:
-- self._test_scatter_axis1(version)
-- exception = cm.exception
-- assert "Scatter is deprecated" in str(exception)
-- else:
-- self._test_scatter_axis1(version)
--
-- def _test_scatter_axis1(self, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (1, 5)),
-- ("i", TensorProto.INT64, (1, 2)),
-- ("u", TensorProto.FLOAT, (1, 2)),
-- ],
-- [make_node("Scatter", ["x", "i", "u"], ["y"], axis=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("ScatterElements"))
-- def test_scatter_elements(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (3, 3)),
-- ("i", TensorProto.INT64, (2, 3)),
-- ("u", TensorProto.FLOAT, (2, 3)),
-- ],
-- [make_node("ScatterElements", ["x", "i", "u"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("ScatterElements"))
-- def test_scatter_elements_axis1(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (1, 5)),
-- ("i", TensorProto.INT64, (1, 2)),
-- ("u", TensorProto.FLOAT, (1, 2)),
-- ],
-- [make_node("ScatterElements", ["x", "i", "u"], ["y"], axis=1)],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
-+ [make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
- )
-
-- @parameterized.expand(all_versions_for("ScatterND"))
-- def test_scatternd(self, _, version) -> None:
-+ def _test_scatter(self, version) -> None:
- graph = self._make_graph(
- [
-- ("x", TensorProto.FLOAT, (4, 5, 6)),
-- ("indices", TensorProto.INT64, (3, 3, 2)),
-- ("updates", TensorProto.FLOAT, (3, 3, 6)),
-+ ("x", TensorProto.FLOAT, (3, 3)),
-+ ("i", TensorProto.INT64, (2, 3)),
-+ ("u", TensorProto.FLOAT, (2, 3)),
- ],
-- [make_node("ScatterND", ["x", "indices", "updates"], ["y"])],
-+ [make_node("Scatter", ["x", "i", "u"], ["y"])],
- [],
- )
- self._assert_inferred(
- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (4, 5, 6))],
-+ [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
- )
-
-- @parameterized.expand(all_versions_for("ScatterND"))
-- def test_scatternd_noshape(self, _, version) -> None:
-- # The shape of 'x_reshaped' cannot be inferred, since it is the output of a dynamic reshape.
-- # Thus the shape of 'y' is also None.
-+ def _test_scatter_axis1(self, version) -> None:
- graph = self._make_graph(
- [
-- ("x", TensorProto.FLOAT, (4, 5, 6)),
-- ("indices", TensorProto.INT64, (3, 3, 2)),
-- ("updates", TensorProto.FLOAT, (3, 3, 6)),
-- ("shape", TensorProto.INT64, ("M",)),
-- ],
-- [
-- make_node("Reshape", ["x", "shape"], ["x_reshaped"]),
-- make_node("ScatterND", ["x_reshaped", "indices", "updates"], ["y"]),
-+ ("x", TensorProto.FLOAT, (1, 5)),
-+ ("i", TensorProto.INT64, (1, 2)),
-+ ("u", TensorProto.FLOAT, (1, 2)),
- ],
-+ [make_node("Scatter", ["x", "i", "u"], ["y"], axis=1)],
- [],
- )
- self._assert_inferred(
- graph,
-- [
-- make_tensor_value_info("x_reshaped", TensorProto.FLOAT, None),
-- make_tensor_value_info("y", TensorProto.FLOAT, None),
-- ],
-+ [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
- )
-
-@@ -1709,95 +334,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 24)],
- )
-
-- @parameterized.expand(all_versions_for("Squeeze"))
-- def test_squeeze(self, _, version) -> None:
-- if version == 11:
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1))],
-- [make_node("Squeeze", "x", "y", axes=[0, 2, 3, 5])],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
-- else:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1)),
-- ("axes", TensorProto.INT64, (4,)),
-- ],
-- [make_node("Squeeze", ["x", "axes"], "y")],
-- [],
-- initializer=[
-- make_tensor("axes", TensorProto.INT64, (4,), (0, 2, 3, 5))
-- ],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringConcat"))
-- def test_stringconcat(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, (2, 3, 4)),
-- ("y", TensorProto.STRING, (2, 3, 4)),
-- ],
-- [make_node("StringConcat", ["x", "y"], "z")],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringConcat"))
-- def test_stringconcat_broadcasting(self, _, version) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, (2, 3, 4)),
-- ("y", TensorProto.STRING, (1, 3, 1)),
-- ],
-- [make_node("StringConcat", ["x", "y"], "z")],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("RegexFullMatch"))
-- def test_regex_full_match(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.STRING, (2, 4, 3, 9))],
-- [make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.BOOL, (2, 4, 3, 9))],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("RegexFullMatch"))
-- def test_regex_full_match_empty_shape(self, _, version) -> None:
-- graph = self._make_graph(
-- [("x", TensorProto.STRING, ())],
-- [make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.BOOL, ())],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
- def test_squeeze_no_axes_opset11(self) -> None:
- graph = self._make_graph(
- [
-@@ -5632,23 +4168,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- self._make_matmulinteger_test((5, 1, 4, 2), (1, 3, 2, 3))
- self._make_matmulinteger_test((4, 2), (3, 2, 3))
-
-- @parameterized.expand(
-- [onnx.TensorProto.FLOAT, onnx.TensorProto.FLOAT16, onnx.TensorProto.BFLOAT16]
-- )
-- def test_quantizelinear(self, elem_type) -> None:
-- graph = self._make_graph(
-- [
-- ("x", elem_type, (30, 4, 5)),
-- ("y_scale", elem_type, ()),
-- ("y_zero_point", TensorProto.UINT8, ()),
-- ],
-- [make_node("QuantizeLinear", ["x", "y_scale", "y_zero_point"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5))]
-- )
--
- def test_quantizelinear_default_zp(self) -> None:
- graph = self._make_graph(
- [("x", TensorProto.FLOAT, (30, 4, 5)), ("y_scale", TensorProto.FLOAT, ())],
-@@ -5754,23 +4273,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- graph,
- )
-
-- @parameterized.expand(
-- [onnx.TensorProto.FLOAT, onnx.TensorProto.FLOAT16, onnx.TensorProto.BFLOAT16]
-- )
-- def test_dequantizelinear(self, elem_type) -> None:
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.UINT8, (30, 4, 5)),
-- ("x_scale", elem_type, ()),
-- ("x_zero_point", TensorProto.UINT8, ()),
-- ],
-- [make_node("DequantizeLinear", ["x", "x_scale", "x_zero_point"], ["y"])],
-- [],
-- )
-- self._assert_inferred(
-- graph, [make_tensor_value_info("y", elem_type, (30, 4, 5))]
-- )
--
- def test_dynamicquantizelinear(self) -> None:
- graph = self._make_graph(
- [("x", TensorProto.FLOAT, (30, 4, 5))],
-@@ -6005,319 +4507,61 @@ class TestShapeInference(TestShapeInferenceHelper):
- [make_node("RoiAlign", ["x", "rois", "batch_indices"], ["y"])],
- [],
- )
-- self._assert_inferred(
-- graph, [make_tensor_value_info("y", TensorProto.FLOAT, (15, "C", 1, 1))]
-- )
--
-- def test_rotaryembedding_4d(self) -> None:
-- graph = self._make_graph(
-- [
-- ("X", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")),
-- ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-- ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-- ("position_ids", TensorProto.INT64, ("B", "seq_len")),
-- ],
-- [
-- make_node(
-- "RotaryEmbedding",
-- ["X", "cos_cache", "sin_cache", "position_ids"],
-- ["Y"],
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info(
-- "Y", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")
-- )
-- ],
-- )
--
-- def test_rotaryembedding_3d(self) -> None:
-- graph = self._make_graph(
-- [
-- ("X", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")),
-- ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-- ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-- ("position_ids", TensorProto.INT64, ("B", "seq_len")),
-- ],
-- [
-- make_node(
-- "RotaryEmbedding",
-- ["X", "cos_cache", "sin_cache", "position_ids"],
-- ["Y"],
-- num_heads=4,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info(
-- "Y", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")
-- )
-- ],
-- )
--
-- @parameterized.expand(
-- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
-- )
-- def test_label_encoder_string_int64(self, _, version) -> None:
-- self.skipIf(
-- version < 2, "keys_* attributes were introduced in ai.onnx.ml opset 2"
-- )
-- string_list = ["A", "m", "y"]
-- float_list = [94.17, 36.00, -99.0]
-- int64_list = [12, 28, 86]
-- graph = self._make_graph(
-- [("x", TensorProto.STRING, (6, 1))],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_strings=string_list,
-- values_int64s=int64_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (6, 1))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.INT64, (2, 3))],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_int64s=int64_list,
-- values_strings=string_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.STRING, (2, 3))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, (2,))],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_floats=float_list,
-- values_int64s=int64_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, (2,))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.INT64, (8,))],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_int64s=int64_list,
-- values_floats=float_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (8,))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.FLOAT, ())],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_floats=float_list,
-- values_strings=string_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.STRING, ())],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.STRING, (1, 2))],
-- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_strings=string_list,
-- values_floats=float_list,
-- )
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 2))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- @parameterized.expand(
-- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
-- )
-- def test_label_encoder_tensor_attributes(self, _, version) -> None:
-- self.skipIf(
-- version < 4, "tensor attributes were introduced in ai.onnx.ml opset 4"
-- )
-- key_tensor = make_tensor(
-- "keys_tensor", TensorProto.STRING, [4], ["a", "b", "cc", "ddd"]
-- )
-- values_tensor = make_tensor(
-- "values_tensor", TensorProto.INT64, [4], [1, 2, 3, 4]
-+ self._assert_inferred(
-+ graph, [make_tensor_value_info("y", TensorProto.FLOAT, (15, "C", 1, 1))]
- )
-+
-+ def test_rotaryembedding_4d(self) -> None:
- graph = self._make_graph(
-- [("x", TensorProto.STRING, ("M", None, 3, 12))],
-+ [
-+ ("X", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")),
-+ ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-+ ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-+ ("position_ids", TensorProto.INT64, ("B", "seq_len")),
-+ ],
- [
- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_tensor=key_tensor,
-- values_tensor=values_tensor,
-- default_tensor=make_tensor(
-- "default_tensor", TensorProto.INT64, [1], [0]
-- ),
-+ "RotaryEmbedding",
-+ ["X", "cos_cache", "sin_cache", "position_ids"],
-+ ["Y"],
- )
- ],
- [],
- )
- self._assert_inferred(
- graph,
-- [make_tensor_value_info("y", TensorProto.INT64, ("M", None, 3, 12))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-+ [
-+ make_tensor_value_info(
-+ "Y", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")
-+ )
- ],
- )
-
-- @parameterized.expand(
-- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
-- )
-- def test_label_encoder_tensor_attributes_invalid_configurations(
-- self, _, version
-- ) -> None:
-- self.skipIf(version < 4, "tensor attributes introduced in ai.onnx.ml opset 4")
-- key_tensor = make_tensor(
-- "keys_tensor", TensorProto.STRING, [4], ["a", "b", "cc", "ddd"]
-- )
-- values_tensor = make_tensor(
-- "values_tensor", TensorProto.INT64, [4], [1, 2, 3, 4]
-- )
--
-- opset_imports = [
-- make_opsetid(ONNX_ML_DOMAIN, version),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ]
--
-- # default_tensor should be INT64, same type as values_tensor
-+ def test_rotaryembedding_3d(self) -> None:
- graph = self._make_graph(
-- [("x", TensorProto.STRING, ("M", None, 3, 12))],
-+ [
-+ ("X", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")),
-+ ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-+ ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
-+ ("position_ids", TensorProto.INT64, ("B", "seq_len")),
-+ ],
- [
- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_tensor=key_tensor,
-- values_tensor=values_tensor,
-- default_tensor=make_tensor(
-- "default_tensor", TensorProto.INT32, [1], [0]
-- ),
-+ "RotaryEmbedding",
-+ ["X", "cos_cache", "sin_cache", "position_ids"],
-+ ["Y"],
-+ num_heads=4,
- )
- ],
- [],
- )
--
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-+ self._assert_inferred(
- graph,
-- opset_imports=opset_imports,
-- )
--
-- # default_tensor should be a singleton of shape (1,)
-- graph = self._make_graph(
-- [("x", TensorProto.STRING, ("M", None, 3, 12))],
- [
-- make_node(
-- "LabelEncoder",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- keys_tensor=key_tensor,
-- values_strings=["a", "b", "cc", "ddd"],
-- default_tensor=make_tensor(
-- "default_tensor", TensorProto.STRING, [1, 2], ["a", "b"]
-- ),
-+ make_tensor_value_info(
-+ "Y", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")
- )
- ],
-- [],
-- )
--
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-- graph,
-- opset_imports=opset_imports,
- )
-
- def make_sparse(
-@@ -8746,105 +6990,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- )
- self._assert_inferred(graph, [output_tensor_val_info])
-
-- @parameterized.expand(all_versions_for("StringSplit"))
-- def test_string_split_basic(self, _, version) -> None:
-- substrings = make_tensor_value_info(
-- "substrings",
-- TensorProto.STRING,
-- (2, None),
-- )
-- length = make_tensor_value_info("length", TensorProto.INT64, (2,))
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, (2,)),
-- ],
-- [make_node("StringSplit", ["x"], ["substrings", "length"])],
-- [substrings, length],
-- )
-- self._assert_inferred(
-- graph,
-- [substrings, length],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringSplit"))
-- def test_string_split_symbolic(self, _, version) -> None:
-- substrings = make_tensor_value_info(
-- "substrings",
-- TensorProto.STRING,
-- ("A", None),
-- )
-- length = make_tensor_value_info("length", TensorProto.INT64, ("A",))
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, ("A",)),
-- ],
-- [make_node("StringSplit", ["x"], ["substrings", "length"])],
-- [substrings, length],
-- )
-- self._assert_inferred(
-- graph,
-- [substrings, length],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringSplit"))
-- def test_string_split_nested(self, _, version) -> None:
-- substrings = make_tensor_value_info(
-- "substrings", TensorProto.STRING, (2, 4, 3, None)
-- )
-- length = make_tensor_value_info("length", TensorProto.INT64, (2, 4, 3))
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, (2, 4, 3)),
-- ],
-- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
-- [substrings, length],
-- )
-- self._assert_inferred(
-- graph,
-- [substrings, length],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringSplit"))
-- def test_string_split_zero_dimensional_input(self, _, version) -> None:
-- substrings = make_tensor_value_info("substrings", TensorProto.STRING, (None,))
-- length = make_tensor_value_info("length", TensorProto.INT64, ())
--
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, ()),
-- ],
-- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
-- [substrings, length],
-- )
-- self._assert_inferred(
-- graph,
-- [substrings, length],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(all_versions_for("StringSplit"))
-- def test_string_split_empty_input(self, _, version) -> None:
-- substrings = make_tensor_value_info(
-- "substrings", TensorProto.STRING, ("M", 3, 0, None)
-- )
-- length = make_tensor_value_info("length", TensorProto.INT64, ("M", 3, 0))
--
-- graph = self._make_graph(
-- [
-- ("x", TensorProto.STRING, ("M", 3, 0)),
-- ],
-- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
-- [substrings, length],
-- )
-- self._assert_inferred(
-- graph,
-- [substrings, length],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
- def test_optional_tensor_get_element(self) -> None:
- tensor_type_proto = helper.make_tensor_type_proto(
- elem_type=TensorProto.DOUBLE, shape=[2, 1, 4]
-@@ -9452,426 +7597,76 @@ class TestShapeInference(TestShapeInferenceHelper):
- [],
- )
- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("shape", TensorProto.INT64, ()),
-- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-- ],
-- )
--
-- graph = self._make_graph(
-- [],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["shape"],
-- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
-- ),
-- make_node("HannWindow", ["shape"], ["y"], periodic=0),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("shape", TensorProto.INT64, ()),
-- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-- ],
-- )
--
-- def test_blackmanwindow(self):
-- graph = self._make_graph(
-- [],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["shape"],
-- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
-- ),
-- make_node("BlackmanWindow", ["shape"], ["y"]),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("shape", TensorProto.INT64, ()),
-- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-- ],
-- )
--
-- graph = self._make_graph(
-- [],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["shape"],
-- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
-- ),
-- make_node("BlackmanWindow", ["shape"], ["y"], periodic=0),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("shape", TensorProto.INT64, ()),
-- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-- ],
-- )
--
-- @parameterized.expand(
-- [
-- (
-- name,
-- version,
-- test_aspect,
-- input_shape,
-- axis,
-- onesided,
-- inverse,
-- expected_shape,
-- )
-- for (name, version), (
-- test_aspect,
-- input_shape,
-- axis,
-- onesided,
-- inverse,
-- expected_shape,
-- ) in itertools.product(
-- all_versions_for("DFT"),
-- (
-- ("reals_default_axis", (2, 5, 1), None, None, None, (2, 5, 2)),
-- ("reals_axis_0", (3, 5, 10, 1), 0, 0, 0, (3, 5, 10, 2)),
-- ("reals_axis_1", (3, 5, 10, 1), 1, 0, 0, (3, 5, 10, 2)),
-- ("reals_axis_2", (3, 5, 10, 1), 2, 0, 0, (3, 5, 10, 2)),
-- ("reals_axis_neg", (3, 5, 10, 1), -2, 0, 0, (3, 5, 10, 2)),
-- ("reals_axis_0_onesided", (3, 5, 10, 1), 0, 1, 0, (2, 5, 10, 2)),
-- ("reals_axis_1_onesided", (3, 5, 10, 1), 1, 1, 0, (3, 3, 10, 2)),
-- ("reals_axis_2_onesided", (3, 5, 10, 1), 2, 1, 0, (3, 5, 6, 2)),
-- ("reals_axis_neg_onesided", (3, 5, 10, 1), -2, 1, 0, (3, 5, 6, 2)),
-- ("complex_default_axis", (2, 5, 2), None, None, None, (2, 5, 2)),
-- ("real_inverse", (2, 5, 1), 1, None, 1, (2, 5, 2)),
-- ("complex_inverse", (2, 5, 2), 1, None, 1, (2, 5, 2)),
-- ("irfft_axis_0", (2, 5, 10, 2), 0, 1, 1, (2, 5, 10, 1)),
-- ("irfft_axis_1", (3, 3, 10, 2), 1, 1, 1, (3, 4, 10, 1)),
-- ("irfft_axis_2", (3, 5, 6, 2), 2, 1, 1, (3, 5, 10, 1)),
-- ("irfft_axis_neg", (3, 5, 6, 2), -2, 1, 1, (3, 5, 10, 1)),
-- ),
-- )
-- ]
-- )
-- def test_dft(
-- self,
-- _: str,
-- version: int,
-- _test_aspect: str,
-- input_shape: tuple[int],
-- axis: int | None,
-- onesided: int | None,
-- inverse: int | None,
-- expected_shape: tuple[int],
-- ) -> None:
-- # Build the attributes for different opset versions
-- attributes = {}
-- if onesided is not None:
-- attributes["onesided"] = onesided
-- if inverse is not None:
-- attributes["inverse"] = inverse
--
-- if version < 20:
-- if axis is not None:
-- attributes["axis"] = axis
-- nodes = [make_node("DFT", ["input", ""], ["output"], **attributes)]
-- value_infos = []
-- else:
-- assert version >= 20
-- if axis is not None:
-- nodes = [
-- make_node(
-- "Constant",
-- [],
-- ["axis"],
-- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
-- ),
-- make_node("DFT", ["input", "", "axis"], ["output"], **attributes),
-- ]
-- value_infos = [make_tensor_value_info("axis", TensorProto.INT64, ())]
-- else:
-- nodes = [
-- make_node("DFT", ["input", "", ""], ["output"], **attributes),
-- ]
-- value_infos = []
--
-- # Construct the graph
-- graph = self._make_graph(
-- [],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- input_shape,
-- np.ones(input_shape, dtype=np.float32).flatten(),
-- ),
-- ),
-- *nodes,
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, input_shape),
-- *value_infos,
-- make_tensor_value_info("output", TensorProto.FLOAT, expected_shape),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
-- @parameterized.expand(
-- [
-- (
-- name,
-- version,
-- test_aspect,
-- input_shape,
-- axis,
-- onesided,
-- inverse,
-- expected_shape,
-- )
-- for (name, version), (
-- test_aspect,
-- input_shape,
-- axis,
-- onesided,
-- inverse,
-- expected_shape,
-- ) in itertools.product(
-- all_versions_for("DFT"),
-- (
-- ("reals_default_axis", (2, 5, 1), None, None, None, (2, 42, 2)),
-- ("reals_axis_0", (3, 5, 10, 1), 0, 0, 0, (42, 5, 10, 2)),
-- ("reals_axis_1", (3, 5, 10, 1), 1, 0, 0, (3, 42, 10, 2)),
-- ("reals_axis_2", (3, 5, 10, 1), 2, 0, 0, (3, 5, 42, 2)),
-- ("reals_axis_neg", (3, 5, 10, 1), -2, 0, 0, (3, 5, 42, 2)),
-- ("reals_axis_0_onesided", (3, 5, 10, 1), 0, 1, 0, (22, 5, 10, 2)),
-- ("reals_axis_1_onesided", (3, 5, 10, 1), 1, 1, 0, (3, 22, 10, 2)),
-- ("reals_axis_2_onesided", (3, 5, 10, 1), 2, 1, 0, (3, 5, 22, 2)),
-- ("reals_axis_neg_onesided", (3, 5, 10, 1), -2, 1, 0, (3, 5, 22, 2)),
-- ("complex_default_axis", (2, 5, 2), None, None, None, (2, 42, 2)),
-- ("real_inverse", (2, 5, 1), 1, None, 1, (2, 42, 2)),
-- ("complex_inverse", (2, 5, 2), 1, None, 1, (2, 42, 2)),
-- ("irfft_axis_0", (2, 5, 10, 2), 0, 1, 1, (42, 5, 10, 1)),
-- ("irfft_axis_1", (3, 3, 10, 2), 1, 1, 1, (3, 42, 10, 1)),
-- ("irfft_axis_2", (3, 5, 6, 2), 2, 1, 1, (3, 5, 42, 1)),
-- ("irfft_axis_neg", (3, 5, 6, 2), -2, 1, 1, (3, 5, 42, 1)),
-- ),
-- )
-- ]
-- )
-- def test_dft_dft_length(
-- self,
-- _: str,
-- version: int,
-- _test_aspect: str,
-- input_shape: tuple[int],
-- axis: int | None,
-- onesided: int | None,
-- inverse: int | None,
-- expected_shape: tuple[int],
-- ) -> None:
-- # Build the attributes for different opset versions
-- attributes = {}
-- if onesided is not None:
-- attributes["onesided"] = onesided
-- if inverse is not None:
-- attributes["inverse"] = inverse
--
-- dft_length = 42
--
-- if version < 20:
-- if axis is not None:
-- attributes["axis"] = axis
-- nodes = [
-- make_node(
-- "Constant",
-- [],
-- ["dft_length"],
-- value=make_tensor(
-- "dft_length", TensorProto.INT64, (), (dft_length,)
-- ),
-- ),
-- make_node("DFT", ["input", "dft_length"], ["output"], **attributes),
-- ]
-- value_infos = [make_tensor_value_info("dft_length", TensorProto.INT64, ())]
-- else:
-- assert version >= 20
-- if axis is not None:
-- nodes = [
-- make_node(
-- "Constant",
-- [],
-- ["axis"],
-- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
-- ),
-- make_node(
-- "Constant",
-- [],
-- ["dft_length"],
-- value=make_tensor(
-- "dft_length", TensorProto.INT64, (), (dft_length,)
-- ),
-- ),
-- make_node(
-- "DFT",
-- ["input", "dft_length", "axis"],
-- ["output"],
-- **attributes,
-- ),
-- ]
-- value_infos = [
-- make_tensor_value_info("dft_length", TensorProto.INT64, ()),
-- make_tensor_value_info("axis", TensorProto.INT64, ()),
-- ]
-- else:
-- nodes = [
-- make_node(
-- "Constant",
-- [],
-- ["dft_length"],
-- value=make_tensor(
-- "dft_length", TensorProto.INT64, (), (dft_length,)
-- ),
-- ),
-- make_node(
-- "DFT",
-- ["input", "dft_length", ""],
-- ["output"],
-- **attributes,
-- ),
-- ]
-- value_infos = [
-- make_tensor_value_info("dft_length", TensorProto.INT64, ())
-- ]
-+ graph,
-+ [
-+ make_tensor_value_info("shape", TensorProto.INT64, ()),
-+ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-+ ],
-+ )
-
-- # Construct the graph
- graph = self._make_graph(
- [],
- [
- make_node(
- "Constant",
- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- input_shape,
-- np.ones(input_shape, dtype=np.float32).flatten(),
-- ),
-+ ["shape"],
-+ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
- ),
-- *nodes,
-+ make_node("HannWindow", ["shape"], ["y"], periodic=0),
- ],
- [],
- )
- self._assert_inferred(
- graph,
- [
-- make_tensor_value_info("input", TensorProto.FLOAT, input_shape),
-- *value_infos,
-- make_tensor_value_info("output", TensorProto.FLOAT, expected_shape),
-+ make_tensor_value_info("shape", TensorProto.INT64, ()),
-+ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
- )
-
-- @parameterized.expand(
-- [
-- ("last", 3),
-- ("last_negative", -1),
-- ("out_of_range", 4),
-- ("out_of_range_negative", -5),
-- ]
-- )
-- def test_dft_invalid_axis_opset17(self, _: str, axis: int) -> None:
-+ def test_blackmanwindow(self):
- graph = self._make_graph(
- [],
- [
- make_node(
- "Constant",
- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- (2, 5, 5, 2),
-- np.ones((2, 5, 5, 2), dtype=np.float32).flatten(),
-- ),
-+ ["shape"],
-+ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
- ),
-- make_node("DFT", ["input", ""], ["output"], onesided=1, axis=axis),
-+ make_node("HammingWindow", ["shape"], ["y"]),
- ],
- [],
- )
-- with self.assertRaises(onnx.shape_inference.InferenceError):
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, (2, 5, 5, 2)),
-- make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 17)],
-- )
-+ self._assert_inferred(
-+ graph,
-+ [
-+ make_tensor_value_info("shape", TensorProto.INT64, ()),
-+ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-+ ],
-+ )
-
-- @parameterized.expand(
-- [
-- ("last", 3),
-- ("last_negative", -1),
-- ("out_of_range", 4),
-- ("out_of_range_negative", -5),
-- ]
-- )
-- def test_dft_invalid_axis_opset20(self, _: str, axis: int) -> None:
- graph = self._make_graph(
- [],
- [
- make_node(
- "Constant",
- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- (2, 5, 5, 2),
-- np.ones((2, 5, 5, 2), dtype=np.float32).flatten(),
-- ),
-- ),
-- make_node(
-- "Constant",
-- [],
-- ["axis"],
-- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
-+ ["shape"],
-+ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
- ),
-- make_node("DFT", ["input", "", "axis"], ["output"]),
-+ make_node("HammingWindow", ["shape"], ["y"], periodic=0),
- ],
- [],
- )
-- with self.assertRaises(onnx.shape_inference.InferenceError):
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, (2, 5, 5, 2)),
-- make_tensor_value_info("axis", TensorProto.INT64, ()),
-- make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
-- )
-+ self._assert_inferred(
-+ graph,
-+ [
-+ make_tensor_value_info("shape", TensorProto.INT64, ()),
-+ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
-+ ],
-+ )
-
- def test_dft_rfft_invalid_complex_input_opset17(self) -> None:
- """Test that RFFT (onesided=1, inverse=0) rejects complex input"""
-@@ -10011,128 +7806,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
- )
-
-- @parameterized.expand(
-- [
-- ("real", (2, 5, 5, 1)),
-- ("complex", (2, 5, 5, 2)),
-- ]
-- )
-- def test_dft_dynamic_axis_opset20(self, _: str, shape: tuple[int, ...]) -> None:
-- graph = self._make_graph(
-- [("axis", TensorProto.INT64, ())],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- shape,
-- np.ones(shape, dtype=np.float32).flatten(),
-- ),
-- ),
-- make_node("DFT", ["input", "", "axis"], ["output"]),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, shape),
-- make_tensor_value_info("output", TensorProto.FLOAT, (2, 5, 5, 2)),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
-- )
--
-- @parameterized.expand(
-- [
-- ("real", (2, 5, 5, 1)),
-- ]
-- )
-- def test_dft_dynamic_axis_onesided_dft_length_opset20(
-- self, _: str, shape: tuple[int, ...]
-- ) -> None:
-- graph = self._make_graph(
-- [("axis", TensorProto.INT64, ())],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- shape,
-- np.ones(shape, dtype=np.float32).flatten(),
-- ),
-- ),
-- make_node(
-- "Constant",
-- [],
-- ["dft_length"],
-- value=make_tensor(
-- "dft_length",
-- TensorProto.INT64,
-- (),
-- np.array([42], dtype=np.int64),
-- ),
-- ),
-- make_node(
-- "DFT", ["input", "dft_length", "axis"], ["output"], onesided=1
-- ),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, shape),
-- make_tensor_value_info("dft_length", TensorProto.INT64, ()),
-- make_tensor_value_info(
-- "output", TensorProto.FLOAT, (None, None, None, 2)
-- ),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
-- )
--
-- @parameterized.expand(
-- [
-- ("real", (2, 5, 5, 1)),
-- ]
-- )
-- def test_dft_dynamic_axis_onesided_opset20(
-- self, _: str, shape: tuple[int, ...]
-- ) -> None:
-- graph = self._make_graph(
-- [("axis", TensorProto.INT64, ())],
-- [
-- make_node(
-- "Constant",
-- [],
-- ["input"],
-- value=make_tensor(
-- "input",
-- TensorProto.FLOAT,
-- shape,
-- np.ones(shape, dtype=np.float32).flatten(),
-- ),
-- ),
-- make_node("DFT", ["input", "", "axis"], ["output"], onesided=1),
-- ],
-- [],
-- )
-- self._assert_inferred(
-- graph,
-- [
-- make_tensor_value_info("input", TensorProto.FLOAT, shape),
-- make_tensor_value_info(
-- "output", TensorProto.FLOAT, (None, None, None, 2)
-- ),
-- ],
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
-- )
--
- def test_dft_onesided_default_axis_opset17(self) -> None:
- # Opset 17 sets default axis to be 1.
- graph = self._make_graph(
-@@ -10591,41 +8264,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- ],
- )
-
-- @parameterized.expand(
-- [
-- ([1, 2, 3], ["1", "2"]),
-- ([1, 2, 3], None),
-- (None, ["1", "2", "3"]),
-- (None, None),
-- ]
-- )
-- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
-- def test_category_mapper_fails_if_invalid_attributes(
-- self, cats_int64s, cats_strings
-- ) -> None:
-- cat = make_node(
-- "CategoryMapper",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- cats_int64s=cats_int64s,
-- cats_strings=cats_strings,
-- )
-- graph = self._make_graph(
-- [("x", TensorProto.INT64, (30, 4, 5))],
-- [cat],
-- [],
-- )
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-- graph,
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, 1),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
- def test_tree_ensemble_regressor(self) -> None:
- tree = make_node(
-@@ -10649,131 +8287,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- ],
- )
-
-- @parameterized.expand([TensorProto.FLOAT, TensorProto.DOUBLE, TensorProto.FLOAT16])
-- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
-- def test_tree_ensemble(self, dtype) -> None:
-- interior_nodes = 5
-- leaves = 9
-- tree = make_node(
-- "TreeEnsemble",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- n_targets=5,
-- nodes_featureids=[0] * interior_nodes,
-- nodes_splits=make_tensor(
-- "nodes_splits",
-- dtype,
-- (interior_nodes,),
-- list(range(interior_nodes)),
-- ),
-- nodes_modes=make_tensor(
-- "nodes_modes",
-- TensorProto.UINT8,
-- (interior_nodes,),
-- [0] * interior_nodes,
-- ),
-- nodes_truenodeids=[0] * interior_nodes,
-- nodes_falsenodeids=[0] * interior_nodes,
-- nodes_trueleafs=[0] * interior_nodes,
-- nodes_falseleafs=[0] * interior_nodes,
-- membership_values=make_tensor(
-- "membership_values",
-- dtype,
-- (7,),
-- [0.0, 0.1, 0.2, np.nan, 0.4, 0.5, 1.0],
-- ),
-- leaf_targetids=[0] * leaves,
-- leaf_weights=make_tensor("leaf_weights", dtype, (leaves,), [1] * leaves),
-- tree_roots=[0],
-- )
--
-- graph = self._make_graph(
-- [("x", dtype, ("Batch Size", "Features"))],
-- [tree],
-- [],
-- )
--
-- self._assert_inferred(
-- graph,
-- [make_tensor_value_info("y", dtype, ("Batch Size", 5))],
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, 5),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
-- @parameterized.expand(
-- [
-- (
-- [0] * 6,
-- make_tensor("leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9),
-- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
-- ),
-- (
-- [0] * 5,
-- make_tensor("leaf_weights", TensorProto.FLOAT, (9,), [1] * 9),
-- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
-- ),
-- (
-- [0] * 5,
-- make_tensor("leaf_weights", TensorProto.DOUBLE, (18,), [1] * 18),
-- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
-- ),
-- (
-- [0] * 5,
-- make_tensor("leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9),
-- make_tensor("nodes_splits", TensorProto.FLOAT, (5,), [1] * 5),
-- ),
-- ]
-- )
-- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
-- def test_tree_ensemble_fails_if_invalid_attributes(
-- self,
-- nodes_truenodeids,
-- leaf_weights,
-- nodes_splits,
-- ) -> None:
-- interior_nodes = 5
-- leaves = 9
-- tree = make_node(
-- "TreeEnsemble",
-- ["x"],
-- ["y"],
-- domain=ONNX_ML_DOMAIN,
-- n_targets=5,
-- nodes_featureids=[0] * interior_nodes,
-- nodes_splits=nodes_splits,
-- nodes_modes=make_tensor(
-- "nodes_modes",
-- TensorProto.UINT8,
-- (interior_nodes,),
-- [0] * interior_nodes,
-- ),
-- nodes_truenodeids=nodes_truenodeids,
-- nodes_falsenodeids=[0] * interior_nodes,
-- nodes_trueleafs=[0] * interior_nodes,
-- nodes_falseleafs=[0] * interior_nodes,
-- leaf_targetids=[0] * leaves,
-- leaf_weights=leaf_weights,
-- tree_roots=[0],
-- )
--
-- graph = self._make_graph(
-- [("x", TensorProto.DOUBLE, ("Batch Size", "Features"))],
-- [tree],
-- [],
-- )
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-- graph,
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, 5),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
- def test_tree_ensemble_classifier(self) -> None:
- tree = make_node(
-@@ -10878,40 +8391,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- ],
- )
-
-- @parameterized.expand(
-- [
-- ([1, 2, 3], ["1", "2", "3"]),
-- (None, None),
-- ]
-- )
-- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
-- def test_one_hot_encoder_fails_if_invalid_attributes(
-- self, cats_int64s, cats_strings
-- ) -> None:
-- graph = self._make_graph(
-- [("input", TensorProto.INT64, (2, "N", 3))],
-- [
-- make_node(
-- "OneHotEncoder",
-- ["input"],
-- ["output"],
-- cats_int64s=cats_int64s,
-- cats_strings=cats_strings,
-- domain="ai.onnx.ml",
-- )
-- ],
-- [],
-- )
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-- graph,
-- opset_imports=[
-- make_opsetid(ONNX_ML_DOMAIN, 1),
-- make_opsetid(ONNX_DOMAIN, 11),
-- ],
-- )
--
- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
- def test_zip_map(self) -> None:
- params = (
-@@ -11066,28 +8545,6 @@ class TestShapeInference(TestShapeInferenceHelper):
- onnx.checker.check_model(model, full_check=True)
- onnx.shape_inference.infer_shapes(model)
-
-- @parameterized.expand(all_versions_for("ConstantOfShape"))
-- def test_issue_constantofshape_6135(self, _, version):
-- graph = self._make_graph(
-- [("std.constant", TensorProto.INT64, (1,)), "output"],
-- [
-- make_node(
-- "ConstantOfShape",
-- inputs=["std.constant"],
-- outputs=["output"],
-- name="invalid_node",
-- )
-- ],
-- [],
-- initializer=[make_tensor("std.constant", TensorProto.FLOAT, (1,), (-10,))],
-- )
-- self.assertRaises(
-- onnx.shape_inference.InferenceError,
-- self._inferred,
-- graph,
-- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
-- )
--
- def test_protobuf_default(self) -> None:
- model_text = """
- ir_version: 8
-diff --git a/onnx/test/test_external_data.py b/onnx/test/test_external_data.py
-index c0845f96a..eb07f09e8 100644
---- a/onnx/test/test_external_data.py
-+++ b/onnx/test/test_external_data.py
-@@ -14,7 +14,6 @@ import warnings
- from typing import TYPE_CHECKING, Any
-
- import numpy as np
--import parameterized
-
- import onnx
- from onnx import (
-@@ -123,616 +122,6 @@ class TestLoadExternalDataBase(unittest.TestCase):
- checker.check_model(self.model_filename)
-
-
--@parameterized.parameterized_class(
-- [
-- {"serialization_format": "protobuf"},
-- {"serialization_format": "textproto"},
-- ]
--)
--class TestLoadExternalData(TestLoadExternalDataBase):
-- def test_load_external_data(self) -> None:
-- model = onnx.load_model(self.model_filename, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_load_external_data_for_model(self) -> None:
-- model = onnx.load_model(
-- self.model_filename, self.serialization_format, load_external_data=False
-- )
-- load_external_data_for_model(model, self.temp_dir)
-- initializer_tensor = model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_save_external_data(self) -> None:
-- model = onnx.load_model(self.model_filename, self.serialization_format)
--
-- temp_dir = os.path.join(self.temp_dir, "save_copy")
-- os.mkdir(temp_dir)
-- new_model_filename = os.path.join(temp_dir, "model.onnx")
-- onnx.save_model(model, new_model_filename, self.serialization_format)
--
-- new_model = onnx.load_model(new_model_filename, self.serialization_format)
-- initializer_tensor = new_model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = new_model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
--
--@parameterized.parameterized_class(
-- [
-- {"serialization_format": "protobuf"},
-- {"serialization_format": "textproto"},
-- ]
--)
--class TestLoadExternalDataSingleFile(TestLoadExternalDataBase):
-- def create_external_data_tensors(
-- self, tensors_data: list[tuple[list[Any], Any]]
-- ) -> list[TensorProto]:
-- tensor_filename = "tensors.bin"
-- tensors = []
--
-- with open(os.path.join(self.temp_dir, tensor_filename), "ab") as data_file:
-- for value, tensor_name in tensors_data:
-- tensor = from_array(np.array(value))
-- offset = data_file.tell()
-- if offset % 4096 != 0:
-- data_file.write(b"\0" * (4096 - offset % 4096))
-- offset = offset + 4096 - offset % 4096
--
-- data_file.write(tensor.raw_data)
-- set_external_data(
-- tensor,
-- location=tensor_filename,
-- offset=offset,
-- length=data_file.tell() - offset,
-- )
-- tensor.name = tensor_name
-- tensor.ClearField("raw_data")
-- tensor.data_location = onnx.TensorProto.EXTERNAL
-- tensors.append(tensor)
--
-- return tensors
--
-- def test_load_external_single_file_data(self) -> None:
-- model = onnx.load_model(self.model_filename, self.serialization_format)
--
-- initializer_tensor = model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_save_external_single_file_data(self) -> None:
-- model = onnx.load_model(self.model_filename, self.serialization_format)
--
-- temp_dir = os.path.join(self.temp_dir, "save_copy")
-- os.mkdir(temp_dir)
-- new_model_filename = os.path.join(temp_dir, "model.onnx")
-- onnx.save_model(model, new_model_filename, self.serialization_format)
--
-- new_model = onnx.load_model(new_model_filename, self.serialization_format)
-- initializer_tensor = new_model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = new_model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- @parameterized.parameterized.expand(
-- itertools.product(
-- (True, False),
-- )
-- )
-- def test_save_external_invalid_single_file_data_and_check(
-- self, use_absolute_path: bool
-- ) -> None:
-- model = onnx.load_model(self.model_filename, self.serialization_format)
--
-- model_dir = os.path.join(self.temp_dir, "save_copy")
-- os.mkdir(model_dir)
--
-- traversal_external_data_dir = os.path.join(
-- self.temp_dir, "invalid_external_data"
-- )
-- os.mkdir(traversal_external_data_dir)
--
-- if use_absolute_path:
-- traversal_external_data_location = os.path.join(
-- traversal_external_data_dir, "tensors.bin"
-- )
-- else:
-- traversal_external_data_location = "../invalid_external_data/tensors.bin"
--
-- external_data_dir = os.path.join(self.temp_dir, "external_data")
-- os.mkdir(external_data_dir)
-- new_model_filepath = os.path.join(model_dir, "model.onnx")
--
-- def convert_model_to_external_data_no_check(model: ModelProto, location: str):
-- for tensor in model.graph.initializer:
-- if tensor.HasField("raw_data"):
-- set_external_data(tensor, location)
--
-- convert_model_to_external_data_no_check(
-- model,
-- location=traversal_external_data_location,
-- )
--
-- with self.assertRaises(onnx.checker.ValidationError):
-- onnx.save_model(model, new_model_filepath, self.serialization_format)
--
--
--@parameterized.parameterized_class(
-- [
-- {"serialization_format": "protobuf"},
-- {"serialization_format": "textproto"},
-- ]
--)
--class TestSaveAllTensorsAsExternalData(unittest.TestCase):
-- serialization_format: str = "protobuf"
--
-- def setUp(self) -> None:
-- self._temp_dir_obj = tempfile.TemporaryDirectory()
-- self.temp_dir: str = self._temp_dir_obj.name
-- self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
-- self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
-- self.model = self.create_test_model_proto()
--
-- def get_temp_model_filename(self):
-- return os.path.join(self.temp_dir, str(uuid.uuid4()) + ".onnx")
--
-- def create_data_tensors(
-- self, tensors_data: list[tuple[list[Any], Any]]
-- ) -> list[TensorProto]:
-- tensors = []
-- for value, tensor_name in tensors_data:
-- tensor = from_array(np.array(value))
-- tensor.name = tensor_name
-- tensors.append(tensor)
--
-- return tensors
--
-- def create_test_model_proto(self) -> ModelProto:
-- tensors = self.create_data_tensors(
-- [
-- (self.attribute_value, "attribute_value"),
-- (self.initializer_value, "input_value"),
-- ]
-- )
--
-- constant_node = onnx.helper.make_node(
-- "Constant", inputs=[], outputs=["values"], value=tensors[0]
-- )
--
-- inputs = [
-- helper.make_tensor_value_info(
-- "input_value", onnx.TensorProto.FLOAT, self.initializer_value.shape
-- )
-- ]
--
-- graph = helper.make_graph(
-- [constant_node],
-- "test_graph",
-- inputs=inputs,
-- outputs=[],
-- initializer=[tensors[1]],
-- )
-- return helper.make_model(graph)
--
-- @unittest.skipIf(
-- serialization_format != "protobuf",
-- "check_model supports protobuf only when provided as a path",
-- )
-- def test_check_model(self) -> None:
-- checker.check_model(self.model)
--
-- def test_convert_model_to_external_data_with_size_threshold(self) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(self.model, size_threshold=1024)
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- self.assertFalse(initializer_tensor.HasField("data_location"))
--
-- def test_convert_model_to_external_data_without_size_threshold(self) -> None:
-- model_file_path = self.get_temp_model_filename()
-- convert_model_to_external_data(self.model, size_threshold=0)
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- self.assertTrue(initializer_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- def test_convert_model_to_external_data_from_one_file_with_location(self) -> None:
-- model_file_path = self.get_temp_model_filename()
-- external_data_file = str(uuid.uuid4())
--
-- convert_model_to_external_data(
-- self.model,
-- size_threshold=0,
-- all_tensors_to_one_file=True,
-- location=external_data_file,
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, external_data_file)))
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
--
-- # test convert model from external data
-- convert_model_from_external_data(model)
-- model_file_path = self.get_temp_model_filename()
-- onnx.save_model(model, model_file_path, self.serialization_format)
-- model = onnx.load_model(model_file_path, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- self.assertFalse(len(initializer_tensor.external_data))
-- self.assertEqual(initializer_tensor.data_location, TensorProto.DEFAULT)
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- self.assertFalse(len(attribute_tensor.external_data))
-- self.assertEqual(attribute_tensor.data_location, TensorProto.DEFAULT)
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_convert_model_to_external_data_from_one_file_without_location_uses_model_name(
-- self,
-- ) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(
-- self.model, size_threshold=0, all_tensors_to_one_file=True
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- self.assertTrue(os.path.isfile(model_file_path))
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, model_file_path)))
--
-- def test_convert_model_to_external_data_one_file_per_tensor_without_attribute(
-- self,
-- ) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(
-- self.model,
-- size_threshold=0,
-- all_tensors_to_one_file=False,
-- convert_attribute=False,
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- self.assertTrue(os.path.isfile(model_file_path))
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
-- self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
--
-- def test_convert_model_to_external_data_one_file_per_tensor_with_attribute(
-- self,
-- ) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(
-- self.model,
-- size_threshold=0,
-- all_tensors_to_one_file=False,
-- convert_attribute=True,
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- self.assertTrue(os.path.isfile(model_file_path))
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
--
-- def test_convert_model_to_external_data_does_not_convert_attribute_values(
-- self,
-- ) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(
-- self.model,
-- size_threshold=0,
-- convert_attribute=False,
-- all_tensors_to_one_file=False,
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
-- self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- self.assertTrue(initializer_tensor.HasField("data_location"))
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- self.assertFalse(attribute_tensor.HasField("data_location"))
--
-- def test_convert_model_to_external_data_converts_attribute_values(self) -> None:
-- model_file_path = self.get_temp_model_filename()
--
-- convert_model_to_external_data(
-- self.model, size_threshold=0, convert_attribute=True
-- )
-- onnx.save_model(self.model, model_file_path, self.serialization_format)
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
--
-- initializer_tensor = model.graph.initializer[0]
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
-- self.assertTrue(initializer_tensor.HasField("data_location"))
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
-- self.assertTrue(attribute_tensor.HasField("data_location"))
--
-- def test_save_model_does_not_convert_to_external_data_and_saves_the_model(
-- self,
-- ) -> None:
-- model_file_path = self.get_temp_model_filename()
-- onnx.save_model(
-- self.model,
-- model_file_path,
-- self.serialization_format,
-- save_as_external_data=False,
-- )
-- self.assertTrue(os.path.isfile(model_file_path))
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
-- initializer_tensor = model.graph.initializer[0]
-- self.assertFalse(initializer_tensor.HasField("data_location"))
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- self.assertFalse(attribute_tensor.HasField("data_location"))
--
-- def test_save_model_does_convert_and_saves_the_model(self) -> None:
-- model_file_path = self.get_temp_model_filename()
-- onnx.save_model(
-- self.model,
-- model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- all_tensors_to_one_file=True,
-- location=None,
-- size_threshold=0,
-- convert_attribute=False,
-- )
--
-- model = onnx.load_model(model_file_path, self.serialization_format)
--
-- initializer_tensor = model.graph.initializer[0]
-- self.assertTrue(initializer_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- self.assertFalse(attribute_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_save_model_without_loading_external_data(self) -> None:
-- model_file_path = self.get_temp_model_filename()
-- onnx.save_model(
-- self.model,
-- model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- location=None,
-- size_threshold=0,
-- convert_attribute=False,
-- )
-- # Save without load_external_data
-- model = onnx.load_model(
-- model_file_path, self.serialization_format, load_external_data=False
-- )
-- onnx.save_model(
-- model,
-- model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- location=None,
-- size_threshold=0,
-- convert_attribute=False,
-- )
-- # Load the saved model again; Only works if the saved path is under the same directory
-- model = onnx.load_model(model_file_path, self.serialization_format)
--
-- initializer_tensor = model.graph.initializer[0]
-- self.assertTrue(initializer_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
--
-- attribute_tensor = model.graph.node[0].attribute[0].t
-- self.assertFalse(attribute_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
--
-- def test_save_model_with_existing_raw_data_should_override(self) -> None:
-- model_file_path = self.get_temp_model_filename()
-- original_raw_data = self.model.graph.initializer[0].raw_data
-- onnx.save_model(
-- self.model,
-- model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- size_threshold=0,
-- )
-- self.assertTrue(os.path.isfile(model_file_path))
--
-- model = onnx.load_model(
-- model_file_path, self.serialization_format, load_external_data=False
-- )
-- initializer_tensor = model.graph.initializer[0]
-- initializer_tensor.raw_data = b"dummpy_raw_data"
-- # If raw_data and external tensor exist at the same time, override existing raw_data
-- load_external_data_for_tensor(initializer_tensor, self.temp_dir)
-- self.assertEqual(initializer_tensor.raw_data, original_raw_data)
--
--
--@parameterized.parameterized_class(
-- [
-- {"serialization_format": "protobuf"},
-- {"serialization_format": "textproto"},
-- ]
--)
--class TestExternalDataToArray(unittest.TestCase):
-- serialization_format: str = "protobuf"
--
-- def setUp(self) -> None:
-- self._temp_dir_obj = tempfile.TemporaryDirectory()
-- self.temp_dir: str = self._temp_dir_obj.name
-- self._model_file_path: str = os.path.join(self.temp_dir, "model.onnx")
-- self.large_data = np.random.rand(10, 60, 100).astype(np.float32)
-- self.small_data = (200, 300)
-- self.model = self.create_test_model()
--
-- @property
-- def model_file_path(self):
-- return self._model_file_path
--
-- def tearDown(self) -> None:
-- self._temp_dir_obj.cleanup()
--
-- def create_test_model(self) -> ModelProto:
-- X = helper.make_tensor_value_info("X", TensorProto.FLOAT, self.large_data.shape)
-- input_init = helper.make_tensor(
-- name="X",
-- data_type=TensorProto.FLOAT,
-- dims=self.large_data.shape,
-- vals=onnx.numpy_helper.tobytes_little_endian(self.large_data),
-- raw=True,
-- )
--
-- shape_data = np.array(self.small_data, np.int64)
-- shape_init = helper.make_tensor(
-- name="Shape",
-- data_type=TensorProto.INT64,
-- dims=shape_data.shape,
-- vals=onnx.numpy_helper.tobytes_little_endian(shape_data),
-- raw=True,
-- )
-- C = helper.make_tensor_value_info("C", TensorProto.INT64, self.small_data)
--
-- reshape = onnx.helper.make_node(
-- "Reshape",
-- inputs=["X", "Shape"],
-- outputs=["Y"],
-- )
-- cast = onnx.helper.make_node(
-- "Cast", inputs=["Y"], outputs=["C"], to=TensorProto.INT64
-- )
--
-- graph_def = helper.make_graph(
-- [reshape, cast],
-- "test-model",
-- [X],
-- [C],
-- initializer=[input_init, shape_init],
-- )
-- return helper.make_model(graph_def, producer_name="onnx-example")
--
-- @unittest.skipIf(
-- serialization_format != "protobuf",
-- "check_model supports protobuf only when provided as a path",
-- )
-- def test_check_model(self) -> None:
-- checker.check_model(self.model)
--
-- def test_reshape_inference_with_external_data_fail(self) -> None:
-- onnx.save_model(
-- self.model,
-- self.model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- all_tensors_to_one_file=False,
-- size_threshold=0,
-- )
-- model_without_external_data = onnx.load(
-- self.model_file_path, self.serialization_format, load_external_data=False
-- )
-- # Shape inference of Reshape uses ParseData
-- # ParseData cannot handle external data and should throw the error as follows:
-- # Cannot parse data from external tensors. Please load external data into raw data for tensor: Shape
-- self.assertRaises(
-- shape_inference.InferenceError,
-- shape_inference.infer_shapes,
-- model_without_external_data,
-- strict_mode=True,
-- )
--
-- def test_to_array_with_external_data(self) -> None:
-- onnx.save_model(
-- self.model,
-- self.model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- all_tensors_to_one_file=False,
-- size_threshold=0,
-- )
-- # raw_data of external tensor is not loaded
-- model = onnx.load(
-- self.model_file_path, self.serialization_format, load_external_data=False
-- )
-- # Specify self.temp_dir to load external tensor
-- loaded_large_data = to_array(model.graph.initializer[0], self.temp_dir)
-- np.testing.assert_allclose(loaded_large_data, self.large_data)
--
-- def test_save_model_with_external_data_multiple_times(self) -> None:
-- # Test onnx.save should respectively handle typical tensor and external tensor properly
-- # 1st save: save two tensors which have raw_data
-- # Only w_large will be stored as external tensors since it's larger than 1024
-- onnx.save_model(
-- self.model,
-- self.model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- all_tensors_to_one_file=False,
-- location=None,
-- size_threshold=1024,
-- convert_attribute=True,
-- )
-- model_without_loading_external = onnx.load(
-- self.model_file_path, self.serialization_format, load_external_data=False
-- )
-- large_input_tensor = model_without_loading_external.graph.initializer[0]
-- self.assertTrue(large_input_tensor.HasField("data_location"))
-- np.testing.assert_allclose(
-- to_array(large_input_tensor, self.temp_dir), self.large_data
-- )
--
-- small_shape_tensor = model_without_loading_external.graph.initializer[1]
-- self.assertTrue(not small_shape_tensor.HasField("data_location"))
-- np.testing.assert_allclose(to_array(small_shape_tensor), self.small_data)
--
-- # 2nd save: one tensor has raw_data (small); one external tensor (large)
-- # Save them both as external tensors this time
-- onnx.save_model(
-- model_without_loading_external,
-- self.model_file_path,
-- self.serialization_format,
-- save_as_external_data=True,
-- all_tensors_to_one_file=False,
-- location=None,
-- size_threshold=0,
-- convert_attribute=True,
-- )
--
-- model_without_loading_external = onnx.load(
-- self.model_file_path, self.serialization_format, load_external_data=False
-- )
-- large_input_tensor = model_without_loading_external.graph.initializer[0]
-- self.assertTrue(large_input_tensor.HasField("data_location"))
-- np.testing.assert_allclose(
-- to_array(large_input_tensor, self.temp_dir), self.large_data
-- )
--
-- small_shape_tensor = model_without_loading_external.graph.initializer[1]
-- self.assertTrue(small_shape_tensor.HasField("data_location"))
-- np.testing.assert_allclose(
-- to_array(small_shape_tensor, self.temp_dir), self.small_data
-- )
--
--
- class TestNotAllowToLoadExternalDataOutsideModelDirectory(TestLoadExternalDataBase):
- """Essential test to check that onnx (validate) C++ code will not allow to load external_data outside the model
- directory.
-@@ -797,17 +186,6 @@ class TestNotAllowToLoadExternalDataOutsideModelDirectoryOnWindows(
- checker.check_model(self.model_filename)
-
-
--class TestSaveAllTensorsAsExternalDataWithPath(TestSaveAllTensorsAsExternalData):
-- def get_temp_model_filename(self) -> pathlib.Path:
-- return pathlib.Path(super().get_temp_model_filename())
--
--
--class TestExternalDataToArrayWithPath(TestExternalDataToArray):
-- @property
-- def model_file_path(self) -> pathlib.Path:
-- return pathlib.Path(self._model_file_path)
--
--
- class TestFunctionsAndSubGraphs(unittest.TestCase):
- def setUp(self) -> None:
- self._temp_dir_obj = tempfile.TemporaryDirectory()
-diff --git a/onnx/test/version_converter/automatic_downgrade_test.py b/onnx/test/version_converter/automatic_downgrade_test.py
-index a94aea1a9..f639d44f8 100644
---- a/onnx/test/version_converter/automatic_downgrade_test.py
-+++ b/onnx/test/version_converter/automatic_downgrade_test.py
-@@ -7,7 +7,6 @@ import unittest
-
- import automatic_conversion_test_base
- import numpy as np
--import parameterized
-
- import onnx
- from onnx import helper
-@@ -22,35 +21,6 @@ class TestAutomaticDowngrade(automatic_conversion_test_base.TestAutomaticConvers
- def _test_op_downgrade(self, op: str, *args, **kwargs):
- self._test_op_conversion(op, *args, **kwargs, is_upgrade=False)
-
-- @parameterized.parameterized.expand(
-- [
-- "ReduceL1",
-- "ReduceL2",
-- "ReduceLogSum",
-- "ReduceLogSumExp",
-- "ReduceMean",
-- "ReduceMax",
-- "ReduceMin",
-- "ReduceProd",
-- "ReduceSum",
-- "ReduceSumSquare",
-- ]
-- )
-- def test_reduce_ops(self, op) -> None:
-- # TODO: need to add test cases for missing axes input which depends on this pr:
-- # https://github.com/onnx/onnx/pull/5613
-- axes = helper.make_tensor(
-- "b", onnx.TensorProto.INT64, dims=[3], vals=np.array([0, 1, 2])
-- )
-- self._test_op_downgrade(
-- op,
-- from_opset=13,
-- input_shapes=[[3, 4, 5], [3]],
-- output_shapes=[[1, 1, 1]],
-- input_types=[onnx.TensorProto.FLOAT, onnx.TensorProto.INT64],
-- initializer=[axes],
-- )
--
- def test_dft20_no_axis(self) -> None:
- self._test_model_conversion(
- to_opset=19,
-diff --git a/onnx/test/version_converter_test.py b/onnx/test/version_converter_test.py
-index a75edcde7..fd057518f 100644
---- a/onnx/test/version_converter_test.py
-+++ b/onnx/test/version_converter_test.py
-@@ -10,7 +10,6 @@ import tempfile
- import unittest
-
- import numpy as np
--import parameterized
-
- import onnx.version_converter
- from onnx import (
-@@ -2115,169 +2114,6 @@ class TestVersionConverter(unittest.TestCase):
- assert converted_model.graph.node[0].attribute[0].i == 2
- assert converted_model.opset_import[0].version == 12
-
-- @parameterized.parameterized.expand(
-- [
-- ("per_tensor", (16, 3), (1,), None, None, None, TensorProto.INT8, True),
-- (
-- "per_axis_none_block_shape",
-- (16, 3),
-- (16,),
-- 1,
-- None,
-- None,
-- TensorProto.INT8,
-- True,
-- ),
-- (
-- "per_axis_zero_block_shape",
-- (16, 3),
-- (16,),
-- 1,
-- 0,
-- None,
-- TensorProto.INT8,
-- True,
-- ),
-- (
-- "per_tensor_positive_block_shape",
-- (16, 3),
-- (1,),
-- 1,
-- 2,
-- None,
-- TensorProto.INT8,
-- False,
-- ),
-- (
-- "per_axis_positive_block_shape",
-- (16, 3),
-- (16,),
-- 1,
-- 2,
-- None,
-- TensorProto.INT8,
-- False,
-- ),
-- ("blocked_2d", (16, 3), (4, 3), 0, 4, None, TensorProto.INT8, False),
-- ("blocked_3d", (4, 3, 32), (4, 3, 8), 2, 4, None, TensorProto.INT8, False),
-- (
-- "per_axis_output_dtype",
-- (16, 3),
-- (16,),
-- 1,
-- None,
-- TensorProto.FLOAT8E4M3FN,
-- None,
-- False,
-- ),
-- (
-- "per_axis_unsupported_type",
-- (16, 3),
-- (16,),
-- 1,
-- None,
-- None,
-- TensorProto.UINT16,
-- False,
-- ),
-- ]
-- )
-- def test_quantize_21_20(
-- self,
-- _: str,
-- x_shape: tuple[int, ...],
-- scale_shape: tuple[int, ...],
-- axis: int,
-- block_size: int,
-- output_dtype: int | None,
-- zero_point_dtype: int | None,
-- compatible: bool,
-- ) -> None:
-- def test(
-- input_shape, scale_shape, axis, block_size, output_dtype, zero_point_dtype
-- ) -> None:
-- nodes = [
-- helper.make_node(
-- "QuantizeLinear",
-- ["X", "S"],
-- ["Y"],
-- axis=axis,
-- block_size=block_size,
-- output_dtype=output_dtype,
-- )
-- ]
-- inputs = [
-- helper.make_tensor_value_info("X", TensorProto.FLOAT, input_shape),
-- helper.make_tensor_value_info("S", TensorProto.FLOAT, scale_shape),
-- ]
-- if zero_point_dtype:
-- inputs.append(
-- helper.make_tensor_value_info("ZP", zero_point_dtype, scale_shape)
-- )
-- nodes[0].input.append("ZP")
-- output_type_ = output_dtype or zero_point_dtype
-- graph = helper.make_graph(
-- nodes,
-- "test",
-- inputs,
-- [helper.make_tensor_value_info("Y", output_type_, input_shape)],
-- )
-- _ = self._converted(graph, helper.make_operatorsetid("", 21), 20)
--
-- context_manager = (
-- contextlib.nullcontext() if compatible else self.assertRaises(RuntimeError)
-- )
-- with context_manager:
-- test(x_shape, scale_shape, axis, block_size, output_dtype, zero_point_dtype)
--
-- @parameterized.parameterized.expand(
-- [
-- ("per_tensor", (16, 3), (1,), None, None, True),
-- ("per_axis_none_block_shape", (16, 3), (16,), 1, None, True),
-- ("per_axis_zero_block_shape", (16, 3), (16,), 1, 0, True),
-- ("per_tensor_positive_block_shape", (16, 3), (1,), 1, 2, False),
-- ("per_axis_positive_block_shape", (16, 3), (16,), 1, 2, False),
-- ("blocked_2d", (16, 3), (4, 3), 0, 4, False),
-- ("blocked_3d", (4, 3, 32), (4, 3, 8), 2, 4, False),
-- ]
-- )
-- def test_dequantize_21_20(
-- self,
-- _: str,
-- y_shape: tuple[int, ...],
-- scale_shape: tuple[int, ...],
-- axis: int,
-- block_size: int,
-- compatible: bool,
-- ) -> None:
-- def test(input_shape, scale_shape, axis, block_size) -> None:
-- nodes = [
-- helper.make_node(
-- "DequantizeLinear",
-- ["X", "S", "ZP"],
-- ["Y"],
-- axis=axis,
-- block_size=block_size,
-- )
-- ]
-- graph = helper.make_graph(
-- nodes,
-- "test",
-- [
-- helper.make_tensor_value_info("X", TensorProto.INT8, input_shape),
-- helper.make_tensor_value_info("S", TensorProto.FLOAT, scale_shape),
-- helper.make_tensor_value_info("ZP", TensorProto.INT8, scale_shape),
-- ],
-- [helper.make_tensor_value_info("Y", TensorProto.FLOAT, input_shape)],
-- )
-- _ = self._converted(graph, helper.make_operatorsetid("", 21), 20)
--
-- context_manager = (
-- contextlib.nullcontext() if compatible else self.assertRaises(RuntimeError)
-- )
-- with context_manager:
-- test(y_shape, scale_shape, axis, block_size)
--
- def test_external_data_version_conversion(self) -> None:
- with tempfile.TemporaryDirectory() as temp_dir:
- # Create a model with external data
-@@ -2413,76 +2249,6 @@ class TestVersionConverter(unittest.TestCase):
- [helper.make_tensor_value_info("out", dtype, data_s)],
- )
-
-- # Scatter 16 -> 15: TypeRestriction (bfloat16) + RemoveAttribute (reduction)
-- @parameterized.parameterized.expand([("ScatterElements",), ("ScatterND",)])
-- def test_scatter_16_15_success(self, op_name: str) -> None:
-- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, "none")
-- converted = self._converted(graph, helper.make_operatorsetid("", 16), 15)
-- assert converted.opset_import[0].version == 15
--
-- @parameterized.parameterized.expand([("ScatterElements",), ("ScatterND",)])
-- def test_scatter_16_15_bfloat16_fails(self, op_name: str) -> None:
-- def test() -> None:
-- graph = self._make_scatter_graph(op_name, TensorProto.BFLOAT16)
-- self._converted(graph, helper.make_operatorsetid("", 16), 15)
--
-- self.assertRaises(RuntimeError, test)
--
-- # Opset 16 added reduction 'add' and 'mul'; 16 -> 15 only allows 'none'
-- @parameterized.parameterized.expand(
-- [
-- ("ScatterElements", "add"),
-- ("ScatterElements", "mul"),
-- ("ScatterND", "add"),
-- ("ScatterND", "mul"),
-- ]
-- )
-- def test_scatter_16_15_reduction_add_mul_fails(
-- self, op_name: str, reduction: str
-- ) -> None:
-- def test() -> None:
-- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
-- self._converted(graph, helper.make_operatorsetid("", 16), 15)
--
-- self.assertRaises(RuntimeError, test)
--
-- # Scatter 18 -> 17: reject reduction "max" / "min"
-- @parameterized.parameterized.expand(
-- [
-- ("ScatterElements", "max"),
-- ("ScatterElements", "min"),
-- ("ScatterND", "max"),
-- ("ScatterND", "min"),
-- ]
-- )
-- def test_scatter_18_17_reduction_max_min_fails(
-- self, op_name: str, reduction: str
-- ) -> None:
-- def test() -> None:
-- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
-- self._converted(graph, helper.make_operatorsetid("", 18), 17)
--
-- self.assertRaises(RuntimeError, test)
--
-- @parameterized.parameterized.expand(
-- [
-- ("ScatterElements", None),
-- ("ScatterElements", "none"),
-- ("ScatterElements", "add"),
-- ("ScatterElements", "mul"),
-- ("ScatterND", None),
-- ("ScatterND", "none"),
-- ("ScatterND", "add"),
-- ("ScatterND", "mul"),
-- ]
-- )
-- def test_scatter_18_17_allowed_reductions_success(
-- self, op_name: str, reduction: str | None
-- ) -> None:
-- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
-- converted = self._converted(graph, helper.make_operatorsetid("", 18), 17)
-- checker.check_model(converted)
--
-
- if __name__ == "__main__":
- unittest.main()
-diff --git a/requirements-dev.txt b/requirements-dev.txt
-index e36ecd6d9..7e4a0ccd4 100644
---- a/requirements-dev.txt
-+++ b/requirements-dev.txt
-@@ -1,6 +1,5 @@
- build
- numpy
--parameterized
- protobuf
- pytest
- pytest-cov
---
-2.54.0
-
diff --git a/0003-Fix-nanobind-arguments.patch b/0003-Fix-nanobind-arguments.patch
new file mode 100644
index 0000000..5225d8a
--- /dev/null
+++ b/0003-Fix-nanobind-arguments.patch
@@ -0,0 +1,22 @@
+From d080c035f5e46bc659264ba1a50f2f9c7053eae7 Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:03:14 -0400
+Subject: [PATCH 3/7] Fix nanobind arguments
+
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 094078160..3a08d3289 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -428,7 +428,7 @@ if(ONNX_BUILD_PYTHON)
+ # find system nanobind
+ if(NOT DEFINED nanobind_DIR OR "${nanobind_DIR}" STREQUAL "")
+ execute_process(
+- COMMAND ${Python_EXECUTABLE} -m nanobind --cmake-dir
++ COMMAND ${Python_EXECUTABLE} -m nanobind --cmake_dir
+ RESULT_VARIABLE NANOBIND_CMAKE_RESULT
+ OUTPUT_VARIABLE NANOBIND_CMAKE_DIR
+ OUTPUT_STRIP_TRAILING_WHITESPACE
+--
+2.54.0
+
diff --git a/0004-Let-pyproject_wheel-use-binaries-from-cmake_build.patch b/0004-Let-pyproject_wheel-use-binaries-from-cmake_build.patch
new file mode 100644
index 0000000..97d15c4
--- /dev/null
+++ b/0004-Let-pyproject_wheel-use-binaries-from-cmake_build.patch
@@ -0,0 +1,30 @@
+From 75e1082f2d17c2c2ccb3c38b429fa24a4b45bbea Mon Sep 17 00:00:00 2001
+From: Alejandro Alvarez Ayllon <a.alvarezayllon@gmail.com>
+Date: Sat, 24 Feb 2024 14:53:10 +0100
+Subject: [PATCH 4/7] Let pyproject_wheel use binaries from cmake_build
+
+
+diff --git a/setup.py b/setup.py
+index db78bfa2b..2dfd65024 100644
+--- a/setup.py
++++ b/setup.py
+@@ -27,7 +27,7 @@ import setuptools.command.build_py
+ import setuptools.command.develop
+
+ TOP_DIR = os.path.realpath(os.path.dirname(__file__))
+-CMAKE_BUILD_DIR = os.path.join(TOP_DIR, ".setuptools-cmake-build")
++CMAKE_BUILD_DIR = os.path.join(TOP_DIR, os.environ.get("VPATH_BUILDDIR", "redhat-linux-build"))
+
+ WINDOWS = os.name == "nt"
+
+@@ -173,6 +173,7 @@ class CmakeBuild(setuptools.Command):
+ self.jobs = multiprocessing.cpu_count()
+
+ def run(self):
++ return
+ assert CMAKE, "Could not find cmake in PATH"
+
+ os.makedirs(CMAKE_BUILD_DIR, exist_ok=True)
+--
+2.54.0
+
diff --git a/0005-Python-binary-should-link-to-system-onnx.patch b/0005-Python-binary-should-link-to-system-onnx.patch
new file mode 100644
index 0000000..8658267
--- /dev/null
+++ b/0005-Python-binary-should-link-to-system-onnx.patch
@@ -0,0 +1,30 @@
+From d8a3356e3daf6d2514f84ff0cd6b240e8113931d Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:06:18 -0400
+Subject: [PATCH 5/7] Python binary should link to system onnx
+
+
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 3a08d3289..895a61d84 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -460,8 +460,15 @@ if(ONNX_BUILD_PYTHON)
+ nanobind_add_module(
+ onnx_cpp2py_export
+ NB_STATIC NB_DOMAIN onnx STABLE_ABI FREE_THREADED LTO
+- "${ONNX_ROOT}/onnx/cpp2py_export.cc" ${ONNX_SRCS} ${ONNX_PROTO_SRCS})
++ "${ONNX_ROOT}/onnx/cpp2py_export.cc")
+ add_dependencies(onnx_cpp2py_export onnx)
++ target_link_libraries(
++ onnx_cpp2py_export
++ PRIVATE
++ onnx
++ onnx_proto
++ protobuf
++ )
+ endif()
+
+ add_onnx_compile_options(onnx_proto)
+--
+2.54.0
+
diff --git a/0006-Remove-python-parameterized-dependency.patch b/0006-Remove-python-parameterized-dependency.patch
new file mode 100644
index 0000000..ef1fbaf
--- /dev/null
+++ b/0006-Remove-python-parameterized-dependency.patch
@@ -0,0 +1,5739 @@
+From 9ca91650b3fb27b09bae885464ec9b6e29292855 Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:06:57 -0400
+Subject: [PATCH 6/7] Remove python parameterized dependency
+
+
+diff --git a/onnx/test/basic_test.py b/onnx/test/basic_test.py
+index 9713e4d1f..6d6c6c90e 100644
+--- a/onnx/test/basic_test.py
++++ b/onnx/test/basic_test.py
+@@ -11,7 +11,6 @@ import unittest
+
+ import google.protobuf.message
+ import google.protobuf.text_format
+-import parameterized
+
+ import onnx
+ from onnx import serialization
+@@ -34,97 +33,6 @@ def _simple_tensor() -> onnx.TensorProto:
+ )
+
+
+-@parameterized.parameterized_class(
+- [
+- {"format": "protobuf"},
+- {"format": "textproto"},
+- {"format": "json"},
+- {"format": "onnxtxt"},
+- ]
+-)
+-class TestIO(unittest.TestCase):
+- format: str
+-
+- def test_load_model_when_input_is_bytes(self) -> None:
+- proto = _simple_model()
+- proto_string = serialization.registry.get(self.format).serialize_proto(proto)
+- loaded_proto = onnx.load_model_from_string(proto_string, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_model_when_input_has_read_function(self) -> None:
+- proto = _simple_model()
+- # When the proto is a bytes representation provided to `save_model`,
+- # it should always be a serialized binary protobuf representation. Aka. format="protobuf"
+- # The saved file format is specified by the `format` argument.
+- proto_string = serialization.registry.get("protobuf").serialize_proto(proto)
+- f = io.BytesIO()
+- onnx.save_model(proto_string, f, format=self.format)
+- loaded_proto = onnx.load_model(io.BytesIO(f.getvalue()), format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_model_when_input_is_file_name(self) -> None:
+- proto = _simple_model()
+- with tempfile.TemporaryDirectory() as temp_dir:
+- model_path = os.path.join(temp_dir, "model.onnx")
+- onnx.save_model(proto, model_path, format=self.format)
+- loaded_proto = onnx.load_model(model_path, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_model_when_input_is_pathlike(self) -> None:
+- proto = _simple_model()
+- with tempfile.TemporaryDirectory() as temp_dir:
+- model_path = pathlib.Path(temp_dir, "model.onnx")
+- onnx.save_model(proto, model_path, format=self.format)
+- loaded_proto = onnx.load_model(model_path, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+-
+-@parameterized.parameterized_class(
+- [
+- {"format": "protobuf"},
+- {"format": "textproto"},
+- {"format": "json"},
+- # The onnxtxt format does not support saving/loading tensors yet
+- ]
+-)
+-class TestIOTensor(unittest.TestCase):
+- """Test loading and saving of TensorProto."""
+-
+- format: str
+-
+- def test_load_tensor_when_input_is_bytes(self) -> None:
+- proto = _simple_tensor()
+- proto_string = serialization.registry.get(self.format).serialize_proto(proto)
+- loaded_proto = onnx.load_tensor_from_string(proto_string, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_tensor_when_input_has_read_function(self) -> None:
+- # Test if input has a read function
+- proto = _simple_tensor()
+- f = io.BytesIO()
+- onnx.save_tensor(proto, f, format=self.format)
+- loaded_proto = onnx.load_tensor(io.BytesIO(f.getvalue()), format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_tensor_when_input_is_file_name(self) -> None:
+- # Test if input is a file name
+- proto = _simple_tensor()
+- with tempfile.TemporaryDirectory() as temp_dir:
+- model_path = os.path.join(temp_dir, "model.onnx")
+- onnx.save_tensor(proto, model_path, format=self.format)
+- loaded_proto = onnx.load_tensor(model_path, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+- def test_save_and_load_tensor_when_input_is_pathlike(self) -> None:
+- # Test if input is a file name
+- proto = _simple_tensor()
+- with tempfile.TemporaryDirectory() as temp_dir:
+- model_path = pathlib.Path(temp_dir, "model.onnx")
+- onnx.save_tensor(proto, model_path, format=self.format)
+- loaded_proto = onnx.load_tensor(model_path, format=self.format)
+- self.assertEqual(proto, loaded_proto)
+-
+-
+ class TestSaveAndLoadFileExtensions(unittest.TestCase):
+ def test_save_model_picks_correct_format_from_extension(self) -> None:
+ proto = _simple_model()
+diff --git a/onnx/test/helper_test.py b/onnx/test/helper_test.py
+index 27793f561..91f7739aa 100644
+--- a/onnx/test/helper_test.py
++++ b/onnx/test/helper_test.py
+@@ -12,7 +12,6 @@ from typing import Any
+ import ml_dtypes
+ import numpy as np
+ import numpy.typing as npt
+-import parameterized
+ import pytest
+
+ from onnx import (
+@@ -636,63 +635,6 @@ class TestHelperTensorFunctions(unittest.TestCase):
+ expected.view(np.uint8),
+ )
+
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT4, TensorProto.INT4),
+- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
+- )
+- )
+- def test_make_4bit_tensor(self, dtype, dims) -> None:
+- type_range = {
+- TensorProto.UINT4: (0, 15),
+- TensorProto.INT4: (-8, 7),
+- }
+- data = np.random.randint(
+- type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
+- )
+- y = helper.make_tensor("y", dtype, data.shape, data)
+-
+- # Check the expected size of int32_data in bytes
+- expected_data_size = math.ceil(np.prod(data.shape) / 2.0)
+- actual_data_size = len(bytes(y.int32_data))
+- np.testing.assert_equal(actual_data_size, expected_data_size)
+-
+- # Check the expected data values.
+- ynp = numpy_helper.to_array(y)
+- np.testing.assert_equal(ynp, data)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
+- )
+- )
+- def test_4bit_tensor_size(self, dims) -> None:
+- # A bug caused negative int4 values to inflate tensor size.
+- # So, test negative values here.
+- num_elems = np.prod(dims)
+- data = np.array([-4] * num_elems, dtype=np.int8).reshape(dims)
+- y = helper.make_tensor("y", TensorProto.INT4, data.shape, data)
+-
+- # Check the expected size of int32_data in bytes
+- expected_data_size = math.ceil(num_elems / 2.0)
+- actual_data_size = len(bytes(y.int32_data))
+- np.testing.assert_equal(actual_data_size, expected_data_size)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT4, TensorProto.INT4), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
+- )
+- )
+- def test_make_4bit_raw_tensor(self, dtype, dims) -> None:
+- data = np.random.randint(0, high=16, size=dims, dtype=np.uint8)
+- packed_data = _pack_4bit(data)
+-
+- y = helper.make_tensor(
+- "packed_int4", dtype, dims, packed_data.tobytes(), raw=True
+- )
+- ynp = numpy_helper.to_array(y)
+- np.testing.assert_equal(ynp.view(np.uint8), data)
+-
+ def test_make_float4e2m1_raw_tensor(self) -> None:
+ data = np.array([0, 0.5, 1, 240, 10, -2], dtype=ml_dtypes.float4_e2m1fn)
+ expected = data.view(np.uint8)
+@@ -707,63 +649,6 @@ class TestHelperTensorFunctions(unittest.TestCase):
+ ynp = numpy_helper.to_array(y)
+ np.testing.assert_equal(ynp.view(np.uint8), expected)
+
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT2, TensorProto.INT2),
+- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
+- )
+- )
+- def test_make_2bit_tensor(self, dtype, dims) -> None:
+- type_range = {
+- TensorProto.UINT2: (0, 3),
+- TensorProto.INT2: (-2, 1),
+- }
+- data = np.random.randint(
+- type_range[dtype][0], high=type_range[dtype][1] + 1, size=dims
+- )
+- y = helper.make_tensor("y", dtype, data.shape, data)
+-
+- # Check the expected size of int32_data in bytes
+- expected_data_size = math.ceil(np.prod(data.shape) / 4.0)
+- actual_data_size = len(bytes(y.int32_data))
+- np.testing.assert_equal(actual_data_size, expected_data_size)
+-
+- # Check the expected data values.
+- ynp = numpy_helper.to_array(y)
+- np.testing.assert_equal(ynp, data)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- ((5, 4, 6), (4, 6, 5), (3, 3), (1,), (2**10,)),
+- )
+- )
+- def test_2bit_tensor_size(self, dims) -> None:
+- # A bug caused negative int2 values to inflate tensor size.
+- # So, test negative values here.
+- num_elems = np.prod(dims)
+- data = np.array([-2] * num_elems, dtype=np.int8).reshape(dims)
+- y = helper.make_tensor("y", TensorProto.INT2, data.shape, data)
+-
+- # Check the expected size of int32_data in bytes
+- expected_data_size = math.ceil(num_elems / 4.0)
+- actual_data_size = len(bytes(y.int32_data))
+- np.testing.assert_equal(actual_data_size, expected_data_size)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT2, TensorProto.INT2), ((5, 4, 6), (4, 6, 5), (3, 3), (1,))
+- )
+- )
+- def test_make_2bit_raw_tensor(self, dtype, dims) -> None:
+- data = np.random.randint(0, high=4, size=dims, dtype=np.uint8)
+- packed_data = _pack_2bit(data)
+-
+- y = helper.make_tensor(
+- "packed_int2", dtype, dims, packed_data.tobytes(), raw=True
+- )
+- ynp = numpy_helper.to_array(y)
+- np.testing.assert_equal(ynp.view(np.uint8), data)
+-
+ def test_make_float4e2m1_tensor(self) -> None:
+ y = helper.make_tensor(
+ "zero_point",
+@@ -1105,28 +990,6 @@ class TestHelperMappingFunctions(unittest.TestCase):
+
+
+ class TestAttrTypeToStr(unittest.TestCase):
+- @parameterized.parameterized.expand(
+- [
+- (AttributeProto.AttributeType.FLOAT, "FLOAT"),
+- (AttributeProto.AttributeType.INT, "INT"),
+- (AttributeProto.AttributeType.STRING, "STRING"),
+- (AttributeProto.AttributeType.TENSOR, "TENSOR"),
+- (AttributeProto.AttributeType.GRAPH, "GRAPH"),
+- (AttributeProto.AttributeType.SPARSE_TENSOR, "SPARSE_TENSOR"),
+- (AttributeProto.AttributeType.TYPE_PROTO, "TYPE_PROTO"),
+- (AttributeProto.AttributeType.FLOATS, "FLOATS"),
+- (AttributeProto.AttributeType.INTS, "INTS"),
+- (AttributeProto.AttributeType.STRINGS, "STRINGS"),
+- (AttributeProto.AttributeType.TENSORS, "TENSORS"),
+- (AttributeProto.AttributeType.GRAPHS, "GRAPHS"),
+- (AttributeProto.AttributeType.SPARSE_TENSORS, "SPARSE_TENSORS"),
+- (AttributeProto.AttributeType.TYPE_PROTOS, "TYPE_PROTOS"),
+- ]
+- )
+- def test_attr_type_to_str(self, attr_type, expected_str):
+- result = helper._attr_type_to_str(attr_type)
+- self.assertEqual(result, expected_str)
+-
+ def test_attr_type_to_str_undefined(self):
+ result = helper._attr_type_to_str(9999)
+ self.assertEqual(result, "UNDEFINED")
+diff --git a/onnx/test/node_shape_inference_test.py b/onnx/test/node_shape_inference_test.py
+index ece776e48..63252b922 100644
+--- a/onnx/test/node_shape_inference_test.py
++++ b/onnx/test/node_shape_inference_test.py
+@@ -5,34 +5,9 @@ from __future__ import annotations
+
+ import unittest
+
+-import parameterized
+-
+ import onnx.helper
+ import onnx.shape_inference
+
+
+-class NodeInferenceTest(unittest.TestCase):
+- @parameterized.parameterized.expand(
+- [
+- ("GreaterOrEqual",),
+- ("LessOrEqual",),
+- ]
+- )
+- def test_comparison_op(self, op_type):
+- node = onnx.helper.make_node(op_type, ["x", "y"], ["z"])
+- schema = onnx.defs.get_schema(node.op_type, 23, "")
+- xtype = onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT32, [1, 10])
+- ytype = onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT32, [10, 1])
+- result = onnx.shape_inference.infer_node_outputs(
+- schema, node, {"x": xtype, "y": ytype}
+- )
+- self.assertEqual(list(result.keys()), ["z"])
+- self.assertEqual(result["z"].tensor_type.elem_type, onnx.TensorProto.BOOL)
+- self.assertEqual(
+- [dim.dim_value for dim in result["z"].tensor_type.shape.dim],
+- [10, 10],
+- )
+-
+-
+ if __name__ == "__main__":
+ unittest.main()
+diff --git a/onnx/test/numpy_helper_test.py b/onnx/test/numpy_helper_test.py
+index 568000550..2265008d2 100644
+--- a/onnx/test/numpy_helper_test.py
++++ b/onnx/test/numpy_helper_test.py
+@@ -6,7 +6,6 @@ from __future__ import annotations
+ import unittest
+
+ import numpy as np
+-import parameterized
+
+ import onnx
+ import onnx.reference
+@@ -148,38 +147,6 @@ class TestNumpyHelper(unittest.TestCase):
+ self.assertEqual(tp.SerializeToString(), again.SerializeToString())
+ self.assertEqual(tp.data_type, helper.np_dtype_to_tensor_dtype(back.dtype))
+
+- @parameterized.parameterized.expand(
+- [
+- ("FLOAT", onnx.TensorProto.FLOAT),
+- ("UINT8", onnx.TensorProto.UINT8),
+- ("INT8", onnx.TensorProto.INT8),
+- ("UINT16", onnx.TensorProto.UINT16),
+- ("INT16", onnx.TensorProto.INT16),
+- ("INT32", onnx.TensorProto.INT32),
+- ("INT64", onnx.TensorProto.INT64),
+- ("BOOL", onnx.TensorProto.BOOL),
+- ("FLOAT16", onnx.TensorProto.FLOAT16),
+- ("DOUBLE", onnx.TensorProto.DOUBLE),
+- ("UINT32", onnx.TensorProto.UINT32),
+- ("UINT64", onnx.TensorProto.UINT64),
+- ("COMPLEX64", onnx.TensorProto.COMPLEX64),
+- ("COMPLEX128", onnx.TensorProto.COMPLEX128),
+- ("BFLOAT16", onnx.TensorProto.BFLOAT16),
+- ("FLOAT8E4M3FN", onnx.TensorProto.FLOAT8E4M3FN),
+- ("FLOAT8E4M3FNUZ", onnx.TensorProto.FLOAT8E4M3FNUZ),
+- ("FLOAT8E5M2", onnx.TensorProto.FLOAT8E5M2),
+- ("FLOAT8E5M2FNUZ", onnx.TensorProto.FLOAT8E5M2FNUZ),
+- ("FLOAT8E8M0", onnx.TensorProto.FLOAT8E8M0),
+- ("UINT4", onnx.TensorProto.UINT4),
+- ("INT4", onnx.TensorProto.INT4),
+- ("UINT2", onnx.TensorProto.UINT2),
+- ("INT2", onnx.TensorProto.INT2),
+- ("FLOAT4E2M1", onnx.TensorProto.FLOAT4E2M1),
+- ]
+- )
+- def test_to_array_from_array(self, _: str, data_type: onnx.TensorProto.DataType):
+- self._to_array_from_array(data_type)
+-
+ def test_to_array_from_array_string(self):
+ self._to_array_from_array(onnx.TensorProto.STRING, False)
+
+diff --git a/onnx/test/parser_test.py b/onnx/test/parser_test.py
+index 19ee40792..9e22f19ae 100644
+--- a/onnx/test/parser_test.py
++++ b/onnx/test/parser_test.py
+@@ -5,8 +5,6 @@ from __future__ import annotations
+
+ import unittest
+
+-from parameterized import parameterized
+-
+ import onnx
+ from onnx import GraphProto, OperatorSetIdProto, TensorProto, checker
+
+@@ -120,94 +118,6 @@ class TestBasicFunctions(unittest.TestCase):
+ model = onnx.parser.parse_model(input)
+ checker.check_model(model)
+
+- @parameterized.expand(
+- [
+- (
+- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu(x) }",
+- {},
+- ),
+- (
+- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0>(x) }",
+- {"alpha": 2.0},
+- ),
+- (
+- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<gamma=3.0>(x) }",
+- {"gamma": 3.0},
+- ),
+- (
+- "agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x) }",
+- {"alpha": 2.0, "gamma": 3.0},
+- ),
+- ]
+- )
+- def test_composite_parse_function_with_attributes(
+- self, graph_text: str, expected_attribute: dict
+- ) -> None:
+- default_alpha = 1.67326319217681884765625
+- default_gamma = 1.05070102214813232421875
+-
+- def expect_custom_node_attribute(node, attributes):
+- for key in attributes:
+- match_attr = [attr for attr in node.attribute if attr.name == key]
+- assert len(match_attr) == 1
+- assert match_attr[0].f == attributes[key]
+-
+- def expect_model_function_attribute(model):
+- assert len(model.functions[0].attribute_proto) == 2
+- attr_proto_alpha = [
+- attr_proto
+- for attr_proto in model.functions[0].attribute_proto
+- if attr_proto.name == "alpha"
+- ]
+- assert len(attr_proto_alpha) == 1 and attr_proto_alpha[0].f == default_alpha
+- attr_proto_gamma = [
+- attr_proto
+- for attr_proto in model.functions[0].attribute_proto
+- if attr_proto.name == "gamma"
+- ]
+- assert len(attr_proto_gamma) == 1 and attr_proto_gamma[0].f == default_gamma
+-
+- function_text = f"""
+- <
+- domain: "custom_domain",
+- opset_import: [ "" : 15],
+- doc_string: "Test function proto"
+- >
+- Selu
+- <alpha: float={default_alpha}, gamma: float={default_gamma}>
+- (X) => (C)
+- {{
+- constant_alpha = Constant<value_float: float=@alpha>()
+- constant_gamma = Constant<value_float: float=@gamma>()
+- alpha_x = CastLike(constant_alpha, X)
+- gamma_x = CastLike(constant_gamma, X)
+- exp_x = Exp(X)
+- alpha_x_exp_x = Mul(alpha_x, exp_x)
+- alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
+- neg = Mul(gamma_x, alpha_x_exp_x_)
+- pos = Mul(gamma_x, X)
+- _zero = Constant<value_float=0.0>()
+- zero = CastLike(_zero, X)
+- less_eq = LessOrEqual(X, zero)
+- C = Where(less_eq, neg, pos)
+- }}
+- """
+-
+- functions = [onnx.parser.parse_function(function_text)]
+- graph = onnx.parser.parse_graph(graph_text)
+- opset_imports = [
+- OperatorSetIdProto(domain="", version=15),
+- OperatorSetIdProto(domain="custom_domain", version=1),
+- ]
+-
+- model = onnx.helper.make_model(
+- graph, functions=functions, opset_imports=opset_imports
+- )
+- checker.check_model(model)
+-
+- expect_model_function_attribute(model)
+- expect_custom_node_attribute(model.graph.node[0], expected_attribute)
+-
+ def test_parse_node(self):
+ node = onnx.parser.parse_node(
+ "out1, out2 = SomeDomain.SomeOp <attr1 = 1> (in1, in2)"
+@@ -242,101 +152,6 @@ class TestBasicFunctions(unittest.TestCase):
+ self.assertEqual(list(node.input), ["", "y"])
+ self.assertEqual(list(node.output), ["", "x"])
+
+- @parameterized.expand(
+- [
+- ("not_a_good_float", True),
+- ("inf1", True),
+- ("-inf1", True),
+- ("nan0", True),
+- ("-nan0", True),
+- ("naninf", True),
+- ("inf", False),
+- ("-inf", False),
+- ("infinity", False),
+- ("-infinity", False),
+- ("nan", False),
+- ("-NaN", False),
+- ]
+- )
+- def test_parse_various_float_values(self, test_literal, expect_exception):
+- model_text = f"""
+- <
+- ir_version: 8,
+- opset_import: ["" : 18, "this" : 1],
+- producer_name: "FunctionProtoTest",
+- producer_version: "1.0"
+- >
+- _func () => ()
+- {{
+- tmp = Constant <value_float = {test_literal}>()
+- }}
+- """
+- if expect_exception:
+- self.assertRaises(
+- onnx.parser.ParseError, lambda: onnx.parser.parse_model(model_text)
+- )
+- else:
+- model = onnx.parser.parse_model(model_text)
+- self.assertEqual(model.ir_version, 8)
+- self.assertEqual(model.producer_name, "FunctionProtoTest")
+- self.assertEqual(model.producer_version, "1.0")
+- self.assertEqual(len(model.graph.node), 1)
+- self.assertEqual(len(model.graph.node[0].attribute), 1)
+- self.assertEqual(model.graph.node[0].attribute[0].name, "value_float")
+- self.assertEqual(
+- model.graph.node[0].attribute[0].type, onnx.AttributeProto.FLOAT
+- )
+- self.assertEqual(
+- str(model.graph.node[0].attribute[0].f), str(float(test_literal))
+- )
+-
+- @parameterized.expand(
+- [
+- ("bfloat16", TensorProto.BFLOAT16),
+- ("bool", TensorProto.BOOL),
+- ("complex64", TensorProto.COMPLEX64),
+- ("complex128", TensorProto.COMPLEX128),
+- ("double", TensorProto.DOUBLE),
+- ("float16", TensorProto.FLOAT16),
+- ("float", TensorProto.FLOAT),
+- ("float8e4m3fn", TensorProto.FLOAT8E4M3FN),
+- ("float8e4m3fnuz", TensorProto.FLOAT8E4M3FNUZ),
+- ("float8e5m2", TensorProto.FLOAT8E5M2),
+- ("float8e5m2fnuz", TensorProto.FLOAT8E5M2FNUZ),
+- ("int2", TensorProto.INT2),
+- ("int4", TensorProto.INT4),
+- ("int8", TensorProto.INT8),
+- ("int16", TensorProto.INT16),
+- ("int32", TensorProto.INT32),
+- ("int64", TensorProto.INT64),
+- ("string", TensorProto.STRING),
+- ("uint2", TensorProto.UINT2),
+- ("uint4", TensorProto.UINT4),
+- ("uint8", TensorProto.UINT8),
+- ("uint16", TensorProto.UINT16),
+- ("uint32", TensorProto.UINT32),
+- ("uint64", TensorProto.UINT64),
+- ("float4e2m1", TensorProto.FLOAT4E2M1),
+- ]
+- )
+- def test_parse_graph_types(self, name, itype) -> None:
+- w = '{"0"}' if itype == TensorProto.STRING else "{0}"
+- text_graph = f"""
+- <
+- ir_version: 10,
+- opset_import: [ "" : 19]
+- >
+- agraph (float[N] X) => ({name}[N] C)
+- <
+- {name}[1] weight = {w}
+- >
+- {{
+- C = Cast<to={itype}>(X)
+- }}
+- """
+- graph = onnx.parser.parse_model(text_graph)
+- self.assertEqual(len(graph.graph.node), 1)
+-
+
+ if __name__ == "__main__":
+ unittest.main(verbosity=2)
+diff --git a/onnx/test/reference_evaluator_ml_test.py b/onnx/test/reference_evaluator_ml_test.py
+index 7dad96703..1c4276482 100644
+--- a/onnx/test/reference_evaluator_ml_test.py
++++ b/onnx/test/reference_evaluator_ml_test.py
+@@ -11,7 +11,6 @@ import unittest
+
+ import numpy as np
+ from numpy.testing import assert_allclose
+-from parameterized import parameterized
+
+ import onnx
+ from onnx import ONNX_ML, TensorProto, TypeProto, ValueInfoProto
+@@ -893,118 +892,6 @@ class TestReferenceEvaluatorAiOnnxMl(unittest.TestCase):
+ onnx.checker.check_model(model)
+ return model
+
+- @parameterized.expand(
+- tuple(
+- itertools.chain.from_iterable(
+- (
+- (
+- AggregationFunction.SUM if opset5 else "SUM",
+- np.array(
+- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- (
+- AggregationFunction.AVERAGE if opset5 else "AVERAGE",
+- np.array(
+- [[0.288462], [0.288462], [0.288462]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- (
+- AggregationFunction.MIN if opset5 else "MIN",
+- np.array(
+- [[0.076923], [0.076923], [0.076923]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- (
+- AggregationFunction.MAX if opset5 else "MAX",
+- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
+- opset5,
+- ),
+- )
+- for opset5 in [True, False]
+- )
+- )
+- )
+- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
+- def test_tree_ensemble_regressor_aggregation_functions(
+- self, aggregate_function, expected_result, opset5
+- ):
+- x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
+- model_factory = (
+- self._get_test_tree_ensemble_opset_latest
+- if opset5
+- else self._get_test_tree_ensemble_regressor
+- )
+- model_proto = model_factory(
+- aggregate_function,
+- )
+- sess = ReferenceEvaluator(model_proto)
+- (actual,) = sess.run(None, {"X": x})
+- assert_allclose(actual, expected_result, atol=1e-6)
+-
+- @parameterized.expand(
+- tuple(
+- itertools.chain.from_iterable(
+- (
+- (
+- Mode.LEQ if opset5 else "BRANCH_LEQ",
+- np.array(
+- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- (
+- Mode.GT if opset5 else "BRANCH_GT",
+- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
+- opset5,
+- ),
+- (
+- Mode.LT if opset5 else "BRANCH_LT",
+- np.array(
+- [[0.576923], [0.576923], [0.576923]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- (
+- Mode.GTE if opset5 else "BRANCH_GTE",
+- np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
+- opset5,
+- ),
+- (
+- Mode.EQ if opset5 else "BRANCH_EQ",
+- np.array([[1.0], [1.0], [1.0]], dtype=np.float32),
+- opset5,
+- ),
+- (
+- Mode.NEQ if opset5 else "BRANCH_NEQ",
+- np.array(
+- [[0.076923], [0.076923], [0.076923]], dtype=np.float32
+- ),
+- opset5,
+- ),
+- )
+- for opset5 in [True, False]
+- )
+- )
+- )
+- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
+- def test_tree_ensemble_regressor_rule(self, rule, expected, opset5):
+- x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
+- model_factory = (
+- self._get_test_tree_ensemble_opset_latest
+- if opset5
+- else self._get_test_tree_ensemble_regressor
+- )
+- aggregate_function = AggregationFunction.SUM if opset5 else "SUM"
+-
+- model_proto = model_factory(aggregate_function, rule)
+- sess = ReferenceEvaluator(model_proto)
+- (actual,) = sess.run(None, {"X": x})
+- assert_allclose(actual, expected, atol=1e-6)
+-
+ @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
+ def test_tree_ensemble_regressor_2_targets_opset3(self):
+ X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
+@@ -1118,23 +1005,6 @@ class TestReferenceEvaluatorAiOnnxMl(unittest.TestCase):
+ assert_allclose(got[0], expected, atol=1e-6)
+ self.assertIn("op_type=TreeEnsembleRegressor", str(sess.rt_nodes_[0]))
+
+- @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
+- @parameterized.expand(
+- [(input_type,) for input_type in [TensorProto.FLOAT, TensorProto.DOUBLE]]
+- )
+- def test_tree_ensemble_missing_opset5(self, input_type):
+- model = self._get_test_tree_ensemble_opset_latest(
+- AggregationFunction.SUM, Mode.LEQ, True, input_type
+- )
+- np_dtype = onnx.helper.tensor_dtype_to_np_dtype(input_type)
+- x = np.arange(9).reshape((-1, 3)).astype(np_dtype) / 10 - 0.5
+- x[2, 0] = 5
+- x[1, :] = np.nan
+- expected = np.array([[100001.0], [100100.0], [100100.0]], dtype=np_dtype)
+- session = ReferenceEvaluator(model)
+- (actual,) = session.run(None, {"X": x})
+- assert_allclose(actual, expected, atol=1e-6)
+-
+ @unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
+ def test_tree_ensemble_regressor_missing_opset5_float16(self):
+ model = self._get_test_tree_ensemble_opset_latest(
+diff --git a/onnx/test/reference_evaluator_test.py b/onnx/test/reference_evaluator_test.py
+index 85cc4cc0e..d0a255fc8 100644
+--- a/onnx/test/reference_evaluator_test.py
++++ b/onnx/test/reference_evaluator_test.py
+@@ -25,7 +25,6 @@ from typing import TYPE_CHECKING
+
+ import ml_dtypes
+ import numpy as np
+-import parameterized
+ import version_utils
+ from numpy.testing import assert_allclose
+
+@@ -2998,163 +2997,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ # model
+ return make_model(graph, opset_imports=[make_opsetid("", opset)])
+
+- @parameterized.parameterized.expand(
+- itertools.product(
+- [
+- (
+- "ReduceMin",
+- [
+- np.array(
+- [[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
+- ),
+- np.array([[2, 15], [10, 4]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceL1",
+- [
+- np.array(
+- [[2.2367053, 2.3516612], [4.076292, 4.2970634]],
+- dtype=np.float32,
+- ),
+- np.array([[18, 6], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceL2",
+- [
+- np.array(
+- [[1.80155, 1.8169948], [2.9928076, 3.1205883]],
+- dtype=np.float32,
+- ),
+- np.array([[11, 18], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceLogSum",
+- [
+- np.array(
+- [[0.9497848, 1.1872643], [1.6764175, 1.70759]],
+- dtype=np.float32,
+- ),
+- np.array([[6, 18], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceLogSumExp",
+- [
+- np.array(
+- [[1.6005973, 1.7445935], [2.5616229, 2.6539795]],
+- dtype=np.float32,
+- ),
+- np.array([[13, 6], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceMax",
+- [
+- np.array(
+- [[1.4217108, 1.5069536], [2.453826, 2.5041783]],
+- dtype=np.float32,
+- ),
+- np.array([[13, 11], [13, 11]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceMean",
+- [
+- np.array(
+- [[0.39247903, 0.78497636], [2.038146, 2.1485317]],
+- dtype=np.float32,
+- ),
+- np.array([[13, 6], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceSumSquare",
+- [
+- np.array(
+- [[3.2455828, 3.3014696], [8.956896, 9.7380705]],
+- dtype=np.float32,
+- ),
+- np.array([[11, 18], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- (
+- "ReduceProd",
+- [
+- np.array(
+- [[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
+- ),
+- np.array([[2, 15], [13, 6]], dtype=np.int64),
+- ],
+- ),
+- ],
+- [17, 18],
+- )
+- )
+- def test_op_reduce(self, reduce_op_expected, opset: int):
+- reduce_op, expected = reduce_op_expected
+- X = np.arange(8).reshape((-1, 4)).astype(np.float32)
+-
+- results = {}
+-
+- model = self._cdist_model(opset, reduce_op)
+- sess = ReferenceEvaluator(model)
+- got = sess.run(None, {"input": X})
+- results["ref", opset] = got
+-
+- cl = [
+- n
+- for n in sess.rt_nodes_[0].body.rt_nodes_
+- if n.__class__.__name__.startswith(reduce_op)
+- ]
+- schema = cl[0]._schema
+- new_cl = type(reduce_op, (cl[0].__class__,), {"op_schema": schema})
+- sess = ReferenceEvaluator(model, new_ops=[new_cl])
+- got = sess.run(None, {"input": X})
+- results["ref_cl", opset] = got
+-
+- baseline = "constant"
+- for k, v in results.items():
+- for a, b in zip(reversed(expected), reversed(v), strict=True):
+- if a.shape != b.shape:
+- raise AssertionError(
+- f"Shape mismatch for {reduce_op!r}, {baseline}:{a.shape} != {k}:{b.shape}."
+- )
+- diff = np.abs(a - b).max()
+- if diff > 1e-6:
+- raise AssertionError(
+- f"Discrepancies (max={diff}) for {reduce_op!r}, {baseline} != {k}\n{a}\n!=\n{b}"
+- )
+-
+- @parameterized.parameterized.expand(
+- [
+- (13,),
+- (17,),
+- (18,),
+- ]
+- )
+- def test_mvn(self, opset: int, ref_opset: int = 13):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
+- nodes = [
+- make_node("MeanVarianceNormalization", ["X"], ["Y"]),
+- ]
+- graph = make_graph(nodes, "g", [X], [Y])
+- x = np.random.rand(3, 3, 3, 1).astype(np.float32)
+-
+- onnx_model = make_model(graph, opset_imports=[make_opsetid("", opset)])
+- ref = ReferenceEvaluator(onnx_model)
+- got = ref.run(None, {"X": x})[0]
+-
+- ref_onnx_model = make_model(graph, opset_imports=[make_opsetid("", ref_opset)])
+- ref_expected = ReferenceEvaluator(ref_onnx_model)
+- expected = ref_expected.run(None, {"X": x})[0]
+-
+- self.assertEqual(got.shape, expected.shape)
+- assert_allclose(got, expected)
+-
+ def test_concat_in_a_function(self):
+ def create_model():
+ nodes = []
+@@ -3829,211 +3671,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ got = ref.run(None, {"X": data})
+ assert_allclose(got[0], expected)
+
+- @parameterized.parameterized.expand(
+- [
+- (
+- 4 * np.arange(12).reshape(3, 4),
+- np.arange(1, 7).reshape(3, 2),
+- np.zeros((3, 2)),
+- 1,
+- 2,
+- [[0, 4, 4, 6], [5, 7, 6, 7], [6, 7, 7, 7]],
+- ),
+- (
+- 4 * np.arange(12).reshape(3, 4),
+- np.arange(1, 7).reshape(3, 2),
+- np.ones((3, 2)),
+- 1,
+- 2,
+- [[1, 5, 5, 7], [6, 8, 7, 8], [7, 8, 8, 8]],
+- ),
+- (
+- np.arange(24).reshape(3, 8),
+- [[0.25, 0.5, 1], [0.25, 0.5, 1], [0.25, 0.5, 1]],
+- np.zeros((3, 3)),
+- 1,
+- 3,
+- [
+- [0, 4, 8, 6, 8, 10, 6, 7],
+- [32, 36, 40, 22, 24, 26, 14, 15],
+- [64, 68, 72, 38, 40, 42, 22, 23],
+- ],
+- ),
+- (
+- np.arange(6),
+- [0.25, 0.5],
+- [-1, -2],
+- 0,
+- 3,
+- [-1, 3, 7, 4, 6, 8],
+- ),
+- (
+- np.ones((9, 12)),
+- np.ones((3, 4)),
+- np.zeros((3, 4)),
+- 0,
+- 3,
+- None, # Blocked quantization is defined for 1-D blocks only
+- ),
+- (
+- np.ones((3, 4, 5, 6)),
+- np.ones((3, 4)),
+- np.zeros((3, 4)),
+- 2,
+- 2,
+- None, # Scale and ZP must have the same rank as the input
+- ),
+- ]
+- )
+- def test_blocked_quantize_linear(
+- self, x, scale, zero_point, axis, block_size, expected
+- ):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
+- Y = make_tensor_value_info("Y", TensorProto.INT8, [None])
+-
+- scale_data = np.array(scale, dtype=np.float32)
+- zp_data = np.array(zero_point, dtype=np.int8)
+- model = make_model(
+- make_graph(
+- [
+- make_node(
+- "QuantizeLinear",
+- ["X", "scale", "zero"],
+- ["Y"],
+- axis=axis,
+- block_size=block_size,
+- ),
+- ],
+- "g",
+- [X],
+- [Y],
+- [
+- make_tensor(
+- "scale", TensorProto.FLOAT, scale_data.shape, scale_data
+- ),
+- make_tensor("zero", TensorProto.INT8, scale_data.shape, zp_data),
+- ],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+-
+- data = np.array(x, dtype=np.float32)
+-
+- if expected is not None:
+- expected = np.array(expected, dtype=np.int8)
+- got = ref.run(None, {"X": data})
+- assert_allclose(got[0], expected)
+- else:
+- with self.assertRaises(ValueError):
+- ref.run(None, {"X": data})
+-
+- @parameterized.parameterized.expand(
+- [
+- (
+- np.arange(12).reshape(3, 4),
+- np.arange(1, 7).reshape(3, 2),
+- np.zeros((3, 2)),
+- 1,
+- 2,
+- [[0, 1, 4, 6], [12, 15, 24, 28], [40, 45, 60, 66]],
+- ),
+- (
+- np.arange(12).reshape(3, 4),
+- np.arange(1, 7).reshape(3, 2),
+- np.ones((3, 2)),
+- 1,
+- 2,
+- [[-1, 0, 2, 4], [9, 12, 20, 24], [35, 40, 54, 60]],
+- ),
+- (
+- np.dstack([np.arange(4).reshape(2, 2)] * 4),
+- np.dstack([np.array([[1, 1], [2, 3]]), np.array([[4, 5], [6, 7]])]),
+- np.zeros((2, 2, 2)),
+- 2,
+- 2,
+- [[[0, 0, 0, 0], [1, 1, 5, 5]], [[4, 4, 12, 12], [9, 9, 21, 21]]],
+- ),
+- (
+- np.arange(24).reshape(3, 8),
+- [[2, 1, 3], [2, 1, 3], [2, 1, 3]],
+- np.zeros((3, 3)),
+- 1,
+- 3,
+- [
+- [0, 2, 4, 3, 4, 5, 18, 21],
+- [16, 18, 20, 11, 12, 13, 42, 45],
+- [32, 34, 36, 19, 20, 21, 66, 69],
+- ],
+- ),
+- (
+- np.arange(
+- 6,
+- ),
+- [2, 3],
+- [1, 2],
+- 0,
+- 3,
+- [-2, 0, 2, 3, 6, 9],
+- ),
+- (
+- np.ones((9, 12)),
+- np.ones((3, 4)),
+- np.zeros((3, 4)),
+- 0,
+- 3,
+- None, # Blocked quantization is defined for 1-D blocks only
+- ),
+- (
+- np.ones((3, 4, 5, 6)),
+- np.ones((3, 4)),
+- np.zeros((3, 4)),
+- 2,
+- 2,
+- None, # Scale and ZP must have the same rank as the input
+- ),
+- ]
+- )
+- def test_blocked_dequantize_linear(
+- self, x, scale, zero_point, axis, block_size, expected
+- ):
+- X = make_tensor_value_info("X", TensorProto.INT8, [None])
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
+-
+- scale_data = np.array(scale, dtype=np.float32)
+- zp_data = np.array(zero_point, dtype=np.int8)
+- model = make_model(
+- make_graph(
+- [
+- make_node(
+- "DequantizeLinear",
+- ["X", "scale", "zero"],
+- ["Y"],
+- axis=axis,
+- block_size=block_size,
+- ),
+- ],
+- "g",
+- [X],
+- [Y],
+- [
+- make_tensor(
+- "scale", TensorProto.FLOAT, scale_data.shape, scale_data
+- ),
+- make_tensor("zero", TensorProto.INT8, scale_data.shape, zp_data),
+- ],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- data = np.array(x, dtype=np.int8)
+-
+- if expected is not None:
+- expected = np.array(expected, dtype=np.float32)
+- got = ref.run(None, {"X": data})
+- assert_allclose(got[0], expected)
+- else:
+- with self.assertRaises(ValueError):
+- ref.run(None, {"X": data})
+-
+ def test_lrn(self):
+ def _expected(x, alpha, beta, bias, size):
+ square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
+@@ -4217,52 +3854,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ got = _conv_implementation_im2col(**feeds, **kwargs)
+ assert_allclose(got, expected)
+
+- @parameterized.parameterized.expand(
+- [
+- ("ReduceSum",),
+- ("ReduceL1",),
+- ("ReduceL2",),
+- ("ReduceMin",),
+- ("ReduceMax",),
+- ("ReduceProd",),
+- ("ReduceSumSquare",),
+- ]
+- )
+- def test_reduce_op_no_axis(self, op):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, None)
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
+- data = np.arange(6).reshape((1, 3, 2)).astype(np.float32)
+- nodes = [make_node(op, ["X"], ["Y"], keepdims=0)]
+- model = make_model(make_graph(nodes, "g", [X], [Y]))
+- ref = ReferenceEvaluator(model)
+- got = ref.run(None, {"X": data})
+- r = got[0]
+- self.assertIsInstance(r, np.ndarray)
+- self.assertEqual(r.shape, ())
+-
+- @parameterized.parameterized.expand([(1,), (2,), (3,), (4,), (5,), (6,)])
+- def test_pad(self, dim):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, None)
+- P = make_tensor_value_info("P", TensorProto.INT64, None)
+- V = make_tensor_value_info("V", TensorProto.FLOAT, None)
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
+- value = np.array([-5], dtype=np.float32)
+-
+- node = make_node("Pad", inputs=["X", "P", "V"], outputs=["Y"], mode="constant")
+- model = make_model(make_graph([node], "g", [X, P, V], [Y]))
+- ref = ReferenceEvaluator(model)
+- x = np.array([1], dtype=np.float32).reshape((1,) * dim)
+-
+- p = np.array([1, 1] * dim, dtype=np.int64)
+- got = ref.run(None, {"X": x, "P": p, "V": value})[0]
+- self.assertEqual(got.shape, (3,) * dim)
+- self.assertEqual(got.dtype, np.float32)
+-
+- p = np.repeat([7, 3], dim).astype(np.int64)
+- got = ref.run(None, {"X": x, "P": p, "V": value})[0]
+- self.assertEqual(got.shape, (11,) * dim)
+- self.assertEqual(got.dtype, np.float32)
+-
+ def test_constant_of_shape(self):
+ X = make_tensor_value_info("X", TensorProto.FLOAT, None)
+ Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
+@@ -5373,124 +4964,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ for i in range(2, -1, -1):
+ assert_allclose(got[i], expected[i])
+
+- @parameterized.parameterized.expand(
+- [
+- (["abc", "def"], [".com", ".net"], ["abc.com", "def.net"], (2,)),
+- (["cat", "dog", "snake"], ["s"], ["cats", "dogs", "snakes"], (3,)),
+- ("cat", "s", "cats", ()),
+- (["a", "ß", "y"], ["a", "ß", "y"], ["aa", "ßß", "yy"], (3,)),
+- ]
+- )
+- def test_string_concat(self, a, b, expected, expected_shape):
+- A = make_tensor_value_info("A", TensorProto.STRING, None)
+- B = make_tensor_value_info("B", TensorProto.STRING, None)
+- Y = make_tensor_value_info("Y", TensorProto.STRING, None)
+- node = make_node("StringConcat", inputs=["A", "B"], outputs=["Y"])
+- model = make_model(make_graph([node], "g", [A, B], [Y]))
+- ref = ReferenceEvaluator(model)
+- result, *_ = ref.run(None, {"A": np.array(a), "B": np.array(b)})
+- np.testing.assert_array_equal(result, expected)
+- self.assertIn(result.dtype.kind, {"O", "U"})
+- self.assertEqual(result.shape, expected_shape)
+-
+- @parameterized.parameterized.expand(
+- [
+- (
+- ["1,2,3", "4,5,6"],
+- ",",
+- None,
+- [["1", "2", "3"], ["4", "5", "6"]],
+- [3, 3],
+- ),
+- (
+- ["1,", "4,6", ""],
+- ",",
+- None,
+- [["1", ""], ["4", "6"], ["", ""]],
+- [2, 2, 1],
+- ),
+- (
+- ["1", "4,6", "4,5,6"],
+- ",",
+- 1,
+- [["1", ""], ["4", "6"], ["4", "5,6"]],
+- [1, 2, 2],
+- ),
+- (
+- [["1,", "4,6", "4,5,6"], ["1,", "4,6", "4,5,6"]],
+- ",",
+- None,
+- [
+- [["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
+- [["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
+- ],
+- [[2, 2, 3], [2, 2, 3]],
+- ),
+- (
+- ["hello world !", " hello world !", " hello world ! "],
+- None,
+- None,
+- [
+- ["hello", "world", "!"],
+- ["hello", "world", "!"],
+- ["hello", "world", "!"],
+- ],
+- [3, 3, 3],
+- ),
+- (
+- ["hello world !", " hello world !", " hello world ! "],
+- "",
+- None,
+- [
+- ["hello", "world", "!"],
+- ["hello", "world", "!"],
+- ["hello", "world", "!"],
+- ],
+- [3, 3, 3],
+- ),
+- (
+- ["o-n-n--x-", "o-n----nx"],
+- "-",
+- None,
+- [["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]],
+- [6, 6],
+- ),
+- (
+- [],
+- " ",
+- 2,
+- np.array([]).reshape((0, 0)),
+- [],
+- ),
+- ]
+- )
+- def test_string_split(
+- self,
+- x,
+- delimiter,
+- maxsplit,
+- expected_split,
+- expected_num_splits,
+- ):
+- X = make_tensor_value_info("X", TensorProto.STRING, (None))
+- Splits = make_tensor_value_info("Splits", TensorProto.STRING, (None))
+- MaxSplits = make_tensor_value_info("MaxSplits", TensorProto.INT32, (None))
+- node = make_node(
+- "StringSplit",
+- inputs=["X"],
+- outputs=["Splits", "MaxSplits"],
+- delimiter=delimiter,
+- maxsplit=maxsplit,
+- )
+- model = make_model(make_graph([node], "g", [X], [Splits, MaxSplits]))
+- ref = ReferenceEvaluator(model)
+- x = np.array(x, dtype=object)
+- result, num_splits, *_ = ref.run(None, {"X": x})
+- np.testing.assert_array_equal(result, np.array(expected_split, dtype=object))
+- np.testing.assert_array_equal(
+- num_splits, np.array(expected_num_splits, dtype=np.int64)
+- )
+-
+ def test_qlinearconv_int8(self):
+ node = make_node(
+ "QLinearMatMul",
+@@ -5561,44 +5034,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ np.array([[41, -12, -9], [1, -75, 20]], dtype=np.int8), got[0]
+ )
+
+- @parameterized.parameterized.expand(
+- [
+- (
+- ["www.google.com", "www.facebook.com", "www.bbc.co.uk"],
+- r"www\.[\w.-]+\.\bcom\b",
+- [True, True, False],
+- (3,),
+- ),
+- (
+- [["Onnx", "tensorflow", "Numpy"], ["Pytorch", "Cython", "numba"]],
+- r"^[A-Z][a-z]*$",
+- [[True, False, True], [True, True, False]],
+- (2, 3),
+- ),
+- (
+- [
+- "account@gmail.com",
+- "account@hotmail.com",
+- "not email",
+- "account2@yahoo.com",
+- ],
+- r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
+- [True, False, False, True],
+- (4,),
+- ),
+- ]
+- )
+- def test_regex_full_match(self, x, pattern, expected, expected_shape):
+- X = make_tensor_value_info("X", TensorProto.STRING, None)
+- Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
+- node = make_node("RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=pattern)
+- model = make_model(make_graph([node], "g", [X], [Y]))
+- ref = ReferenceEvaluator(model)
+- result, *_ = ref.run(None, {"X": np.array(x)})
+- np.testing.assert_array_equal(result, expected)
+- self.assertEqual(result.dtype.kind, "b")
+- self.assertEqual(result.shape, expected_shape)
+-
+ def test_regex_invalid_pattern(self):
+ X = make_tensor_value_info("X", TensorProto.STRING, None)
+ Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
+@@ -5608,220 +5043,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ with self.assertRaises(ValueError):
+ ref.run(None, {"X": np.array(["x"])})
+
+- @parameterized.parameterized.expand(
+- [
+- (
+- TensorProto.UINT2,
+- [-1, 0, 1.5, 2, 3.3, 10, 20, 40],
+- [0, 0, 2, 2, 4, 6, 6, 6],
+- ),
+- (TensorProto.UINT2, [-1, 0, 1.5, 2, 3.3, 10, 40], [0, 0, 2, 2, 4, 6, 6]),
+- (TensorProto.UINT2, [0], [0]),
+- (
+- TensorProto.INT2,
+- [-20, -14.5, 0, 1.5, 2, 3.3, 10, 20],
+- [-4, -4, 0, 2, 2, 2, 2, 2],
+- ),
+- (
+- TensorProto.INT2,
+- [-20, -14.5, 0, 1.5, 2, 3.3, 10],
+- [-4, -4, 0, 2, 2, 2, 2],
+- ),
+- (TensorProto.INT2, [0], [0]),
+- ]
+- )
+- def test_quantize_linear_int2(self, qtype, data, expected):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node(
+- "Constant",
+- [],
+- ["scale"],
+- value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
+- ),
+- make_node(
+- "Constant",
+- [],
+- ["zero"],
+- value=make_tensor("zero", qtype, [1], [0]),
+- ),
+- make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
+- make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- got = ref.run(None, {"X": np.asarray(data)})
+- assert_allclose(got[0], expected)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.FLOAT, TensorProto.FLOAT16),
+- (TensorProto.UINT2, TensorProto.INT2),
+- )
+- )
+- def test_cast_int2_output(self, cast_from, cast_to):
+- X = make_tensor_value_info("X", cast_from, [None])
+- Y = make_tensor_value_info("Y", cast_to, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Y"], to=cast_to),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- data = np.array(
+- [0, 1, 2.4, 2.6, 4, 10],
+- dtype=onnx.helper.tensor_dtype_to_np_dtype(cast_from),
+- )
+- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_to))
+- got = ref.run(None, {"X": data})
+- self.assertEqual(got[0].tolist(), expected.tolist())
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT2, TensorProto.INT2),
+- (TensorProto.FLOAT, TensorProto.FLOAT16),
+- )
+- )
+- def test_cast_int2_input(
+- self, cast_from: TensorProto.DataType, cast_to: TensorProto.DataType
+- ):
+- X = make_tensor_value_info("X", cast_from, [None])
+- Y = make_tensor_value_info("Y", cast_to, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Y"], to=TensorProto.FLOAT),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- data = np.array(range(2), dtype=np.float32)
+- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_from))
+- got = ref.run(None, {"X": data})
+- np.testing.assert_array_equal(got[0], expected)
+-
+- @parameterized.parameterized.expand(
+- [
+- (
+- TensorProto.UINT4,
+- [-1, 0, 1.5, 2, 3.3, 10, 20, 40],
+- [0, 0, 2, 2, 4, 10, 20, 30],
+- ),
+- (TensorProto.UINT4, [-1, 0, 1.5, 2, 3.3, 10, 40], [0, 0, 2, 2, 4, 10, 30]),
+- (TensorProto.UINT4, [0], [0]),
+- (
+- TensorProto.INT4,
+- [-20, -14.5, 0, 1.5, 2, 3.3, 10, 20],
+- [-16, -14, 0, 2, 2, 4, 10, 14],
+- ),
+- (
+- TensorProto.INT4,
+- [-20, -14.5, 0, 1.5, 2, 3.3, 10],
+- [-16, -14, 0, 2, 2, 4, 10],
+- ),
+- (TensorProto.INT4, [0], [0]),
+- ]
+- )
+- def test_quantize_linear_int4(self, qtype, data, expected):
+- X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
+- Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node(
+- "Constant",
+- [],
+- ["scale"],
+- value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
+- ),
+- make_node(
+- "Constant",
+- [],
+- ["zero"],
+- value=make_tensor("zero", qtype, [1], [0]),
+- ),
+- make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
+- make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- got = ref.run(None, {"X": np.asarray(data)})
+- assert_allclose(got[0], expected)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.FLOAT, TensorProto.FLOAT16),
+- (TensorProto.UINT4, TensorProto.INT4),
+- )
+- )
+- def test_cast_int4_output(self, cast_from, cast_to):
+- X = make_tensor_value_info("X", cast_from, [None])
+- Y = make_tensor_value_info("Y", cast_to, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Y"], to=cast_to),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- data = np.array(
+- [0, 1, 2.4, 2.6, 4, 10],
+- dtype=onnx.helper.tensor_dtype_to_np_dtype(cast_from),
+- )
+- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_to))
+- got = ref.run(None, {"X": data})
+- self.assertEqual(got[0].tolist(), expected.tolist())
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (TensorProto.UINT4, TensorProto.INT4),
+- (TensorProto.FLOAT, TensorProto.FLOAT16),
+- )
+- )
+- def test_cast_int4_input(
+- self, cast_from: TensorProto.DataType, cast_to: TensorProto.DataType
+- ):
+- X = make_tensor_value_info("X", cast_from, [None])
+- Y = make_tensor_value_info("Y", cast_to, [None])
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Y"], to=TensorProto.FLOAT),
+- ],
+- "g",
+- [X],
+- [Y],
+- )
+- )
+- ref = ReferenceEvaluator(model)
+- data = np.array(range(7), dtype=np.float32)
+- expected = data.astype(onnx.helper.tensor_dtype_to_np_dtype(cast_from))
+- got = ref.run(None, {"X": data})
+- np.testing.assert_array_equal(got[0], expected)
+-
+ def test_a_function_calling_a_function_once(self):
+ X = make_tensor_value_info("X", TensorProto.FLOAT, ["N"])
+ output = make_tensor_value_info("output", TensorProto.FLOAT, ["N"])
+@@ -6054,94 +5275,6 @@ class TestReferenceEvaluator(unittest.TestCase):
+ for v in oinf.functions_.values():
+ self.assertIsInstance(v, MyReferenceEvaluator)
+
+- @parameterized.parameterized.expand(
+- [
+- ("FLOAT8E4M3FN", 0.23),
+- ("FLOAT8E4M3FNUZ", 0.23),
+- ("FLOAT8E5M2", 0.85),
+- ("FLOAT8E5M2FNUZ", 0.85),
+- ("DOUBLE", 0),
+- ("FLOAT", 0),
+- ("FLOAT16", 2e-3),
+- ("BFLOAT16", 2e-2),
+- ]
+- )
+- def test_add_custom_dtype(self, stype: str, atol: float):
+- itype = getattr(TensorProto, stype)
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Xc"], to=itype),
+- make_node("Cast", ["Y"], ["Yc"], to=itype),
+- make_node("Add", ["Xc", "Yc"], ["Zc"]),
+- make_node("Cast", ["Zc"], ["Z"], to=TensorProto.FLOAT),
+- ],
+- "nd",
+- [
+- make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None]),
+- make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None]),
+- ],
+- [make_tensor_value_info("Z", TensorProto.FLOAT, [None, None, None])],
+- ),
+- opset_imports=[make_opsetid("", 18)],
+- ir_version=9,
+- )
+-
+- ref = ReferenceEvaluator(model, verbose=0)
+-
+- x = (np.arange(18) / 6).reshape((2, 3, 3)).astype(np.float32)
+- y = (np.arange(18) / 9).reshape((2, 3, 3)).astype(np.float32)
+- feeds = dict(X=x, Y=y)
+- expected = x + y
+- got = ref.run(None, feeds)[0]
+- assert_allclose(got, expected, atol=atol)
+-
+- @parameterized.parameterized.expand(
+- [
+- ("DOUBLE",),
+- ("FLOAT",),
+- ("FLOAT16",),
+- ("BFLOAT16",),
+- ("FLOAT8E4M3FN",),
+- ("FLOAT8E4M3FNUZ",),
+- ("FLOAT8E5M2",),
+- ("FLOAT8E5M2FNUZ",),
+- ("INT4",),
+- ("UINT4",),
+- ("INT2",),
+- ("UINT2",),
+- ]
+- )
+- def test_cmp_custom_dtype(self, stype):
+- itype = getattr(TensorProto, stype)
+- model = make_model(
+- make_graph(
+- [
+- make_node("Cast", ["X"], ["Xc"], to=itype),
+- make_node("Cast", ["Y"], ["Yc"], to=itype),
+- make_node("GreaterOrEqual", ["Xc", "Yc"], ["Zc"]),
+- make_node("Cast", ["Zc"], ["Z"], to=TensorProto.BOOL),
+- ],
+- "nd",
+- [
+- make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None]),
+- make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None]),
+- ],
+- [make_tensor_value_info("Z", TensorProto.FLOAT, [None, None, None])],
+- ),
+- opset_imports=[make_opsetid("", 18)],
+- ir_version=9,
+- )
+-
+- ref = ReferenceEvaluator(model)
+-
+- x = (np.arange(18) / 18).reshape((2, 3, 3)).astype(np.float32)
+- y = ((np.arange(18) - 9) / 18).reshape((2, 3, 3)).astype(np.float32)
+- feeds = dict(X=x, Y=y)
+- expected = x >= y
+- got = ref.run(None, feeds)[0]
+- np.testing.assert_equal(got, expected)
+-
+ def test_scatter_elements_4d(self):
+ model = make_model(
+ make_graph(
+diff --git a/onnx/test/schema_test.py b/onnx/test/schema_test.py
+index f8b3f8a65..710b602a3 100644
+--- a/onnx/test/schema_test.py
++++ b/onnx/test/schema_test.py
+@@ -7,8 +7,6 @@ import contextlib
+ import unittest
+ from typing import TYPE_CHECKING
+
+-import parameterized
+-
+ import onnx
+ from onnx import defs
+
+@@ -247,34 +245,6 @@ class TestFormalParameter(unittest.TestCase):
+ )
+
+
+-class TestTypeConstraintParam(unittest.TestCase):
+- @parameterized.parameterized.expand(
+- [
+- ("single_type", "T", ["tensor(float)"], "Test description"),
+- (
+- "double_types",
+- "T",
+- ["tensor(float)", "tensor(int64)"],
+- "Test description",
+- ),
+- ("tuple", "T", ("tensor(float)", "tensor(int64)"), "Test description"),
+- ]
+- )
+- def test_init(
+- self,
+- _: str,
+- type_param_str: str,
+- allowed_types: Sequence[str],
+- description: str,
+- ) -> None:
+- type_constraint = defs.OpSchema.TypeConstraintParam(
+- type_param_str, allowed_types, description
+- )
+- self.assertEqual(type_constraint.description, description)
+- self.assertEqual(type_constraint.allowed_type_strs, list(allowed_types))
+- self.assertEqual(type_constraint.type_param_str, type_param_str)
+-
+-
+ class TestAttribute(unittest.TestCase):
+ def test_init(self):
+ name = "test_attr"
+@@ -297,169 +267,5 @@ class TestAttribute(unittest.TestCase):
+ self.assertEqual("attr1 description", attribute.description)
+
+
+-@parameterized.parameterized_class(
+- [
+- # register to exist domain
+- {
+- "op_type": "CustomOp",
+- "op_version": 5,
+- "op_domain": "",
+- "trap_op_version": [1, 2, 6, 7],
+- },
+- # register to new domain
+- {
+- "op_type": "CustomOp",
+- "op_version": 5,
+- "op_domain": "test",
+- "trap_op_version": [1, 2, 6, 7],
+- },
+- ]
+-)
+-class TestOpSchemaRegister(unittest.TestCase):
+- op_type: str
+- op_version: int
+- op_domain: str
+- # register some fake schema to check behavior
+- trap_op_version: list[int]
+-
+- def setUp(self) -> None:
+- # Ensure the schema is unregistered
+- self.assertFalse(onnx.defs.has(self.op_type, self.op_domain))
+-
+- def tearDown(self) -> None:
+- # Clean up the registered schema
+- for version in [*self.trap_op_version, self.op_version]:
+- with contextlib.suppress(onnx.defs.SchemaError):
+- onnx.defs.deregister_schema(self.op_type, version, self.op_domain)
+-
+- def test_register_multi_schema(self):
+- for version in [*self.trap_op_version, self.op_version]:
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- version,
+- )
+- onnx.defs.register_schema(op_schema)
+- self.assertTrue(onnx.defs.has(self.op_type, version, self.op_domain))
+- for version in [*self.trap_op_version, self.op_version]:
+- # Also make sure the `op_schema` is accessible after register
+- registered_op = onnx.defs.get_schema(
+- op_schema.name, version, op_schema.domain
+- )
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- version,
+- )
+- self.assertEqual(str(registered_op), str(op_schema))
+-
+- def test_using_the_specified_version_in_onnx_check(self):
+- input = f"""
+- <
+- ir_version: 7,
+- opset_import: [
+- "{self.op_domain}" : {self.op_version}
+- ]
+- >
+- agraph (float[N, 128] X, int32 Y) => (float[N] Z)
+- {{
+- Z = {self.op_domain}.{self.op_type}<attr1=[1,2]>(X, Y)
+- }}
+- """
+- model = onnx.parser.parse_model(input)
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- self.op_version,
+- inputs=[
+- defs.OpSchema.FormalParameter("input1", "T"),
+- defs.OpSchema.FormalParameter("input2", "int32"),
+- ],
+- outputs=[
+- defs.OpSchema.FormalParameter("output1", "T"),
+- ],
+- type_constraints=[("T", ["tensor(float)"], "")],
+- attributes=[
+- defs.OpSchema.Attribute(
+- "attr1", defs.OpSchema.AttrType.INTS, "attr1 description"
+- )
+- ],
+- )
+- with self.assertRaises(onnx.checker.ValidationError):
+- onnx.checker.check_model(model, check_custom_domain=True)
+- onnx.defs.register_schema(op_schema)
+- # The fake schema will raise check exception if selected in checker
+- for version in self.trap_op_version:
+- onnx.defs.register_schema(
+- defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- version,
+- outputs=[
+- defs.OpSchema.FormalParameter("output1", "int32"),
+- ],
+- )
+- )
+- onnx.checker.check_model(model, check_custom_domain=True)
+-
+- def test_register_schema_raises_error_when_registering_a_schema_twice(self):
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- self.op_version,
+- )
+- onnx.defs.register_schema(op_schema)
+- with self.assertRaises(onnx.defs.SchemaError):
+- onnx.defs.register_schema(op_schema)
+-
+- def test_deregister_the_specified_schema(self):
+- for version in [*self.trap_op_version, self.op_version]:
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- version,
+- )
+- onnx.defs.register_schema(op_schema)
+- self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
+- onnx.defs.deregister_schema(op_schema.name, self.op_version, op_schema.domain)
+- for version in self.trap_op_version:
+- self.assertTrue(onnx.defs.has(op_schema.name, version, op_schema.domain))
+- # Maybe has lesser op version in trap list
+- if onnx.defs.has(op_schema.name, self.op_version, op_schema.domain):
+- schema = onnx.defs.get_schema(
+- op_schema.name, self.op_version, op_schema.domain
+- )
+- self.assertLess(schema.since_version, self.op_version)
+-
+- def test_deregister_schema_raises_error_when_opschema_does_not_exist(self):
+- with self.assertRaises(onnx.defs.SchemaError):
+- onnx.defs.deregister_schema(self.op_type, self.op_version, self.op_domain)
+-
+- def test_legacy_schema_accessible_after_deregister(self):
+- op_schema = defs.OpSchema(
+- self.op_type,
+- self.op_domain,
+- self.op_version,
+- )
+- onnx.defs.register_schema(op_schema)
+- schema_a = onnx.defs.get_schema(
+- op_schema.name, op_schema.since_version, op_schema.domain
+- )
+- schema_b = onnx.defs.get_schema(op_schema.name, op_schema.domain)
+-
+- def filter_schema(schemas):
+- return [op for op in schemas if op.name == op_schema.name]
+-
+- schema_c = filter_schema(onnx.defs.get_all_schemas())
+- schema_d = filter_schema(onnx.defs.get_all_schemas_with_history())
+- self.assertEqual(len(schema_c), 1)
+- self.assertEqual(len(schema_d), 1)
+- # Avoid memory residue and access storage as much as possible
+- self.assertEqual(str(schema_a), str(op_schema))
+- self.assertEqual(str(schema_b), str(op_schema))
+- self.assertEqual(str(schema_c[0]), str(op_schema))
+- self.assertEqual(str(schema_d[0]), str(op_schema))
+-
+-
+ if __name__ == "__main__":
+ unittest.main()
+diff --git a/onnx/test/shape_inference_test.py b/onnx/test/shape_inference_test.py
+index ee22c8ffb..849ae042a 100644
+--- a/onnx/test/shape_inference_test.py
++++ b/onnx/test/shape_inference_test.py
+@@ -12,7 +12,6 @@ from typing import TYPE_CHECKING, Any
+ import numpy as np
+ import pytest
+ from google.protobuf import text_format
+-from parameterized import parameterized
+
+ import onnx.shape_inference
+ from onnx import (
+@@ -244,86 +243,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ graph, [make_tensor_value_info("y", TensorProto.FLOAT, (30, 4, 5))]
+ )
+
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose(self, _, version) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_preexisting(self, _, version) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+- [make_tensor_value_info("Y", TensorProto.FLOAT, None)],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_scalar(self, _, version) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, ())],
+- [make_node("Transpose", ["X"], ["Y"])],
+- [],
+- )
+-
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("Y", TensorProto.FLOAT, ())],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_partial(self, _, version) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+- [make_tensor_value_info("Y", TensorProto.UNDEFINED, (3, "a", "b"))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_preexisting_incorrect_shape(self, *_) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+- [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5))],
+- )
+- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_preexisting_incorrect_type(self, *_) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
+- [make_tensor_value_info("Y", TensorProto.STRING, (3, 2, 4))],
+- )
+- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+-
+- @parameterized.expand(all_versions_for("Transpose"))
+- def test_transpose_incorrect_repeated_perm(self, *_) -> None:
+- graph = self._make_graph(
+- [("X", TensorProto.FLOAT, (2, 3, 4))],
+- [make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 1])],
+- [],
+- )
+- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+-
+ def _make_matmul_test_all_dims_known(
+ self, version, shape1: Sequence[int], shape2: Sequence[int]
+ ) -> None:
+@@ -342,21 +261,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+ )
+
+- @parameterized.expand(all_versions_for("MatMul"))
+- def test_matmul_all_dims_known(self, _, version) -> None:
+- self._make_matmul_test_all_dims_known(version, (2,), (2,))
+-
+- self._make_matmul_test_all_dims_known(version, (4, 2), (2, 4))
+- self._make_matmul_test_all_dims_known(version, (5, 2), (2, 4))
+- self._make_matmul_test_all_dims_known(version, (5, 2), (2, 1))
+- self._make_matmul_test_all_dims_known(version, (1, 2), (2, 3))
+- self._make_matmul_test_all_dims_known(version, (2,), (2, 3))
+- self._make_matmul_test_all_dims_known(version, (4, 2), (2,))
+- self._make_matmul_test_all_dims_known(version, (1, 4, 2), (3, 2, 3))
+- self._make_matmul_test_all_dims_known(version, (3, 4, 2), (3, 2, 3))
+- self._make_matmul_test_all_dims_known(version, (5, 1, 4, 2), (1, 3, 2, 3))
+- self._make_matmul_test_all_dims_known(version, (4, 2), (3, 2, 3))
+-
+ def _make_matmul_test_allow_unknown(
+ self, version, shape1: Any, shape2: Any, expected_out_shape: Any
+ ) -> None:
+@@ -367,1318 +271,39 @@ class TestShapeInference(TestShapeInferenceHelper):
+ )
+ self._assert_inferred(
+ graph,
+- [make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("MatMul"))
+- def test_matmul_allow_unknown(self, _, version) -> None:
+- self._make_matmul_test_allow_unknown(version, (None,), (None,), ())
+- self._make_matmul_test_allow_unknown(version, (3,), (None,), ())
+- self._make_matmul_test_allow_unknown(version, (2,), (2, "a"), ("a",))
+- self._make_matmul_test_allow_unknown(version, (4, 2), (2, "a"), (4, "a"))
+- self._make_matmul_test_allow_unknown(version, (4, None), (2, "a"), (4, "a"))
+- self._make_matmul_test_allow_unknown(version, (4, None), (None, "a"), (4, "a"))
+- self._make_matmul_test_allow_unknown(
+- version, (1, 4, 2), ("a", 2, 5), ("a", 4, 5)
+- )
+- self._make_matmul_test_allow_unknown(
+- version, (1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)
+- )
+- self._make_matmul_test_allow_unknown(version, (3,), None, None)
+- self._make_matmul_test_allow_unknown(version, None, None, None)
+-
+- @parameterized.expand(all_versions_for("Cast"))
+- def test_cast(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Cast", ["x"], ["y"], to=TensorProto.UINT8)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (2, 4, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Cast"))
+- @unittest.skip(
+- "Issue #5960"
+- ) # FIXME(#5960) propagateElemTypeFromAttributeToOutput does not validate against output type constraints
+- def test_cast_to_complex(self, _, version) -> None: # noqa: ARG002
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Cast", ["x"], ["y"], to=TensorProto.COMPLEX128)],
+- [],
+- )
+-
+- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+-
+- @parameterized.expand(all_versions_for("CastLike"))
+- def test_cast_like(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3)), ("t", TensorProto.FLOAT16, ("N",))],
+- [make_node("CastLike", ["x", "t"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT16, (2, 4, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_same_size(self, _, version) -> None:
+- # Test bitcast between types of same size (float32 -> int32)
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT32)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_scalar(self, _, version) -> None:
+- # Test bitcast with scalar input (same size)
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, ())],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT32)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, ())],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_1d(self, _, version) -> None:
+- # Test bitcast with 1D tensor (float32 -> uint32, same size)
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (8,))],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.UINT32)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT32, (8,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_double_to_int64(self, _, version) -> None:
+- # Test bitcast between 64-bit types (double -> int64)
+- graph = self._make_graph(
+- [("x", TensorProto.DOUBLE, (3, 5))],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT64)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (3, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_int8_to_uint8(self, _, version) -> None:
+- # Test bitcast between 8-bit types (int8 -> uint8)
+- graph = self._make_graph(
+- [("x", TensorProto.INT8, (4, 6))],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.UINT8)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (4, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("BitCast"))
+- def test_bitcast_float16_to_int16(self, _, version) -> None:
+- # Test bitcast between 16-bit types (float16 -> int16)
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT16, (2, 3))],
+- [make_node("BitCast", ["x"], ["y"], to=TensorProto.INT16)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT16, (2, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Col2Im"))
+- def test_col2im(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("input", TensorProto.FLOAT, (1, 5, 5)),
+- ("output_shape", TensorProto.INT64, (2,)),
+- ("kernel_shape", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
+- )
+- ],
+- [],
+- initializer=[
+- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
+- make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Col2Im"))
+- def test_col2im_strides(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("input", TensorProto.FLOAT, (1, 9, 4)),
+- ("output_shape", TensorProto.INT64, (2,)),
+- ("kernel_shape", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Col2Im",
+- ["input", "output_shape", "kernel_shape"],
+- ["output"],
+- strides=[2, 2],
+- )
+- ],
+- [],
+- initializer=[
+- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
+- make_tensor("kernel_shape", TensorProto.INT64, (2,), (3, 3)),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Col2Im"))
+- def test_col2im_pads(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("input", TensorProto.FLOAT, (1, 5, 15)),
+- ("output_shape", TensorProto.INT64, (2,)),
+- ("kernel_shape", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Col2Im",
+- ["input", "output_shape", "kernel_shape"],
+- ["output"],
+- pads=[0, 1, 0, 1],
+- )
+- ],
+- [],
+- initializer=[
+- make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
+- make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Col2Im"))
+- def test_col2im_dilations(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("input", TensorProto.FLOAT, (1, 4, 5)),
+- ("output_shape", TensorProto.INT64, (2,)),
+- ("kernel_shape", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Col2Im",
+- ["input", "output_shape", "kernel_shape"],
+- ["output"],
+- dilations=[1, 5],
+- )
+- ],
+- [],
+- initializer=[
+- make_tensor("output_shape", TensorProto.INT64, (2,), (6, 6)),
+- make_tensor("kernel_shape", TensorProto.INT64, (2,), (2, 2)),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 6, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Col2Im"))
+- def test_col2im_5d(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("input", TensorProto.FLOAT, (1, 10, 12)),
+- ("output_shape", TensorProto.INT64, (3,)),
+- ("kernel_shape", TensorProto.INT64, (3,)),
+- ],
+- [
+- make_node(
+- "Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
+- )
+- ],
+- [],
+- initializer=[
+- make_tensor("output_shape", TensorProto.INT64, (3,), (3, 4, 5)),
+- make_tensor("kernel_shape", TensorProto.INT64, (3,), (1, 1, 5)),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("output", TensorProto.FLOAT, (1, 2, 3, 4, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Concat"))
+- def test_concat(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3)), ("y", TensorProto.FLOAT, (7, 4, 3))],
+- [make_node("Concat", ["x", "y"], ["z"], axis=0)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.FLOAT, (9, 4, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Concat"))
+- def test_concat_missing_shape(self, *_) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (2, 4, 3)),
+- "y",
+- ("z", TensorProto.FLOAT, (None, None, None)),
+- ],
+- [make_node("Concat", ["x", "y", "z"], ["out"], axis=0)],
+- [],
+- )
+- self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
+-
+- @parameterized.expand(all_versions_for("Concat"))
+- def test_concat_3d_axis_2(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 2, 2)), ("y", TensorProto.FLOAT, (2, 2, 2))],
+- [make_node("Concat", ["x", "y"], ["z"], axis=2)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.FLOAT, (2, 2, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Concat"))
+- def test_concat_param(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, ("a", 2)), ("y", TensorProto.FLOAT, ("a", 3))],
+- [make_node("Concat", ["x", "y"], ["z"], axis=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.FLOAT, ("a", 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Concat"))
+- def test_concat_param_single_input(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, ("a", 2))],
+- [make_node("Concat", ["x"], ["z"], axis=0)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.FLOAT, ("a", 2))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_dynamic_shape_known_rank(self, _, version) -> None:
+- self.skipIf(version < 14, "Rank inference is added from Version 14")
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (None, None))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_dynamic_shape_symbolic(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, ("M",))],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, None)],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_dynamic_unknown_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, None)],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, None)],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_static_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (2,), (3, 8))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (3, 8))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_static_shape_inferred(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (3,))],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 3, -1))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (2, 3, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_static_shape_zero(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (1, 1, 1)), ("shape", TensorProto.INT64, (3,))],
+- [make_node("Reshape", ["x", "shape"], ["y"])],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (1, 1, 1))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_static_shape_allowzero(self, _, version) -> None:
+- self.skipIf(version < 14, "allowzero is added from Version 14")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.UINT8, (1, 0, 0)),
+- ("shape", TensorProto.INT64, (3,)),
+- ],
+- [make_node("Reshape", ["x", "shape"], ["y"], allowzero=1)],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.UINT8, (0, 1, 1))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Reshape"))
+- def test_reshape_static_shape_constant(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.UINT8, (2, 4, 3))],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["shape"],
+- value=make_tensor("shape", TensorProto.INT64, (2,), (3, 8)),
+- ),
+- make_node("Reshape", ["x", "shape"], ["y"]),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("shape", TensorProto.INT64, (2,)),
+- make_tensor_value_info("y", TensorProto.UINT8, (3, 8)),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Upsample"))
+- def test_upsample(self, _, version) -> None:
+- if version == 7:
+- graph = self._make_graph(
+- [("x", TensorProto.INT32, (2, 4, 3, 5))],
+- [make_node("Upsample", ["x"], ["y"], scales=[1.0, 1.1, 1.3, 1.9])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+- else:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ],
+- [make_node("Upsample", ["x", "scales"], ["y"])],
+- [],
+- initializer=[
+- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
+- ],
+- )
+-
+- def call_inference():
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- if version == 9:
+- call_inference()
+- else:
+- # Upsample is deprecated since Version 10.
+- with self.assertRaises(onnx.checker.ValidationError) as cm:
+- call_inference()
+- exception = cm.exception
+- assert "Upsample is deprecated" in str(exception)
+-
+- @parameterized.expand(all_versions_for("Upsample"))
+- def test_upsample_raw_data(self, _, version) -> None:
+- if version == 7:
+- graph = self._make_graph(
+- [("x", TensorProto.INT32, (1, 3, 4, 5))],
+- [make_node("Upsample", ["x"], ["y"], scales=[2.0, 1.1, 2.3, 1.9])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+- else:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ],
+- [make_node("Upsample", ["x", "scales"], ["y"])],
+- [],
+- initializer=[
+- make_tensor(
+- "scales",
+- TensorProto.FLOAT,
+- (4,),
+- vals=np.array([1.0, 1.1, 1.3, 1.9], dtype="<f4").tobytes(),
+- raw=True,
+- )
+- ],
+- ) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+-
+- def call_inference():
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- if version == 9:
+- call_inference()
+- else:
+- # Upsample is deprecated since Version 10.
+- with self.assertRaises(onnx.checker.ValidationError) as cm:
+- call_inference()
+- exception = cm.exception
+- assert "Upsample is deprecated" in str(exception)
+-
+- @parameterized.expand(all_versions_for("Expand"))
+- def test_expand(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (3,))],
+- [make_node("Expand", ["x", "shape"], ["y"])],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (3,), (2, 1, 6))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Expand"))
+- def test_expand_scalar_input(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.INT32, ()), ("shape", TensorProto.INT64, (2,))],
+- [make_node("Expand", ["x", "shape"], ["y"])],
+- [],
+- initializer=[make_tensor("shape", TensorProto.INT64, (2,), (4, 8))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (4, 8))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Expand"))
+- def test_expand_raw_data(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (2,))],
+- [make_node("Expand", ["x", "shape"], ["y"])],
+- [],
+- initializer=[
+- make_tensor(
+- "shape",
+- TensorProto.INT64,
+- (2,),
+- vals=np.array([3, 4], dtype="<i8").tobytes(),
+- raw=True,
+- )
+- ],
+- ) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (3, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Expand"))
+- def test_expand_dynamic_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (1, 2, None)),
+- ("shape", TensorProto.INT64, (3,)),
+- ],
+- [make_node("Expand", ["x", "shape"], ["y"])],
+- [],
+- initializer=[],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (None, 2, None))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Expand"))
+- def test_expand_symbolic_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (1, 2, None)),
+- ("shape", TensorProto.INT64, ("unk__0",)),
+- ],
+- [make_node("Expand", ["x", "shape"], ["y"])],
+- [],
+- initializer=[],
+- )
+- # if giving a symbolic shape, Expand should not infer any shape or rank inference
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, None)],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size(self, _, version) -> None:
+- if version == 10:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ],
+- [make_node("Resize", ["x", "scales"], ["y"])],
+- [],
+- initializer=[
+- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+- elif version == 11:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (4,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
+- [],
+- initializer=[
+- make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+- else:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("sizes", TensorProto.INT64, (4,)),
+- ],
+- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"])],
+- [],
+- initializer=[
+- make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("RMSNormalization"))
+- def test_rms_normalization(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("X", TensorProto.FLOAT, ("N", "C", "H", "W")),
+- ("scale", TensorProto.FLOAT, ("H", "W")),
+- ],
+- [make_node("RMSNormalization", ["X", "scale"], ["y"], axis=2)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, ("N", "C", "H", "W"))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_axes_2_3(self, _, version) -> None:
+- self.skipIf(version < 18, "axes is from Version 18")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(2, 3))],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 7))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_axes_3_2(self, _, version) -> None:
+- self.skipIf(version < 18, "axes is from Version 18")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(3, 2))],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 7, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_not_larger(self, _, version) -> None:
+- self.skipIf(
+- version < 18,
+- "keep_aspect_ratio_policy is from Version 18",
+- )
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Resize",
+- ["x", "roi", "", "sizes"],
+- ["y"],
+- keep_aspect_ratio_policy="not_larger",
+- )
+- ],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (4, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_axes_2_3_not_larger(self, _, version) -> None:
+- self.skipIf(
+- version < 18,
+- "axes & keep_aspect_ratio_policy are from Version 18",
+- )
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Resize",
+- ["x", "roi", "", "sizes"],
+- ["y"],
+- axes=(2, 3),
+- keep_aspect_ratio_policy="not_larger",
+- )
+- ],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 4, 6))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_not_smaller(self, _, version) -> None:
+- self.skipIf(
+- version < 18,
+- "keep_aspect_ratio_policy is from Version 18",
+- )
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Resize",
+- ["x", "roi", "", "sizes"],
+- ["y"],
+- keep_aspect_ratio_policy="not_smaller",
+- )
+- ],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (6, 10))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_size_axes_2_3_not_smaller(self, _, version) -> None:
+- self.skipIf(
+- version < 18,
+- "axes & keep_aspect_ratio_policy are from Version 18",
+- )
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (2,)),
+- ],
+- [
+- make_node(
+- "Resize",
+- ["x", "roi", "", "sizes"],
+- ["y"],
+- axes=(2, 3),
+- keep_aspect_ratio_policy="not_smaller",
+- )
+- ],
+- [],
+- initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 10))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_scale(self, _, version) -> None:
+- self.skipIf(version < 11, "roi input is from Version 11")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales"], ["y"])],
+- [],
+- initializer=[
+- make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_scale_axes_2_3(self, _, version) -> None:
+- self.skipIf(version < 18, "axes is from Version 18")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (2,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(2, 3))],
+- [],
+- initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.3, 1.9))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_scale_axes_3_2(self, _, version) -> None:
+- self.skipIf(version < 18, "axes is from Version 18")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (2, 4, 3, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (2,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(3, 2))],
+- [],
+- initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.9, 1.3))],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_scale_raw_data(self, _, version) -> None:
+- self.skipIf(version < 11, "roi input is from Version 11")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (1, 3, 4, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales"], ["y"])],
+- [],
+- initializer=[
+- make_tensor(
+- "scales",
+- TensorProto.FLOAT,
+- (4,),
+- vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
+- raw=True,
+- )
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_scale_and_size_but_one_is_empty(self, _, version) -> None:
+- self.skipIf(version < 11, "roi input is from Version 11")
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (1, 3, 4, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (4,)),
+- ("sizes", TensorProto.INT64, (0,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
+- [],
+- initializer=[
+- make_tensor(
+- "scales",
+- TensorProto.FLOAT,
+- (4,),
+- vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
+- raw=True,
+- ),
+- make_tensor(
+- "sizes",
+- TensorProto.INT64,
+- (0,),
+- vals=np.array([], dtype="<i8").tobytes(),
+- raw=True,
+- ),
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Resize"))
+- def test_resize_opset11_scales_is_empty(self, _, version) -> None:
+- self.skipIf(version != 11, "This test only works for Version 11")
+- # "scales" input in Resize in opset11 is not optional. It must be an empty tensor
+- # if sizes is needed. Shape inference for Resize shall handle this case.
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.INT32, (1, 3, 4, 5)),
+- ("roi", TensorProto.FLOAT, (8,)),
+- ("scales", TensorProto.FLOAT, (0,)),
+- ("sizes", TensorProto.INT64, (4,)),
+- ],
+- [make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
+- [],
+- initializer=[
+- make_tensor(
+- "sizes",
+- TensorProto.INT64,
+- (4,),
+- vals=np.array(
+- [2, 6, 8, 10], dtype="<i8"
+- ).tobytes(), # double in all dimensions
+- raw=True,
+- ),
+- ],
+- )
+-
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT32, (2, 6, 8, 10))],
+- opset_imports=[helper.make_opsetid("", version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape_start_1(self, _, version) -> None:
+- self.skipIf(version < 15, "start and end are from Version 15")
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"], start=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (2,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape_end_1(self, _, version) -> None:
+- self.skipIf(version < 15, "start and end are from Version 15")
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"], end=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (1,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape_negative_start(self, _, version) -> None:
+- self.skipIf(version < 15, "start and end are from Version 15")
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"], start=-1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (1,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape_clip1(self, _, version) -> None:
+- self.skipIf(version < 15, "start and end are from Version 15")
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"], start=-5)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Shape"))
+- def test_shape_clip2(self, _, version) -> None:
+- self.skipIf(version < 15, "start and end are from Version 15")
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))],
+- [make_node("Shape", ["x"], ["y"], end=10)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (3,))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Size"))
+- def test_size(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 4, 3))], [make_node("Size", ["x"], ["y"])], []
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, ())],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Gather"))
+- def test_gather(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (4, 3)), ("i", TensorProto.INT64, (2,))],
+- [make_node("Gather", ["x", "i"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Gather"))
+- def test_gather_axis1(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (4, 3, 5)), ("i", TensorProto.INT64, (1, 2))],
+- [make_node("Gather", ["x", "i"], ["y"], axis=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (4, 1, 2, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Gather"))
+- def test_gather_into_scalar(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (3,)), ("i", TensorProto.INT64, ())],
+- [make_node("Gather", ["x", "i"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, ())],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("GatherElements"))
+- def test_gather_elements(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2, 2)), ("i", TensorProto.INT64, (2, 2))],
+- [make_node("GatherElements", ["x", "i"], ["y"], axis=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("GatherElements"))
+- def test_gather_elements_axis0(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (3, 3)), ("i", TensorProto.INT64, (2, 3))],
+- [make_node("GatherElements", ["x", "i"], ["y"], axis=0)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Scatter"))
+- def test_scatter(self, _, version) -> None:
+- if version >= 11:
+- # Scatter is deprecated in domain_version of 11.
+- with self.assertRaises(onnx.checker.ValidationError) as cm:
+- self._test_scatter(version)
+- exception = cm.exception
+- assert "Scatter is deprecated" in str(exception)
+- else:
+- self._test_scatter(version)
+-
+- def _test_scatter(self, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (3, 3)),
+- ("i", TensorProto.INT64, (2, 3)),
+- ("u", TensorProto.FLOAT, (2, 3)),
+- ],
+- [make_node("Scatter", ["x", "i", "u"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("Scatter"))
+- def test_scatter_axis1(self, _, version) -> None:
+- if version >= 11:
+- # Scatter is deprecated in domain_version of 11.
+- with self.assertRaises(onnx.checker.ValidationError) as cm:
+- self._test_scatter_axis1(version)
+- exception = cm.exception
+- assert "Scatter is deprecated" in str(exception)
+- else:
+- self._test_scatter_axis1(version)
+-
+- def _test_scatter_axis1(self, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (1, 5)),
+- ("i", TensorProto.INT64, (1, 2)),
+- ("u", TensorProto.FLOAT, (1, 2)),
+- ],
+- [make_node("Scatter", ["x", "i", "u"], ["y"], axis=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("ScatterElements"))
+- def test_scatter_elements(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (3, 3)),
+- ("i", TensorProto.INT64, (2, 3)),
+- ("u", TensorProto.FLOAT, (2, 3)),
+- ],
+- [make_node("ScatterElements", ["x", "i", "u"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("ScatterElements"))
+- def test_scatter_elements_axis1(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (1, 5)),
+- ("i", TensorProto.INT64, (1, 2)),
+- ("u", TensorProto.FLOAT, (1, 2)),
+- ],
+- [make_node("ScatterElements", ["x", "i", "u"], ["y"], axis=1)],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
++ [make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+ )
+
+- @parameterized.expand(all_versions_for("ScatterND"))
+- def test_scatternd(self, _, version) -> None:
++ def _test_scatter(self, version) -> None:
+ graph = self._make_graph(
+ [
+- ("x", TensorProto.FLOAT, (4, 5, 6)),
+- ("indices", TensorProto.INT64, (3, 3, 2)),
+- ("updates", TensorProto.FLOAT, (3, 3, 6)),
++ ("x", TensorProto.FLOAT, (3, 3)),
++ ("i", TensorProto.INT64, (2, 3)),
++ ("u", TensorProto.FLOAT, (2, 3)),
+ ],
+- [make_node("ScatterND", ["x", "indices", "updates"], ["y"])],
++ [make_node("Scatter", ["x", "i", "u"], ["y"])],
+ [],
+ )
+ self._assert_inferred(
+ graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (4, 5, 6))],
++ [make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+ )
+
+- @parameterized.expand(all_versions_for("ScatterND"))
+- def test_scatternd_noshape(self, _, version) -> None:
+- # The shape of 'x_reshaped' cannot be inferred, since it is the output of a dynamic reshape.
+- # Thus the shape of 'y' is also None.
++ def _test_scatter_axis1(self, version) -> None:
+ graph = self._make_graph(
+ [
+- ("x", TensorProto.FLOAT, (4, 5, 6)),
+- ("indices", TensorProto.INT64, (3, 3, 2)),
+- ("updates", TensorProto.FLOAT, (3, 3, 6)),
+- ("shape", TensorProto.INT64, ("M",)),
+- ],
+- [
+- make_node("Reshape", ["x", "shape"], ["x_reshaped"]),
+- make_node("ScatterND", ["x_reshaped", "indices", "updates"], ["y"]),
++ ("x", TensorProto.FLOAT, (1, 5)),
++ ("i", TensorProto.INT64, (1, 2)),
++ ("u", TensorProto.FLOAT, (1, 2)),
+ ],
++ [make_node("Scatter", ["x", "i", "u"], ["y"], axis=1)],
+ [],
+ )
+ self._assert_inferred(
+ graph,
+- [
+- make_tensor_value_info("x_reshaped", TensorProto.FLOAT, None),
+- make_tensor_value_info("y", TensorProto.FLOAT, None),
+- ],
++ [make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+ )
+
+@@ -1709,95 +334,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 24)],
+ )
+
+- @parameterized.expand(all_versions_for("Squeeze"))
+- def test_squeeze(self, _, version) -> None:
+- if version == 11:
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1))],
+- [make_node("Squeeze", "x", "y", axes=[0, 2, 3, 5])],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+- else:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1)),
+- ("axes", TensorProto.INT64, (4,)),
+- ],
+- [make_node("Squeeze", ["x", "axes"], "y")],
+- [],
+- initializer=[
+- make_tensor("axes", TensorProto.INT64, (4,), (0, 2, 3, 5))
+- ],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringConcat"))
+- def test_stringconcat(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, (2, 3, 4)),
+- ("y", TensorProto.STRING, (2, 3, 4)),
+- ],
+- [make_node("StringConcat", ["x", "y"], "z")],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringConcat"))
+- def test_stringconcat_broadcasting(self, _, version) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, (2, 3, 4)),
+- ("y", TensorProto.STRING, (1, 3, 1)),
+- ],
+- [make_node("StringConcat", ["x", "y"], "z")],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("RegexFullMatch"))
+- def test_regex_full_match(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.STRING, (2, 4, 3, 9))],
+- [make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.BOOL, (2, 4, 3, 9))],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("RegexFullMatch"))
+- def test_regex_full_match_empty_shape(self, _, version) -> None:
+- graph = self._make_graph(
+- [("x", TensorProto.STRING, ())],
+- [make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.BOOL, ())],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+ def test_squeeze_no_axes_opset11(self) -> None:
+ graph = self._make_graph(
+ [
+@@ -5632,23 +4168,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ self._make_matmulinteger_test((5, 1, 4, 2), (1, 3, 2, 3))
+ self._make_matmulinteger_test((4, 2), (3, 2, 3))
+
+- @parameterized.expand(
+- [onnx.TensorProto.FLOAT, onnx.TensorProto.FLOAT16, onnx.TensorProto.BFLOAT16]
+- )
+- def test_quantizelinear(self, elem_type) -> None:
+- graph = self._make_graph(
+- [
+- ("x", elem_type, (30, 4, 5)),
+- ("y_scale", elem_type, ()),
+- ("y_zero_point", TensorProto.UINT8, ()),
+- ],
+- [make_node("QuantizeLinear", ["x", "y_scale", "y_zero_point"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5))]
+- )
+-
+ def test_quantizelinear_default_zp(self) -> None:
+ graph = self._make_graph(
+ [("x", TensorProto.FLOAT, (30, 4, 5)), ("y_scale", TensorProto.FLOAT, ())],
+@@ -5754,23 +4273,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ graph,
+ )
+
+- @parameterized.expand(
+- [onnx.TensorProto.FLOAT, onnx.TensorProto.FLOAT16, onnx.TensorProto.BFLOAT16]
+- )
+- def test_dequantizelinear(self, elem_type) -> None:
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.UINT8, (30, 4, 5)),
+- ("x_scale", elem_type, ()),
+- ("x_zero_point", TensorProto.UINT8, ()),
+- ],
+- [make_node("DequantizeLinear", ["x", "x_scale", "x_zero_point"], ["y"])],
+- [],
+- )
+- self._assert_inferred(
+- graph, [make_tensor_value_info("y", elem_type, (30, 4, 5))]
+- )
+-
+ def test_dynamicquantizelinear(self) -> None:
+ graph = self._make_graph(
+ [("x", TensorProto.FLOAT, (30, 4, 5))],
+@@ -6005,319 +4507,61 @@ class TestShapeInference(TestShapeInferenceHelper):
+ [make_node("RoiAlign", ["x", "rois", "batch_indices"], ["y"])],
+ [],
+ )
+- self._assert_inferred(
+- graph, [make_tensor_value_info("y", TensorProto.FLOAT, (15, "C", 1, 1))]
+- )
+-
+- def test_rotaryembedding_4d(self) -> None:
+- graph = self._make_graph(
+- [
+- ("X", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")),
+- ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
+- ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
+- ("position_ids", TensorProto.INT64, ("B", "seq_len")),
+- ],
+- [
+- make_node(
+- "RotaryEmbedding",
+- ["X", "cos_cache", "sin_cache", "position_ids"],
+- ["Y"],
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info(
+- "Y", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")
+- )
+- ],
+- )
+-
+- def test_rotaryembedding_3d(self) -> None:
+- graph = self._make_graph(
+- [
+- ("X", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")),
+- ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
+- ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
+- ("position_ids", TensorProto.INT64, ("B", "seq_len")),
+- ],
+- [
+- make_node(
+- "RotaryEmbedding",
+- ["X", "cos_cache", "sin_cache", "position_ids"],
+- ["Y"],
+- num_heads=4,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info(
+- "Y", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")
+- )
+- ],
+- )
+-
+- @parameterized.expand(
+- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
+- )
+- def test_label_encoder_string_int64(self, _, version) -> None:
+- self.skipIf(
+- version < 2, "keys_* attributes were introduced in ai.onnx.ml opset 2"
+- )
+- string_list = ["A", "m", "y"]
+- float_list = [94.17, 36.00, -99.0]
+- int64_list = [12, 28, 86]
+- graph = self._make_graph(
+- [("x", TensorProto.STRING, (6, 1))],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_strings=string_list,
+- values_int64s=int64_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (6, 1))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.INT64, (2, 3))],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_int64s=int64_list,
+- values_strings=string_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.STRING, (2, 3))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, (2,))],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_floats=float_list,
+- values_int64s=int64_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.INT64, (2,))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.INT64, (8,))],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_int64s=int64_list,
+- values_floats=float_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (8,))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.FLOAT, ())],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_floats=float_list,
+- values_strings=string_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.STRING, ())],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.STRING, (1, 2))],
+- [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_strings=string_list,
+- values_floats=float_list,
+- )
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", TensorProto.FLOAT, (1, 2))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- @parameterized.expand(
+- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
+- )
+- def test_label_encoder_tensor_attributes(self, _, version) -> None:
+- self.skipIf(
+- version < 4, "tensor attributes were introduced in ai.onnx.ml opset 4"
+- )
+- key_tensor = make_tensor(
+- "keys_tensor", TensorProto.STRING, [4], ["a", "b", "cc", "ddd"]
+- )
+- values_tensor = make_tensor(
+- "values_tensor", TensorProto.INT64, [4], [1, 2, 3, 4]
++ self._assert_inferred(
++ graph, [make_tensor_value_info("y", TensorProto.FLOAT, (15, "C", 1, 1))]
+ )
++
++ def test_rotaryembedding_4d(self) -> None:
+ graph = self._make_graph(
+- [("x", TensorProto.STRING, ("M", None, 3, 12))],
++ [
++ ("X", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")),
++ ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
++ ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
++ ("position_ids", TensorProto.INT64, ("B", "seq_len")),
++ ],
+ [
+ make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_tensor=key_tensor,
+- values_tensor=values_tensor,
+- default_tensor=make_tensor(
+- "default_tensor", TensorProto.INT64, [1], [0]
+- ),
++ "RotaryEmbedding",
++ ["X", "cos_cache", "sin_cache", "position_ids"],
++ ["Y"],
+ )
+ ],
+ [],
+ )
+ self._assert_inferred(
+ graph,
+- [make_tensor_value_info("y", TensorProto.INT64, ("M", None, 3, 12))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
++ [
++ make_tensor_value_info(
++ "Y", TensorProto.FLOAT, ("B", "num_heads", "seq_len", "head_size")
++ )
+ ],
+ )
+
+- @parameterized.expand(
+- all_versions_for("LabelEncoder") if ONNX_ML else [], skip_on_empty=True
+- )
+- def test_label_encoder_tensor_attributes_invalid_configurations(
+- self, _, version
+- ) -> None:
+- self.skipIf(version < 4, "tensor attributes introduced in ai.onnx.ml opset 4")
+- key_tensor = make_tensor(
+- "keys_tensor", TensorProto.STRING, [4], ["a", "b", "cc", "ddd"]
+- )
+- values_tensor = make_tensor(
+- "values_tensor", TensorProto.INT64, [4], [1, 2, 3, 4]
+- )
+-
+- opset_imports = [
+- make_opsetid(ONNX_ML_DOMAIN, version),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ]
+-
+- # default_tensor should be INT64, same type as values_tensor
++ def test_rotaryembedding_3d(self) -> None:
+ graph = self._make_graph(
+- [("x", TensorProto.STRING, ("M", None, 3, 12))],
++ [
++ ("X", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")),
++ ("cos_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
++ ("sin_cache", TensorProto.FLOAT, ("max_seq_len", "head_size_div_2")),
++ ("position_ids", TensorProto.INT64, ("B", "seq_len")),
++ ],
+ [
+ make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_tensor=key_tensor,
+- values_tensor=values_tensor,
+- default_tensor=make_tensor(
+- "default_tensor", TensorProto.INT32, [1], [0]
+- ),
++ "RotaryEmbedding",
++ ["X", "cos_cache", "sin_cache", "position_ids"],
++ ["Y"],
++ num_heads=4,
+ )
+ ],
+ [],
+ )
+-
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
++ self._assert_inferred(
+ graph,
+- opset_imports=opset_imports,
+- )
+-
+- # default_tensor should be a singleton of shape (1,)
+- graph = self._make_graph(
+- [("x", TensorProto.STRING, ("M", None, 3, 12))],
+ [
+- make_node(
+- "LabelEncoder",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- keys_tensor=key_tensor,
+- values_strings=["a", "b", "cc", "ddd"],
+- default_tensor=make_tensor(
+- "default_tensor", TensorProto.STRING, [1, 2], ["a", "b"]
+- ),
++ make_tensor_value_info(
++ "Y", TensorProto.FLOAT, ("B", "seq_len", "hidden_size")
+ )
+ ],
+- [],
+- )
+-
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
+- graph,
+- opset_imports=opset_imports,
+ )
+
+ def make_sparse(
+@@ -8746,105 +6990,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ )
+ self._assert_inferred(graph, [output_tensor_val_info])
+
+- @parameterized.expand(all_versions_for("StringSplit"))
+- def test_string_split_basic(self, _, version) -> None:
+- substrings = make_tensor_value_info(
+- "substrings",
+- TensorProto.STRING,
+- (2, None),
+- )
+- length = make_tensor_value_info("length", TensorProto.INT64, (2,))
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, (2,)),
+- ],
+- [make_node("StringSplit", ["x"], ["substrings", "length"])],
+- [substrings, length],
+- )
+- self._assert_inferred(
+- graph,
+- [substrings, length],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringSplit"))
+- def test_string_split_symbolic(self, _, version) -> None:
+- substrings = make_tensor_value_info(
+- "substrings",
+- TensorProto.STRING,
+- ("A", None),
+- )
+- length = make_tensor_value_info("length", TensorProto.INT64, ("A",))
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, ("A",)),
+- ],
+- [make_node("StringSplit", ["x"], ["substrings", "length"])],
+- [substrings, length],
+- )
+- self._assert_inferred(
+- graph,
+- [substrings, length],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringSplit"))
+- def test_string_split_nested(self, _, version) -> None:
+- substrings = make_tensor_value_info(
+- "substrings", TensorProto.STRING, (2, 4, 3, None)
+- )
+- length = make_tensor_value_info("length", TensorProto.INT64, (2, 4, 3))
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, (2, 4, 3)),
+- ],
+- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
+- [substrings, length],
+- )
+- self._assert_inferred(
+- graph,
+- [substrings, length],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringSplit"))
+- def test_string_split_zero_dimensional_input(self, _, version) -> None:
+- substrings = make_tensor_value_info("substrings", TensorProto.STRING, (None,))
+- length = make_tensor_value_info("length", TensorProto.INT64, ())
+-
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, ()),
+- ],
+- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
+- [substrings, length],
+- )
+- self._assert_inferred(
+- graph,
+- [substrings, length],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(all_versions_for("StringSplit"))
+- def test_string_split_empty_input(self, _, version) -> None:
+- substrings = make_tensor_value_info(
+- "substrings", TensorProto.STRING, ("M", 3, 0, None)
+- )
+- length = make_tensor_value_info("length", TensorProto.INT64, ("M", 3, 0))
+-
+- graph = self._make_graph(
+- [
+- ("x", TensorProto.STRING, ("M", 3, 0)),
+- ],
+- [make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
+- [substrings, length],
+- )
+- self._assert_inferred(
+- graph,
+- [substrings, length],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+ def test_optional_tensor_get_element(self) -> None:
+ tensor_type_proto = helper.make_tensor_type_proto(
+ elem_type=TensorProto.DOUBLE, shape=[2, 1, 4]
+@@ -9452,426 +7597,76 @@ class TestShapeInference(TestShapeInferenceHelper):
+ [],
+ )
+ self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("shape", TensorProto.INT64, ()),
+- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["shape"],
+- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+- ),
+- make_node("HannWindow", ["shape"], ["y"], periodic=0),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("shape", TensorProto.INT64, ()),
+- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
+- ],
+- )
+-
+- def test_blackmanwindow(self):
+- graph = self._make_graph(
+- [],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["shape"],
+- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+- ),
+- make_node("BlackmanWindow", ["shape"], ["y"]),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("shape", TensorProto.INT64, ()),
+- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
+- ],
+- )
+-
+- graph = self._make_graph(
+- [],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["shape"],
+- value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+- ),
+- make_node("BlackmanWindow", ["shape"], ["y"], periodic=0),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("shape", TensorProto.INT64, ()),
+- make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
+- ],
+- )
+-
+- @parameterized.expand(
+- [
+- (
+- name,
+- version,
+- test_aspect,
+- input_shape,
+- axis,
+- onesided,
+- inverse,
+- expected_shape,
+- )
+- for (name, version), (
+- test_aspect,
+- input_shape,
+- axis,
+- onesided,
+- inverse,
+- expected_shape,
+- ) in itertools.product(
+- all_versions_for("DFT"),
+- (
+- ("reals_default_axis", (2, 5, 1), None, None, None, (2, 5, 2)),
+- ("reals_axis_0", (3, 5, 10, 1), 0, 0, 0, (3, 5, 10, 2)),
+- ("reals_axis_1", (3, 5, 10, 1), 1, 0, 0, (3, 5, 10, 2)),
+- ("reals_axis_2", (3, 5, 10, 1), 2, 0, 0, (3, 5, 10, 2)),
+- ("reals_axis_neg", (3, 5, 10, 1), -2, 0, 0, (3, 5, 10, 2)),
+- ("reals_axis_0_onesided", (3, 5, 10, 1), 0, 1, 0, (2, 5, 10, 2)),
+- ("reals_axis_1_onesided", (3, 5, 10, 1), 1, 1, 0, (3, 3, 10, 2)),
+- ("reals_axis_2_onesided", (3, 5, 10, 1), 2, 1, 0, (3, 5, 6, 2)),
+- ("reals_axis_neg_onesided", (3, 5, 10, 1), -2, 1, 0, (3, 5, 6, 2)),
+- ("complex_default_axis", (2, 5, 2), None, None, None, (2, 5, 2)),
+- ("real_inverse", (2, 5, 1), 1, None, 1, (2, 5, 2)),
+- ("complex_inverse", (2, 5, 2), 1, None, 1, (2, 5, 2)),
+- ("irfft_axis_0", (2, 5, 10, 2), 0, 1, 1, (2, 5, 10, 1)),
+- ("irfft_axis_1", (3, 3, 10, 2), 1, 1, 1, (3, 4, 10, 1)),
+- ("irfft_axis_2", (3, 5, 6, 2), 2, 1, 1, (3, 5, 10, 1)),
+- ("irfft_axis_neg", (3, 5, 6, 2), -2, 1, 1, (3, 5, 10, 1)),
+- ),
+- )
+- ]
+- )
+- def test_dft(
+- self,
+- _: str,
+- version: int,
+- _test_aspect: str,
+- input_shape: tuple[int],
+- axis: int | None,
+- onesided: int | None,
+- inverse: int | None,
+- expected_shape: tuple[int],
+- ) -> None:
+- # Build the attributes for different opset versions
+- attributes = {}
+- if onesided is not None:
+- attributes["onesided"] = onesided
+- if inverse is not None:
+- attributes["inverse"] = inverse
+-
+- if version < 20:
+- if axis is not None:
+- attributes["axis"] = axis
+- nodes = [make_node("DFT", ["input", ""], ["output"], **attributes)]
+- value_infos = []
+- else:
+- assert version >= 20
+- if axis is not None:
+- nodes = [
+- make_node(
+- "Constant",
+- [],
+- ["axis"],
+- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
+- ),
+- make_node("DFT", ["input", "", "axis"], ["output"], **attributes),
+- ]
+- value_infos = [make_tensor_value_info("axis", TensorProto.INT64, ())]
+- else:
+- nodes = [
+- make_node("DFT", ["input", "", ""], ["output"], **attributes),
+- ]
+- value_infos = []
+-
+- # Construct the graph
+- graph = self._make_graph(
+- [],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- input_shape,
+- np.ones(input_shape, dtype=np.float32).flatten(),
+- ),
+- ),
+- *nodes,
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, input_shape),
+- *value_infos,
+- make_tensor_value_info("output", TensorProto.FLOAT, expected_shape),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+- @parameterized.expand(
+- [
+- (
+- name,
+- version,
+- test_aspect,
+- input_shape,
+- axis,
+- onesided,
+- inverse,
+- expected_shape,
+- )
+- for (name, version), (
+- test_aspect,
+- input_shape,
+- axis,
+- onesided,
+- inverse,
+- expected_shape,
+- ) in itertools.product(
+- all_versions_for("DFT"),
+- (
+- ("reals_default_axis", (2, 5, 1), None, None, None, (2, 42, 2)),
+- ("reals_axis_0", (3, 5, 10, 1), 0, 0, 0, (42, 5, 10, 2)),
+- ("reals_axis_1", (3, 5, 10, 1), 1, 0, 0, (3, 42, 10, 2)),
+- ("reals_axis_2", (3, 5, 10, 1), 2, 0, 0, (3, 5, 42, 2)),
+- ("reals_axis_neg", (3, 5, 10, 1), -2, 0, 0, (3, 5, 42, 2)),
+- ("reals_axis_0_onesided", (3, 5, 10, 1), 0, 1, 0, (22, 5, 10, 2)),
+- ("reals_axis_1_onesided", (3, 5, 10, 1), 1, 1, 0, (3, 22, 10, 2)),
+- ("reals_axis_2_onesided", (3, 5, 10, 1), 2, 1, 0, (3, 5, 22, 2)),
+- ("reals_axis_neg_onesided", (3, 5, 10, 1), -2, 1, 0, (3, 5, 22, 2)),
+- ("complex_default_axis", (2, 5, 2), None, None, None, (2, 42, 2)),
+- ("real_inverse", (2, 5, 1), 1, None, 1, (2, 42, 2)),
+- ("complex_inverse", (2, 5, 2), 1, None, 1, (2, 42, 2)),
+- ("irfft_axis_0", (2, 5, 10, 2), 0, 1, 1, (42, 5, 10, 1)),
+- ("irfft_axis_1", (3, 3, 10, 2), 1, 1, 1, (3, 42, 10, 1)),
+- ("irfft_axis_2", (3, 5, 6, 2), 2, 1, 1, (3, 5, 42, 1)),
+- ("irfft_axis_neg", (3, 5, 6, 2), -2, 1, 1, (3, 5, 42, 1)),
+- ),
+- )
+- ]
+- )
+- def test_dft_dft_length(
+- self,
+- _: str,
+- version: int,
+- _test_aspect: str,
+- input_shape: tuple[int],
+- axis: int | None,
+- onesided: int | None,
+- inverse: int | None,
+- expected_shape: tuple[int],
+- ) -> None:
+- # Build the attributes for different opset versions
+- attributes = {}
+- if onesided is not None:
+- attributes["onesided"] = onesided
+- if inverse is not None:
+- attributes["inverse"] = inverse
+-
+- dft_length = 42
+-
+- if version < 20:
+- if axis is not None:
+- attributes["axis"] = axis
+- nodes = [
+- make_node(
+- "Constant",
+- [],
+- ["dft_length"],
+- value=make_tensor(
+- "dft_length", TensorProto.INT64, (), (dft_length,)
+- ),
+- ),
+- make_node("DFT", ["input", "dft_length"], ["output"], **attributes),
+- ]
+- value_infos = [make_tensor_value_info("dft_length", TensorProto.INT64, ())]
+- else:
+- assert version >= 20
+- if axis is not None:
+- nodes = [
+- make_node(
+- "Constant",
+- [],
+- ["axis"],
+- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
+- ),
+- make_node(
+- "Constant",
+- [],
+- ["dft_length"],
+- value=make_tensor(
+- "dft_length", TensorProto.INT64, (), (dft_length,)
+- ),
+- ),
+- make_node(
+- "DFT",
+- ["input", "dft_length", "axis"],
+- ["output"],
+- **attributes,
+- ),
+- ]
+- value_infos = [
+- make_tensor_value_info("dft_length", TensorProto.INT64, ()),
+- make_tensor_value_info("axis", TensorProto.INT64, ()),
+- ]
+- else:
+- nodes = [
+- make_node(
+- "Constant",
+- [],
+- ["dft_length"],
+- value=make_tensor(
+- "dft_length", TensorProto.INT64, (), (dft_length,)
+- ),
+- ),
+- make_node(
+- "DFT",
+- ["input", "dft_length", ""],
+- ["output"],
+- **attributes,
+- ),
+- ]
+- value_infos = [
+- make_tensor_value_info("dft_length", TensorProto.INT64, ())
+- ]
++ graph,
++ [
++ make_tensor_value_info("shape", TensorProto.INT64, ()),
++ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
++ ],
++ )
+
+- # Construct the graph
+ graph = self._make_graph(
+ [],
+ [
+ make_node(
+ "Constant",
+ [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- input_shape,
+- np.ones(input_shape, dtype=np.float32).flatten(),
+- ),
++ ["shape"],
++ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+ ),
+- *nodes,
++ make_node("HannWindow", ["shape"], ["y"], periodic=0),
+ ],
+ [],
+ )
+ self._assert_inferred(
+ graph,
+ [
+- make_tensor_value_info("input", TensorProto.FLOAT, input_shape),
+- *value_infos,
+- make_tensor_value_info("output", TensorProto.FLOAT, expected_shape),
++ make_tensor_value_info("shape", TensorProto.INT64, ()),
++ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
+ ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+ )
+
+- @parameterized.expand(
+- [
+- ("last", 3),
+- ("last_negative", -1),
+- ("out_of_range", 4),
+- ("out_of_range_negative", -5),
+- ]
+- )
+- def test_dft_invalid_axis_opset17(self, _: str, axis: int) -> None:
++ def test_blackmanwindow(self):
+ graph = self._make_graph(
+ [],
+ [
+ make_node(
+ "Constant",
+ [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- (2, 5, 5, 2),
+- np.ones((2, 5, 5, 2), dtype=np.float32).flatten(),
+- ),
++ ["shape"],
++ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+ ),
+- make_node("DFT", ["input", ""], ["output"], onesided=1, axis=axis),
++ make_node("HammingWindow", ["shape"], ["y"]),
+ ],
+ [],
+ )
+- with self.assertRaises(onnx.shape_inference.InferenceError):
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, (2, 5, 5, 2)),
+- make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 17)],
+- )
++ self._assert_inferred(
++ graph,
++ [
++ make_tensor_value_info("shape", TensorProto.INT64, ()),
++ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
++ ],
++ )
+
+- @parameterized.expand(
+- [
+- ("last", 3),
+- ("last_negative", -1),
+- ("out_of_range", 4),
+- ("out_of_range_negative", -5),
+- ]
+- )
+- def test_dft_invalid_axis_opset20(self, _: str, axis: int) -> None:
+ graph = self._make_graph(
+ [],
+ [
+ make_node(
+ "Constant",
+ [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- (2, 5, 5, 2),
+- np.ones((2, 5, 5, 2), dtype=np.float32).flatten(),
+- ),
+- ),
+- make_node(
+- "Constant",
+- [],
+- ["axis"],
+- value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
++ ["shape"],
++ value=make_tensor("shape", TensorProto.INT64, (), (10,)),
+ ),
+- make_node("DFT", ["input", "", "axis"], ["output"]),
++ make_node("HammingWindow", ["shape"], ["y"], periodic=0),
+ ],
+ [],
+ )
+- with self.assertRaises(onnx.shape_inference.InferenceError):
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, (2, 5, 5, 2)),
+- make_tensor_value_info("axis", TensorProto.INT64, ()),
+- make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
+- )
++ self._assert_inferred(
++ graph,
++ [
++ make_tensor_value_info("shape", TensorProto.INT64, ()),
++ make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
++ ],
++ )
+
+ def test_dft_rfft_invalid_complex_input_opset17(self) -> None:
+ """Test that RFFT (onesided=1, inverse=0) rejects complex input"""
+@@ -10011,128 +7806,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
+ )
+
+- @parameterized.expand(
+- [
+- ("real", (2, 5, 5, 1)),
+- ("complex", (2, 5, 5, 2)),
+- ]
+- )
+- def test_dft_dynamic_axis_opset20(self, _: str, shape: tuple[int, ...]) -> None:
+- graph = self._make_graph(
+- [("axis", TensorProto.INT64, ())],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- shape,
+- np.ones(shape, dtype=np.float32).flatten(),
+- ),
+- ),
+- make_node("DFT", ["input", "", "axis"], ["output"]),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, shape),
+- make_tensor_value_info("output", TensorProto.FLOAT, (2, 5, 5, 2)),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
+- )
+-
+- @parameterized.expand(
+- [
+- ("real", (2, 5, 5, 1)),
+- ]
+- )
+- def test_dft_dynamic_axis_onesided_dft_length_opset20(
+- self, _: str, shape: tuple[int, ...]
+- ) -> None:
+- graph = self._make_graph(
+- [("axis", TensorProto.INT64, ())],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- shape,
+- np.ones(shape, dtype=np.float32).flatten(),
+- ),
+- ),
+- make_node(
+- "Constant",
+- [],
+- ["dft_length"],
+- value=make_tensor(
+- "dft_length",
+- TensorProto.INT64,
+- (),
+- np.array([42], dtype=np.int64),
+- ),
+- ),
+- make_node(
+- "DFT", ["input", "dft_length", "axis"], ["output"], onesided=1
+- ),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, shape),
+- make_tensor_value_info("dft_length", TensorProto.INT64, ()),
+- make_tensor_value_info(
+- "output", TensorProto.FLOAT, (None, None, None, 2)
+- ),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
+- )
+-
+- @parameterized.expand(
+- [
+- ("real", (2, 5, 5, 1)),
+- ]
+- )
+- def test_dft_dynamic_axis_onesided_opset20(
+- self, _: str, shape: tuple[int, ...]
+- ) -> None:
+- graph = self._make_graph(
+- [("axis", TensorProto.INT64, ())],
+- [
+- make_node(
+- "Constant",
+- [],
+- ["input"],
+- value=make_tensor(
+- "input",
+- TensorProto.FLOAT,
+- shape,
+- np.ones(shape, dtype=np.float32).flatten(),
+- ),
+- ),
+- make_node("DFT", ["input", "", "axis"], ["output"], onesided=1),
+- ],
+- [],
+- )
+- self._assert_inferred(
+- graph,
+- [
+- make_tensor_value_info("input", TensorProto.FLOAT, shape),
+- make_tensor_value_info(
+- "output", TensorProto.FLOAT, (None, None, None, 2)
+- ),
+- ],
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 20)],
+- )
+-
+ def test_dft_onesided_default_axis_opset17(self) -> None:
+ # Opset 17 sets default axis to be 1.
+ graph = self._make_graph(
+@@ -10591,41 +8264,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ ],
+ )
+
+- @parameterized.expand(
+- [
+- ([1, 2, 3], ["1", "2"]),
+- ([1, 2, 3], None),
+- (None, ["1", "2", "3"]),
+- (None, None),
+- ]
+- )
+- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+- def test_category_mapper_fails_if_invalid_attributes(
+- self, cats_int64s, cats_strings
+- ) -> None:
+- cat = make_node(
+- "CategoryMapper",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- cats_int64s=cats_int64s,
+- cats_strings=cats_strings,
+- )
+- graph = self._make_graph(
+- [("x", TensorProto.INT64, (30, 4, 5))],
+- [cat],
+- [],
+- )
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
+- graph,
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, 1),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+ @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+ def test_tree_ensemble_regressor(self) -> None:
+ tree = make_node(
+@@ -10649,131 +8287,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ ],
+ )
+
+- @parameterized.expand([TensorProto.FLOAT, TensorProto.DOUBLE, TensorProto.FLOAT16])
+- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+- def test_tree_ensemble(self, dtype) -> None:
+- interior_nodes = 5
+- leaves = 9
+- tree = make_node(
+- "TreeEnsemble",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- n_targets=5,
+- nodes_featureids=[0] * interior_nodes,
+- nodes_splits=make_tensor(
+- "nodes_splits",
+- dtype,
+- (interior_nodes,),
+- list(range(interior_nodes)),
+- ),
+- nodes_modes=make_tensor(
+- "nodes_modes",
+- TensorProto.UINT8,
+- (interior_nodes,),
+- [0] * interior_nodes,
+- ),
+- nodes_truenodeids=[0] * interior_nodes,
+- nodes_falsenodeids=[0] * interior_nodes,
+- nodes_trueleafs=[0] * interior_nodes,
+- nodes_falseleafs=[0] * interior_nodes,
+- membership_values=make_tensor(
+- "membership_values",
+- dtype,
+- (7,),
+- [0.0, 0.1, 0.2, np.nan, 0.4, 0.5, 1.0],
+- ),
+- leaf_targetids=[0] * leaves,
+- leaf_weights=make_tensor("leaf_weights", dtype, (leaves,), [1] * leaves),
+- tree_roots=[0],
+- )
+-
+- graph = self._make_graph(
+- [("x", dtype, ("Batch Size", "Features"))],
+- [tree],
+- [],
+- )
+-
+- self._assert_inferred(
+- graph,
+- [make_tensor_value_info("y", dtype, ("Batch Size", 5))],
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, 5),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+- @parameterized.expand(
+- [
+- (
+- [0] * 6,
+- make_tensor("leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9),
+- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
+- ),
+- (
+- [0] * 5,
+- make_tensor("leaf_weights", TensorProto.FLOAT, (9,), [1] * 9),
+- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
+- ),
+- (
+- [0] * 5,
+- make_tensor("leaf_weights", TensorProto.DOUBLE, (18,), [1] * 18),
+- make_tensor("nodes_splits", TensorProto.DOUBLE, (5,), [1] * 5),
+- ),
+- (
+- [0] * 5,
+- make_tensor("leaf_weights", TensorProto.DOUBLE, (9,), [1] * 9),
+- make_tensor("nodes_splits", TensorProto.FLOAT, (5,), [1] * 5),
+- ),
+- ]
+- )
+- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+- def test_tree_ensemble_fails_if_invalid_attributes(
+- self,
+- nodes_truenodeids,
+- leaf_weights,
+- nodes_splits,
+- ) -> None:
+- interior_nodes = 5
+- leaves = 9
+- tree = make_node(
+- "TreeEnsemble",
+- ["x"],
+- ["y"],
+- domain=ONNX_ML_DOMAIN,
+- n_targets=5,
+- nodes_featureids=[0] * interior_nodes,
+- nodes_splits=nodes_splits,
+- nodes_modes=make_tensor(
+- "nodes_modes",
+- TensorProto.UINT8,
+- (interior_nodes,),
+- [0] * interior_nodes,
+- ),
+- nodes_truenodeids=nodes_truenodeids,
+- nodes_falsenodeids=[0] * interior_nodes,
+- nodes_trueleafs=[0] * interior_nodes,
+- nodes_falseleafs=[0] * interior_nodes,
+- leaf_targetids=[0] * leaves,
+- leaf_weights=leaf_weights,
+- tree_roots=[0],
+- )
+-
+- graph = self._make_graph(
+- [("x", TensorProto.DOUBLE, ("Batch Size", "Features"))],
+- [tree],
+- [],
+- )
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
+- graph,
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, 5),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+ @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+ def test_tree_ensemble_classifier(self) -> None:
+ tree = make_node(
+@@ -10878,40 +8391,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ ],
+ )
+
+- @parameterized.expand(
+- [
+- ([1, 2, 3], ["1", "2", "3"]),
+- (None, None),
+- ]
+- )
+- @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+- def test_one_hot_encoder_fails_if_invalid_attributes(
+- self, cats_int64s, cats_strings
+- ) -> None:
+- graph = self._make_graph(
+- [("input", TensorProto.INT64, (2, "N", 3))],
+- [
+- make_node(
+- "OneHotEncoder",
+- ["input"],
+- ["output"],
+- cats_int64s=cats_int64s,
+- cats_strings=cats_strings,
+- domain="ai.onnx.ml",
+- )
+- ],
+- [],
+- )
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
+- graph,
+- opset_imports=[
+- make_opsetid(ONNX_ML_DOMAIN, 1),
+- make_opsetid(ONNX_DOMAIN, 11),
+- ],
+- )
+-
+ @unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
+ def test_zip_map(self) -> None:
+ params = (
+@@ -11066,28 +8545,6 @@ class TestShapeInference(TestShapeInferenceHelper):
+ onnx.checker.check_model(model, full_check=True)
+ onnx.shape_inference.infer_shapes(model)
+
+- @parameterized.expand(all_versions_for("ConstantOfShape"))
+- def test_issue_constantofshape_6135(self, _, version):
+- graph = self._make_graph(
+- [("std.constant", TensorProto.INT64, (1,)), "output"],
+- [
+- make_node(
+- "ConstantOfShape",
+- inputs=["std.constant"],
+- outputs=["output"],
+- name="invalid_node",
+- )
+- ],
+- [],
+- initializer=[make_tensor("std.constant", TensorProto.FLOAT, (1,), (-10,))],
+- )
+- self.assertRaises(
+- onnx.shape_inference.InferenceError,
+- self._inferred,
+- graph,
+- opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
+- )
+-
+ def test_protobuf_default(self) -> None:
+ model_text = """
+ ir_version: 8
+diff --git a/onnx/test/test_external_data.py b/onnx/test/test_external_data.py
+index c0845f96a..eb07f09e8 100644
+--- a/onnx/test/test_external_data.py
++++ b/onnx/test/test_external_data.py
+@@ -14,7 +14,6 @@ import warnings
+ from typing import TYPE_CHECKING, Any
+
+ import numpy as np
+-import parameterized
+
+ import onnx
+ from onnx import (
+@@ -123,616 +122,6 @@ class TestLoadExternalDataBase(unittest.TestCase):
+ checker.check_model(self.model_filename)
+
+
+-@parameterized.parameterized_class(
+- [
+- {"serialization_format": "protobuf"},
+- {"serialization_format": "textproto"},
+- ]
+-)
+-class TestLoadExternalData(TestLoadExternalDataBase):
+- def test_load_external_data(self) -> None:
+- model = onnx.load_model(self.model_filename, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_load_external_data_for_model(self) -> None:
+- model = onnx.load_model(
+- self.model_filename, self.serialization_format, load_external_data=False
+- )
+- load_external_data_for_model(model, self.temp_dir)
+- initializer_tensor = model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_save_external_data(self) -> None:
+- model = onnx.load_model(self.model_filename, self.serialization_format)
+-
+- temp_dir = os.path.join(self.temp_dir, "save_copy")
+- os.mkdir(temp_dir)
+- new_model_filename = os.path.join(temp_dir, "model.onnx")
+- onnx.save_model(model, new_model_filename, self.serialization_format)
+-
+- new_model = onnx.load_model(new_model_filename, self.serialization_format)
+- initializer_tensor = new_model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = new_model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+-
+-@parameterized.parameterized_class(
+- [
+- {"serialization_format": "protobuf"},
+- {"serialization_format": "textproto"},
+- ]
+-)
+-class TestLoadExternalDataSingleFile(TestLoadExternalDataBase):
+- def create_external_data_tensors(
+- self, tensors_data: list[tuple[list[Any], Any]]
+- ) -> list[TensorProto]:
+- tensor_filename = "tensors.bin"
+- tensors = []
+-
+- with open(os.path.join(self.temp_dir, tensor_filename), "ab") as data_file:
+- for value, tensor_name in tensors_data:
+- tensor = from_array(np.array(value))
+- offset = data_file.tell()
+- if offset % 4096 != 0:
+- data_file.write(b"\0" * (4096 - offset % 4096))
+- offset = offset + 4096 - offset % 4096
+-
+- data_file.write(tensor.raw_data)
+- set_external_data(
+- tensor,
+- location=tensor_filename,
+- offset=offset,
+- length=data_file.tell() - offset,
+- )
+- tensor.name = tensor_name
+- tensor.ClearField("raw_data")
+- tensor.data_location = onnx.TensorProto.EXTERNAL
+- tensors.append(tensor)
+-
+- return tensors
+-
+- def test_load_external_single_file_data(self) -> None:
+- model = onnx.load_model(self.model_filename, self.serialization_format)
+-
+- initializer_tensor = model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_save_external_single_file_data(self) -> None:
+- model = onnx.load_model(self.model_filename, self.serialization_format)
+-
+- temp_dir = os.path.join(self.temp_dir, "save_copy")
+- os.mkdir(temp_dir)
+- new_model_filename = os.path.join(temp_dir, "model.onnx")
+- onnx.save_model(model, new_model_filename, self.serialization_format)
+-
+- new_model = onnx.load_model(new_model_filename, self.serialization_format)
+- initializer_tensor = new_model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = new_model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- @parameterized.parameterized.expand(
+- itertools.product(
+- (True, False),
+- )
+- )
+- def test_save_external_invalid_single_file_data_and_check(
+- self, use_absolute_path: bool
+- ) -> None:
+- model = onnx.load_model(self.model_filename, self.serialization_format)
+-
+- model_dir = os.path.join(self.temp_dir, "save_copy")
+- os.mkdir(model_dir)
+-
+- traversal_external_data_dir = os.path.join(
+- self.temp_dir, "invalid_external_data"
+- )
+- os.mkdir(traversal_external_data_dir)
+-
+- if use_absolute_path:
+- traversal_external_data_location = os.path.join(
+- traversal_external_data_dir, "tensors.bin"
+- )
+- else:
+- traversal_external_data_location = "../invalid_external_data/tensors.bin"
+-
+- external_data_dir = os.path.join(self.temp_dir, "external_data")
+- os.mkdir(external_data_dir)
+- new_model_filepath = os.path.join(model_dir, "model.onnx")
+-
+- def convert_model_to_external_data_no_check(model: ModelProto, location: str):
+- for tensor in model.graph.initializer:
+- if tensor.HasField("raw_data"):
+- set_external_data(tensor, location)
+-
+- convert_model_to_external_data_no_check(
+- model,
+- location=traversal_external_data_location,
+- )
+-
+- with self.assertRaises(onnx.checker.ValidationError):
+- onnx.save_model(model, new_model_filepath, self.serialization_format)
+-
+-
+-@parameterized.parameterized_class(
+- [
+- {"serialization_format": "protobuf"},
+- {"serialization_format": "textproto"},
+- ]
+-)
+-class TestSaveAllTensorsAsExternalData(unittest.TestCase):
+- serialization_format: str = "protobuf"
+-
+- def setUp(self) -> None:
+- self._temp_dir_obj = tempfile.TemporaryDirectory()
+- self.temp_dir: str = self._temp_dir_obj.name
+- self.initializer_value = np.arange(6).reshape(3, 2).astype(np.float32) + 512
+- self.attribute_value = np.arange(6).reshape(2, 3).astype(np.float32) + 256
+- self.model = self.create_test_model_proto()
+-
+- def get_temp_model_filename(self):
+- return os.path.join(self.temp_dir, str(uuid.uuid4()) + ".onnx")
+-
+- def create_data_tensors(
+- self, tensors_data: list[tuple[list[Any], Any]]
+- ) -> list[TensorProto]:
+- tensors = []
+- for value, tensor_name in tensors_data:
+- tensor = from_array(np.array(value))
+- tensor.name = tensor_name
+- tensors.append(tensor)
+-
+- return tensors
+-
+- def create_test_model_proto(self) -> ModelProto:
+- tensors = self.create_data_tensors(
+- [
+- (self.attribute_value, "attribute_value"),
+- (self.initializer_value, "input_value"),
+- ]
+- )
+-
+- constant_node = onnx.helper.make_node(
+- "Constant", inputs=[], outputs=["values"], value=tensors[0]
+- )
+-
+- inputs = [
+- helper.make_tensor_value_info(
+- "input_value", onnx.TensorProto.FLOAT, self.initializer_value.shape
+- )
+- ]
+-
+- graph = helper.make_graph(
+- [constant_node],
+- "test_graph",
+- inputs=inputs,
+- outputs=[],
+- initializer=[tensors[1]],
+- )
+- return helper.make_model(graph)
+-
+- @unittest.skipIf(
+- serialization_format != "protobuf",
+- "check_model supports protobuf only when provided as a path",
+- )
+- def test_check_model(self) -> None:
+- checker.check_model(self.model)
+-
+- def test_convert_model_to_external_data_with_size_threshold(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(self.model, size_threshold=1024)
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- self.assertFalse(initializer_tensor.HasField("data_location"))
+-
+- def test_convert_model_to_external_data_without_size_threshold(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+- convert_model_to_external_data(self.model, size_threshold=0)
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- self.assertTrue(initializer_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- def test_convert_model_to_external_data_from_one_file_with_location(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+- external_data_file = str(uuid.uuid4())
+-
+- convert_model_to_external_data(
+- self.model,
+- size_threshold=0,
+- all_tensors_to_one_file=True,
+- location=external_data_file,
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, external_data_file)))
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+-
+- # test convert model from external data
+- convert_model_from_external_data(model)
+- model_file_path = self.get_temp_model_filename()
+- onnx.save_model(model, model_file_path, self.serialization_format)
+- model = onnx.load_model(model_file_path, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- self.assertFalse(len(initializer_tensor.external_data))
+- self.assertEqual(initializer_tensor.data_location, TensorProto.DEFAULT)
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- self.assertFalse(len(attribute_tensor.external_data))
+- self.assertEqual(attribute_tensor.data_location, TensorProto.DEFAULT)
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_convert_model_to_external_data_from_one_file_without_location_uses_model_name(
+- self,
+- ) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(
+- self.model, size_threshold=0, all_tensors_to_one_file=True
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- self.assertTrue(os.path.isfile(model_file_path))
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, model_file_path)))
+-
+- def test_convert_model_to_external_data_one_file_per_tensor_without_attribute(
+- self,
+- ) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(
+- self.model,
+- size_threshold=0,
+- all_tensors_to_one_file=False,
+- convert_attribute=False,
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- self.assertTrue(os.path.isfile(model_file_path))
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
+- self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+-
+- def test_convert_model_to_external_data_one_file_per_tensor_with_attribute(
+- self,
+- ) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(
+- self.model,
+- size_threshold=0,
+- all_tensors_to_one_file=False,
+- convert_attribute=True,
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- self.assertTrue(os.path.isfile(model_file_path))
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+-
+- def test_convert_model_to_external_data_does_not_convert_attribute_values(
+- self,
+- ) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(
+- self.model,
+- size_threshold=0,
+- convert_attribute=False,
+- all_tensors_to_one_file=False,
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- self.assertTrue(os.path.isfile(os.path.join(self.temp_dir, "input_value")))
+- self.assertFalse(os.path.isfile(os.path.join(self.temp_dir, "attribute_value")))
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- self.assertTrue(initializer_tensor.HasField("data_location"))
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- self.assertFalse(attribute_tensor.HasField("data_location"))
+-
+- def test_convert_model_to_external_data_converts_attribute_values(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+-
+- convert_model_to_external_data(
+- self.model, size_threshold=0, convert_attribute=True
+- )
+- onnx.save_model(self.model, model_file_path, self.serialization_format)
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+-
+- initializer_tensor = model.graph.initializer[0]
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+- self.assertTrue(initializer_tensor.HasField("data_location"))
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+- self.assertTrue(attribute_tensor.HasField("data_location"))
+-
+- def test_save_model_does_not_convert_to_external_data_and_saves_the_model(
+- self,
+- ) -> None:
+- model_file_path = self.get_temp_model_filename()
+- onnx.save_model(
+- self.model,
+- model_file_path,
+- self.serialization_format,
+- save_as_external_data=False,
+- )
+- self.assertTrue(os.path.isfile(model_file_path))
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+- initializer_tensor = model.graph.initializer[0]
+- self.assertFalse(initializer_tensor.HasField("data_location"))
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- self.assertFalse(attribute_tensor.HasField("data_location"))
+-
+- def test_save_model_does_convert_and_saves_the_model(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+- onnx.save_model(
+- self.model,
+- model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- all_tensors_to_one_file=True,
+- location=None,
+- size_threshold=0,
+- convert_attribute=False,
+- )
+-
+- model = onnx.load_model(model_file_path, self.serialization_format)
+-
+- initializer_tensor = model.graph.initializer[0]
+- self.assertTrue(initializer_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- self.assertFalse(attribute_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_save_model_without_loading_external_data(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+- onnx.save_model(
+- self.model,
+- model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- location=None,
+- size_threshold=0,
+- convert_attribute=False,
+- )
+- # Save without load_external_data
+- model = onnx.load_model(
+- model_file_path, self.serialization_format, load_external_data=False
+- )
+- onnx.save_model(
+- model,
+- model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- location=None,
+- size_threshold=0,
+- convert_attribute=False,
+- )
+- # Load the saved model again; Only works if the saved path is under the same directory
+- model = onnx.load_model(model_file_path, self.serialization_format)
+-
+- initializer_tensor = model.graph.initializer[0]
+- self.assertTrue(initializer_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(initializer_tensor), self.initializer_value)
+-
+- attribute_tensor = model.graph.node[0].attribute[0].t
+- self.assertFalse(attribute_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(attribute_tensor), self.attribute_value)
+-
+- def test_save_model_with_existing_raw_data_should_override(self) -> None:
+- model_file_path = self.get_temp_model_filename()
+- original_raw_data = self.model.graph.initializer[0].raw_data
+- onnx.save_model(
+- self.model,
+- model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- size_threshold=0,
+- )
+- self.assertTrue(os.path.isfile(model_file_path))
+-
+- model = onnx.load_model(
+- model_file_path, self.serialization_format, load_external_data=False
+- )
+- initializer_tensor = model.graph.initializer[0]
+- initializer_tensor.raw_data = b"dummpy_raw_data"
+- # If raw_data and external tensor exist at the same time, override existing raw_data
+- load_external_data_for_tensor(initializer_tensor, self.temp_dir)
+- self.assertEqual(initializer_tensor.raw_data, original_raw_data)
+-
+-
+-@parameterized.parameterized_class(
+- [
+- {"serialization_format": "protobuf"},
+- {"serialization_format": "textproto"},
+- ]
+-)
+-class TestExternalDataToArray(unittest.TestCase):
+- serialization_format: str = "protobuf"
+-
+- def setUp(self) -> None:
+- self._temp_dir_obj = tempfile.TemporaryDirectory()
+- self.temp_dir: str = self._temp_dir_obj.name
+- self._model_file_path: str = os.path.join(self.temp_dir, "model.onnx")
+- self.large_data = np.random.rand(10, 60, 100).astype(np.float32)
+- self.small_data = (200, 300)
+- self.model = self.create_test_model()
+-
+- @property
+- def model_file_path(self):
+- return self._model_file_path
+-
+- def tearDown(self) -> None:
+- self._temp_dir_obj.cleanup()
+-
+- def create_test_model(self) -> ModelProto:
+- X = helper.make_tensor_value_info("X", TensorProto.FLOAT, self.large_data.shape)
+- input_init = helper.make_tensor(
+- name="X",
+- data_type=TensorProto.FLOAT,
+- dims=self.large_data.shape,
+- vals=onnx.numpy_helper.tobytes_little_endian(self.large_data),
+- raw=True,
+- )
+-
+- shape_data = np.array(self.small_data, np.int64)
+- shape_init = helper.make_tensor(
+- name="Shape",
+- data_type=TensorProto.INT64,
+- dims=shape_data.shape,
+- vals=onnx.numpy_helper.tobytes_little_endian(shape_data),
+- raw=True,
+- )
+- C = helper.make_tensor_value_info("C", TensorProto.INT64, self.small_data)
+-
+- reshape = onnx.helper.make_node(
+- "Reshape",
+- inputs=["X", "Shape"],
+- outputs=["Y"],
+- )
+- cast = onnx.helper.make_node(
+- "Cast", inputs=["Y"], outputs=["C"], to=TensorProto.INT64
+- )
+-
+- graph_def = helper.make_graph(
+- [reshape, cast],
+- "test-model",
+- [X],
+- [C],
+- initializer=[input_init, shape_init],
+- )
+- return helper.make_model(graph_def, producer_name="onnx-example")
+-
+- @unittest.skipIf(
+- serialization_format != "protobuf",
+- "check_model supports protobuf only when provided as a path",
+- )
+- def test_check_model(self) -> None:
+- checker.check_model(self.model)
+-
+- def test_reshape_inference_with_external_data_fail(self) -> None:
+- onnx.save_model(
+- self.model,
+- self.model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- all_tensors_to_one_file=False,
+- size_threshold=0,
+- )
+- model_without_external_data = onnx.load(
+- self.model_file_path, self.serialization_format, load_external_data=False
+- )
+- # Shape inference of Reshape uses ParseData
+- # ParseData cannot handle external data and should throw the error as follows:
+- # Cannot parse data from external tensors. Please load external data into raw data for tensor: Shape
+- self.assertRaises(
+- shape_inference.InferenceError,
+- shape_inference.infer_shapes,
+- model_without_external_data,
+- strict_mode=True,
+- )
+-
+- def test_to_array_with_external_data(self) -> None:
+- onnx.save_model(
+- self.model,
+- self.model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- all_tensors_to_one_file=False,
+- size_threshold=0,
+- )
+- # raw_data of external tensor is not loaded
+- model = onnx.load(
+- self.model_file_path, self.serialization_format, load_external_data=False
+- )
+- # Specify self.temp_dir to load external tensor
+- loaded_large_data = to_array(model.graph.initializer[0], self.temp_dir)
+- np.testing.assert_allclose(loaded_large_data, self.large_data)
+-
+- def test_save_model_with_external_data_multiple_times(self) -> None:
+- # Test onnx.save should respectively handle typical tensor and external tensor properly
+- # 1st save: save two tensors which have raw_data
+- # Only w_large will be stored as external tensors since it's larger than 1024
+- onnx.save_model(
+- self.model,
+- self.model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- all_tensors_to_one_file=False,
+- location=None,
+- size_threshold=1024,
+- convert_attribute=True,
+- )
+- model_without_loading_external = onnx.load(
+- self.model_file_path, self.serialization_format, load_external_data=False
+- )
+- large_input_tensor = model_without_loading_external.graph.initializer[0]
+- self.assertTrue(large_input_tensor.HasField("data_location"))
+- np.testing.assert_allclose(
+- to_array(large_input_tensor, self.temp_dir), self.large_data
+- )
+-
+- small_shape_tensor = model_without_loading_external.graph.initializer[1]
+- self.assertTrue(not small_shape_tensor.HasField("data_location"))
+- np.testing.assert_allclose(to_array(small_shape_tensor), self.small_data)
+-
+- # 2nd save: one tensor has raw_data (small); one external tensor (large)
+- # Save them both as external tensors this time
+- onnx.save_model(
+- model_without_loading_external,
+- self.model_file_path,
+- self.serialization_format,
+- save_as_external_data=True,
+- all_tensors_to_one_file=False,
+- location=None,
+- size_threshold=0,
+- convert_attribute=True,
+- )
+-
+- model_without_loading_external = onnx.load(
+- self.model_file_path, self.serialization_format, load_external_data=False
+- )
+- large_input_tensor = model_without_loading_external.graph.initializer[0]
+- self.assertTrue(large_input_tensor.HasField("data_location"))
+- np.testing.assert_allclose(
+- to_array(large_input_tensor, self.temp_dir), self.large_data
+- )
+-
+- small_shape_tensor = model_without_loading_external.graph.initializer[1]
+- self.assertTrue(small_shape_tensor.HasField("data_location"))
+- np.testing.assert_allclose(
+- to_array(small_shape_tensor, self.temp_dir), self.small_data
+- )
+-
+-
+ class TestNotAllowToLoadExternalDataOutsideModelDirectory(TestLoadExternalDataBase):
+ """Essential test to check that onnx (validate) C++ code will not allow to load external_data outside the model
+ directory.
+@@ -797,17 +186,6 @@ class TestNotAllowToLoadExternalDataOutsideModelDirectoryOnWindows(
+ checker.check_model(self.model_filename)
+
+
+-class TestSaveAllTensorsAsExternalDataWithPath(TestSaveAllTensorsAsExternalData):
+- def get_temp_model_filename(self) -> pathlib.Path:
+- return pathlib.Path(super().get_temp_model_filename())
+-
+-
+-class TestExternalDataToArrayWithPath(TestExternalDataToArray):
+- @property
+- def model_file_path(self) -> pathlib.Path:
+- return pathlib.Path(self._model_file_path)
+-
+-
+ class TestFunctionsAndSubGraphs(unittest.TestCase):
+ def setUp(self) -> None:
+ self._temp_dir_obj = tempfile.TemporaryDirectory()
+diff --git a/onnx/test/version_converter/automatic_downgrade_test.py b/onnx/test/version_converter/automatic_downgrade_test.py
+index a94aea1a9..f639d44f8 100644
+--- a/onnx/test/version_converter/automatic_downgrade_test.py
++++ b/onnx/test/version_converter/automatic_downgrade_test.py
+@@ -7,7 +7,6 @@ import unittest
+
+ import automatic_conversion_test_base
+ import numpy as np
+-import parameterized
+
+ import onnx
+ from onnx import helper
+@@ -22,35 +21,6 @@ class TestAutomaticDowngrade(automatic_conversion_test_base.TestAutomaticConvers
+ def _test_op_downgrade(self, op: str, *args, **kwargs):
+ self._test_op_conversion(op, *args, **kwargs, is_upgrade=False)
+
+- @parameterized.parameterized.expand(
+- [
+- "ReduceL1",
+- "ReduceL2",
+- "ReduceLogSum",
+- "ReduceLogSumExp",
+- "ReduceMean",
+- "ReduceMax",
+- "ReduceMin",
+- "ReduceProd",
+- "ReduceSum",
+- "ReduceSumSquare",
+- ]
+- )
+- def test_reduce_ops(self, op) -> None:
+- # TODO: need to add test cases for missing axes input which depends on this pr:
+- # https://github.com/onnx/onnx/pull/5613
+- axes = helper.make_tensor(
+- "b", onnx.TensorProto.INT64, dims=[3], vals=np.array([0, 1, 2])
+- )
+- self._test_op_downgrade(
+- op,
+- from_opset=13,
+- input_shapes=[[3, 4, 5], [3]],
+- output_shapes=[[1, 1, 1]],
+- input_types=[onnx.TensorProto.FLOAT, onnx.TensorProto.INT64],
+- initializer=[axes],
+- )
+-
+ def test_dft20_no_axis(self) -> None:
+ self._test_model_conversion(
+ to_opset=19,
+diff --git a/onnx/test/version_converter_test.py b/onnx/test/version_converter_test.py
+index a75edcde7..fd057518f 100644
+--- a/onnx/test/version_converter_test.py
++++ b/onnx/test/version_converter_test.py
+@@ -10,7 +10,6 @@ import tempfile
+ import unittest
+
+ import numpy as np
+-import parameterized
+
+ import onnx.version_converter
+ from onnx import (
+@@ -2115,169 +2114,6 @@ class TestVersionConverter(unittest.TestCase):
+ assert converted_model.graph.node[0].attribute[0].i == 2
+ assert converted_model.opset_import[0].version == 12
+
+- @parameterized.parameterized.expand(
+- [
+- ("per_tensor", (16, 3), (1,), None, None, None, TensorProto.INT8, True),
+- (
+- "per_axis_none_block_shape",
+- (16, 3),
+- (16,),
+- 1,
+- None,
+- None,
+- TensorProto.INT8,
+- True,
+- ),
+- (
+- "per_axis_zero_block_shape",
+- (16, 3),
+- (16,),
+- 1,
+- 0,
+- None,
+- TensorProto.INT8,
+- True,
+- ),
+- (
+- "per_tensor_positive_block_shape",
+- (16, 3),
+- (1,),
+- 1,
+- 2,
+- None,
+- TensorProto.INT8,
+- False,
+- ),
+- (
+- "per_axis_positive_block_shape",
+- (16, 3),
+- (16,),
+- 1,
+- 2,
+- None,
+- TensorProto.INT8,
+- False,
+- ),
+- ("blocked_2d", (16, 3), (4, 3), 0, 4, None, TensorProto.INT8, False),
+- ("blocked_3d", (4, 3, 32), (4, 3, 8), 2, 4, None, TensorProto.INT8, False),
+- (
+- "per_axis_output_dtype",
+- (16, 3),
+- (16,),
+- 1,
+- None,
+- TensorProto.FLOAT8E4M3FN,
+- None,
+- False,
+- ),
+- (
+- "per_axis_unsupported_type",
+- (16, 3),
+- (16,),
+- 1,
+- None,
+- None,
+- TensorProto.UINT16,
+- False,
+- ),
+- ]
+- )
+- def test_quantize_21_20(
+- self,
+- _: str,
+- x_shape: tuple[int, ...],
+- scale_shape: tuple[int, ...],
+- axis: int,
+- block_size: int,
+- output_dtype: int | None,
+- zero_point_dtype: int | None,
+- compatible: bool,
+- ) -> None:
+- def test(
+- input_shape, scale_shape, axis, block_size, output_dtype, zero_point_dtype
+- ) -> None:
+- nodes = [
+- helper.make_node(
+- "QuantizeLinear",
+- ["X", "S"],
+- ["Y"],
+- axis=axis,
+- block_size=block_size,
+- output_dtype=output_dtype,
+- )
+- ]
+- inputs = [
+- helper.make_tensor_value_info("X", TensorProto.FLOAT, input_shape),
+- helper.make_tensor_value_info("S", TensorProto.FLOAT, scale_shape),
+- ]
+- if zero_point_dtype:
+- inputs.append(
+- helper.make_tensor_value_info("ZP", zero_point_dtype, scale_shape)
+- )
+- nodes[0].input.append("ZP")
+- output_type_ = output_dtype or zero_point_dtype
+- graph = helper.make_graph(
+- nodes,
+- "test",
+- inputs,
+- [helper.make_tensor_value_info("Y", output_type_, input_shape)],
+- )
+- _ = self._converted(graph, helper.make_operatorsetid("", 21), 20)
+-
+- context_manager = (
+- contextlib.nullcontext() if compatible else self.assertRaises(RuntimeError)
+- )
+- with context_manager:
+- test(x_shape, scale_shape, axis, block_size, output_dtype, zero_point_dtype)
+-
+- @parameterized.parameterized.expand(
+- [
+- ("per_tensor", (16, 3), (1,), None, None, True),
+- ("per_axis_none_block_shape", (16, 3), (16,), 1, None, True),
+- ("per_axis_zero_block_shape", (16, 3), (16,), 1, 0, True),
+- ("per_tensor_positive_block_shape", (16, 3), (1,), 1, 2, False),
+- ("per_axis_positive_block_shape", (16, 3), (16,), 1, 2, False),
+- ("blocked_2d", (16, 3), (4, 3), 0, 4, False),
+- ("blocked_3d", (4, 3, 32), (4, 3, 8), 2, 4, False),
+- ]
+- )
+- def test_dequantize_21_20(
+- self,
+- _: str,
+- y_shape: tuple[int, ...],
+- scale_shape: tuple[int, ...],
+- axis: int,
+- block_size: int,
+- compatible: bool,
+- ) -> None:
+- def test(input_shape, scale_shape, axis, block_size) -> None:
+- nodes = [
+- helper.make_node(
+- "DequantizeLinear",
+- ["X", "S", "ZP"],
+- ["Y"],
+- axis=axis,
+- block_size=block_size,
+- )
+- ]
+- graph = helper.make_graph(
+- nodes,
+- "test",
+- [
+- helper.make_tensor_value_info("X", TensorProto.INT8, input_shape),
+- helper.make_tensor_value_info("S", TensorProto.FLOAT, scale_shape),
+- helper.make_tensor_value_info("ZP", TensorProto.INT8, scale_shape),
+- ],
+- [helper.make_tensor_value_info("Y", TensorProto.FLOAT, input_shape)],
+- )
+- _ = self._converted(graph, helper.make_operatorsetid("", 21), 20)
+-
+- context_manager = (
+- contextlib.nullcontext() if compatible else self.assertRaises(RuntimeError)
+- )
+- with context_manager:
+- test(y_shape, scale_shape, axis, block_size)
+-
+ def test_external_data_version_conversion(self) -> None:
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create a model with external data
+@@ -2413,76 +2249,6 @@ class TestVersionConverter(unittest.TestCase):
+ [helper.make_tensor_value_info("out", dtype, data_s)],
+ )
+
+- # Scatter 16 -> 15: TypeRestriction (bfloat16) + RemoveAttribute (reduction)
+- @parameterized.parameterized.expand([("ScatterElements",), ("ScatterND",)])
+- def test_scatter_16_15_success(self, op_name: str) -> None:
+- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, "none")
+- converted = self._converted(graph, helper.make_operatorsetid("", 16), 15)
+- assert converted.opset_import[0].version == 15
+-
+- @parameterized.parameterized.expand([("ScatterElements",), ("ScatterND",)])
+- def test_scatter_16_15_bfloat16_fails(self, op_name: str) -> None:
+- def test() -> None:
+- graph = self._make_scatter_graph(op_name, TensorProto.BFLOAT16)
+- self._converted(graph, helper.make_operatorsetid("", 16), 15)
+-
+- self.assertRaises(RuntimeError, test)
+-
+- # Opset 16 added reduction 'add' and 'mul'; 16 -> 15 only allows 'none'
+- @parameterized.parameterized.expand(
+- [
+- ("ScatterElements", "add"),
+- ("ScatterElements", "mul"),
+- ("ScatterND", "add"),
+- ("ScatterND", "mul"),
+- ]
+- )
+- def test_scatter_16_15_reduction_add_mul_fails(
+- self, op_name: str, reduction: str
+- ) -> None:
+- def test() -> None:
+- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
+- self._converted(graph, helper.make_operatorsetid("", 16), 15)
+-
+- self.assertRaises(RuntimeError, test)
+-
+- # Scatter 18 -> 17: reject reduction "max" / "min"
+- @parameterized.parameterized.expand(
+- [
+- ("ScatterElements", "max"),
+- ("ScatterElements", "min"),
+- ("ScatterND", "max"),
+- ("ScatterND", "min"),
+- ]
+- )
+- def test_scatter_18_17_reduction_max_min_fails(
+- self, op_name: str, reduction: str
+- ) -> None:
+- def test() -> None:
+- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
+- self._converted(graph, helper.make_operatorsetid("", 18), 17)
+-
+- self.assertRaises(RuntimeError, test)
+-
+- @parameterized.parameterized.expand(
+- [
+- ("ScatterElements", None),
+- ("ScatterElements", "none"),
+- ("ScatterElements", "add"),
+- ("ScatterElements", "mul"),
+- ("ScatterND", None),
+- ("ScatterND", "none"),
+- ("ScatterND", "add"),
+- ("ScatterND", "mul"),
+- ]
+- )
+- def test_scatter_18_17_allowed_reductions_success(
+- self, op_name: str, reduction: str | None
+- ) -> None:
+- graph = self._make_scatter_graph(op_name, TensorProto.FLOAT, reduction)
+- converted = self._converted(graph, helper.make_operatorsetid("", 18), 17)
+- checker.check_model(converted)
+-
+
+ if __name__ == "__main__":
+ unittest.main()
+diff --git a/requirements-dev.txt b/requirements-dev.txt
+index e36ecd6d9..7e4a0ccd4 100644
+--- a/requirements-dev.txt
++++ b/requirements-dev.txt
+@@ -1,6 +1,5 @@
+ build
+ numpy
+-parameterized
+ protobuf
+ pytest
+ pytest-cov
+--
+2.54.0
+
diff --git a/0007-Add-fixes-for-use-with-onnxruntime.patch b/0007-Add-fixes-for-use-with-onnxruntime.patch
new file mode 100644
index 0000000..7388b99
--- /dev/null
+++ b/0007-Add-fixes-for-use-with-onnxruntime.patch
@@ -0,0 +1,72 @@
+From 8972db90c348367289ce764000607fa7a82336ba Mon Sep 17 00:00:00 2001
+From: Diego Herrera <dherrera@redhat.com>
+Date: Wed, 1 Jul 2026 00:20:17 -0400
+Subject: [PATCH 7/7] Add fixes for use with onnxruntime
+
+
+diff --git a/onnx/defs/schema.cc b/onnx/defs/schema.cc
+index 1eec34eb2..2973951c3 100644
+--- a/onnx/defs/schema.cc
++++ b/onnx/defs/schema.cc
+@@ -27,8 +27,19 @@
+ #include "onnx/common/assertions.h"
+ #endif
+ #include "onnx/defs/parser.h"
++#include "onnx/onnxruntime_fix.h"
+
+ namespace ONNX_NAMESPACE {
++
++bool ONNXRuntimeFix::_static_registration_disabled = false;
++bool ONNXRuntimeFix::isStaticRegistrationDisabled() {
++ return _static_registration_disabled;
++}
++
++void ONNXRuntimeFix::disableStaticRegistration() {
++ _static_registration_disabled = true;
++}
++
+ // -1 means ONNX schema hasn't been loaded yet
+ // 0 means all versions of ONNX schema have been loaded
+ // Other positive integer means the ONNX schemas for the specified version have been loaded
+@@ -1777,6 +1788,9 @@ OpName_Domain_Version_Schema_Map& OpSchemaRegistry::map() {
+ class SchemasRegisterer {
+ public:
+ SchemasRegisterer() {
++ // Check if static registration is actually disabled
++ if(ONNXRuntimeFix::isStaticRegistrationDisabled()) return;
++
+ // In debug builds, the number of schema registered in this constructor
+ // is compared against the number of calls to schema registration macros.
+ #ifndef NDEBUG
+@@ -1845,7 +1859,7 @@ bool IsOnnxStaticRegistrationDisabled() {
+ #ifdef __ONNX_DISABLE_STATIC_REGISTRATION
+ return true;
+ #else
+- return false;
++ return ONNXRuntimeFix::isStaticRegistrationDisabled();
+ #endif
+ }
+
+diff --git a/onnx/onnxruntime_fix.h b/onnx/onnxruntime_fix.h
+new file mode 100644
+index 000000000..2495b9310
+--- /dev/null
++++ b/onnx/onnxruntime_fix.h
+@@ -0,0 +1,14 @@
++#pragma once
++
++namespace ONNX_NAMESPACE {
++
++class ONNXRuntimeFix {
++public:
++ static bool isStaticRegistrationDisabled();
++ static void disableStaticRegistration();
++
++private:
++ static bool _static_registration_disabled;
++};
++
++} // namespace ONNX_NAMESPACE
+--
+2.54.0
+
diff --git a/onnx.spec b/onnx.spec
index 08b73ea..4d11ce3 100644
--- a/onnx.spec
+++ b/onnx.spec
@@ -9,9 +9,18 @@ License: Apache-2.0
URL: https://github.com/onnx/onnx
Source0: https://github.com/onnx/onnx/archive/v%{version}/%{name}-%{version}.tar.gz
# Build shared libraries and fix install location
-Patch: 0000-Build-shared-libraries-and-fix-install-location.patch
+Patch: 0001-Build-shared-libraries.patch
+Patch: 0002-Fix-install-location.patch
+# Latest nanobind uses different argument format
+Patch: 0003-Fix-nanobind-arguments.patch
+# Let pyproject_wheel use binaries from cmake_build
+Patch: 0004-Let-pyproject_wheel-use-binaries-from-cmake_build.patch
+# Fixes protobuf conflicts when using both python-onnx and python-onnxruntime
+Patch: 0005-Python-binary-should-link-to-system-onnx.patch
# Remove obsoleted testing dependency
-Patch: 0002-Remove-python-parameterized-dependency.patch
+Patch: 0006-Remove-python-parameterized-dependency.patch
+# Add fixes for use with onnxruntime
+Patch: 0007-Add-fixes-for-use-with-onnxruntime.patch
%if %{undefined fc40} && %{undefined fc41}
# https://fedoraproject.org/wiki/Changes/EncourageI686LeafRemoval
@@ -73,7 +82,7 @@ export VPATH_BUILDDIR=%{_vpath_builddir}
-DPY_EXT_SUFFIX=%{python3_ext_suffix} \
-DPY_SITEARCH=%{python3_sitearch} \
-DCMAKE_SKIP_RPATH:BOOL=ON \
- -DONNX_DISABLE_STATIC_REGISTRATION=ON
+ -DONNX_DISABLE_STATIC_REGISTRATION=OFF
# Build
%cmake_build
reply other threads:[~2026-07-01 15:50 UTC|newest]
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