implement isinf for opset 10

Author: mindest

tests/test_backend.py

@@ -2078,5 +2078,17 @@ def test_space_to_batchnd(self):
         _ = tf.space_to_batch_nd(input_x, block_size, pad, name=_TFOUTPUT)
         self._run_test_case([_OUTPUT], {_INPUT: input_val})
 
+    @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()
+
 if __name__ == '__main__':
     unittest_main()

tf2onnx/onnx_opset/tensor.py

@@ -881,3 +881,10 @@ def version_4(cls, ctx, node, **kwargs):
         reorganize_node = ctx.make_node(node.type, trans1.output, attr={"blocksize": blocksize[0]})
         ctx.make_node("Transpose", reorganize_node.output, {"perm": [1, 2, 3, 0]}, name=node.name, outputs=node.output,
                       shapes=shapes, dtypes=dtypes)
+
+
+@tf_op("IsInf", onnx_op="IsInf")
+class IsInf:
+    @classmethod
+    def version_10(cls, ctx, node, **kwargs):
+        pass