Update tests

Signed-off-by: Justin Chu <justinchu@microsoft.com>

onnx/backend/test/case/node/__init__.py

@@ -9,6 +9,7 @@
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
+from typing_extensions import Literal
 
 import onnx
 from onnx.backend.test.case.test_case import TestCase
@@ -236,7 +237,7 @@ def _make_test_model_gen_version(graph: GraphProto, **kwargs: Any) -> ModelProto
 # the latest opset vesion that supports before targeted opset version
 def expect(
     node_op: onnx.NodeProto,
-    inputs: Sequence[Union[np.ndarray, TensorProto]],
+    inputs: Sequence[Union[np.ndarray, TensorProto, Literal[""]]],
     outputs: Sequence[Union[np.ndarray, TensorProto]],
     name: str,
     **kwargs: Any,

onnx/backend/test/case/node/dft.py

@@ -11,22 +11,34 @@
 
 class DFT(Base):
     @staticmethod
-    def export() -> None:
+    def export_opset19() -> None:
         node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1)
         x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
         y = np.fft.fft(x, axis=0)
 
         x = x.reshape(1, 10, 10, 1)
         y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
-        expect(node, inputs=[x], outputs=[y], name="test_dft")
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_dft_opset19",
+            opset_imports=[onnx.helper.make_opsetid("", 19)],
+        )
 
         node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2)
         x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
         y = np.fft.fft(x, axis=1)
 
         x = x.reshape(1, 10, 10, 1)
         y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
-        expect(node, inputs=[x], outputs=[y], name="test_dft_axis")
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_dft_axis_opset19",
+            opset_imports=[onnx.helper.make_opsetid("", 19)],
+        )
 
         node = onnx.helper.make_node(
             "DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1
@@ -39,4 +51,39 @@ def export() -> None:
 
         x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
         y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
-        expect(node, inputs=[x], outputs=[y], name="test_dft_inverse")
+        expect(
+            node,
+            inputs=[x],
+            outputs=[y],
+            name="test_dft_inverse_opset19",
+            opset_imports=[onnx.helper.make_opsetid("", 19)],
+        )
+
+    @staticmethod
+    def export() -> None:
+        node = onnx.helper.make_node("DFT", inputs=["x", "", "axis"], outputs=["y"])
+        x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
+        axis = np.array(1, dtype=np.int64)
+        y = np.fft.fft(x, axis=0)
+
+        x = x.reshape(1, 10, 10, 1)
+        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
+        expect(node, inputs=[x, "", axis], outputs=[y], name="test_dft")
+
+        node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"])
+        x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
+        axis = np.array(2, dtype=np.int64)
+        y = np.fft.fft(x, axis=1)
+
+        x = x.reshape(1, 10, 10, 1)
+        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
+        expect(node, inputs=[x, "", axis], outputs=[y], name="test_dft_axis")
+
+        node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], inverse=1)
+        x = np.arange(0, 100, dtype=np.complex64).reshape(10, 10)
+        axis = np.array(1, dtype=np.int64)
+        y = np.fft.ifft(x, axis=0)
+
+        x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
+        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
+        expect(node, inputs=[x, "", axis], outputs=[y], name="test_dft_inverse")

onnx/defs/math/old.cc

@@ -2966,7 +2966,7 @@ ONNX_OPERATOR_SET_SCHEMA(
         .Attr(
             "onesided",
             "If onesided is 1, only values for w in [0, 1, 2, ..., floor(n_fft/2) + 1] are returned because "
-            "the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. "
+            "the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] =X[m, n_fft-w]*. "
             "Note if the input or window tensors are complex, then onesided output is not possible. "
             "Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT). "
             "When invoked with real or complex valued input, the default value is 0. "

onnx/test/shape_inference_test.py

@@ -8552,7 +8552,7 @@ def test_dft_reals(
         if version < 20:
             if axis is not None:
                 attributes["axis"] = axis
-            nodes = [make_node("DFT", ["input", ""], ["output"], **attributes)]
+            nodes = [make_node("DFT", ["input", ""], ["output"], **attributes)]  # type: ignore[arg-type]
             value_infos = []
         else:
             assert version >= 20
@@ -8564,12 +8564,12 @@ def test_dft_reals(
                         ["axis"],
                         value=make_tensor("axis", TensorProto.INT64, (), (axis,)),
                     ),
-                    make_node("DFT", ["input", "", "axis"], ["output"], **attributes),
+                    make_node("DFT", ["input", "", "axis"], ["output"], **attributes),  # type: ignore[arg-type]
                 ]
                 value_infos = [make_tensor_value_info("axis", TensorProto.INT64, ())]
             else:
                 nodes = [
-                    make_node("DFT", ["input", "", ""], ["output"], **attributes),
+                    make_node("DFT", ["input", "", ""], ["output"], **attributes),  # type: ignore[arg-type]
                 ]
                 value_infos = []