add double type in test, add type check in def

Author: mindest

tests/test_backend.py

@@ -2080,15 +2080,17 @@ def test_space_to_batchnd(self):
 
     @check_opset_min_version(10, "is_inf")
     def test_isinf(self):
-        x_val1 = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
-        x_val2 = np.array([np.inf, np.inf, np.inf, np.inf], dtype=np.float32).reshape((2, 2))
-        x_val3 = np.array([1.0, np.inf, -3.0, np.inf], dtype=np.float32).reshape((2, 2))
-        for x_val in [x_val1, x_val2, x_val3]:
-            x = tf.placeholder(tf.float32, x_val.shape, name=_TFINPUT)
-            x_ = tf.is_inf(x)
-            _ = tf.identity(x_, name=_TFOUTPUT)
-            self._run_test_case([_OUTPUT], {_INPUT: x_val})
-            tf.reset_default_graph()
+        x_types = [np.float32, np.float64]
+        for x_type in x_types:
+            x_val1 = np.array([1.0, -2.0, 3.0, -4.0], dtype=x_type).reshape((2, 2))
+            x_val2 = np.array([np.inf, np.inf, np.inf, np.inf], dtype=x_type).reshape((1, 4))
+            x_val3 = np.array([1.0, np.inf, -3.0, np.inf, 5.0, np.inf, -7.0, np.inf, 9.0], dtype=x_type).reshape((3, 3))
+            for x_val in [x_val1, x_val2, x_val3]:
+                x = tf.placeholder(x_type, x_val.shape, name=_TFINPUT)
+                x_ = tf.is_inf(x)
+                _ = tf.identity(x_, name=_TFOUTPUT)
+                self._run_test_case([_OUTPUT], {_INPUT: x_val})
+                tf.reset_default_graph()
 
 if __name__ == '__main__':
     unittest_main()

tf2onnx/onnx_opset/tensor.py

@@ -887,4 +887,7 @@ def version_4(cls, ctx, node, **kwargs):
 class IsInf:
     @classmethod
     def version_10(cls, ctx, node, **kwargs):
-        pass
+        node_dtype = ctx.get_dtype(node.input[0])
+        utils.make_sure(node_dtype, "Dtype of {} is None".format(node.name))
+        if node_dtype not in [onnx_pb.TensorProto.FLOAT, onnx_pb.TensorProto.DOUBLE]:
+            raise ValueError("dtype " + str(node_dtype) + " is not supported in onnx for now")