Fix unsupported ops TF 2.14.0: OnesLike (#2270)

* add OnesLike handler
* add tests for OnesLike

---------

Signed-off-by: Alexander Gerstenberger <agerstenberger@apptek.com>
Co-authored-by: Alexander Gerstenberger <agerstenberger@apptek.com>
Co-authored-by: Jay Zhang <36183870+fatcat-z@users.noreply.github.com>

tests/test_backend.py

@@ -4113,6 +4113,16 @@ def func(x, y):
         self._run_test_case(func, [_OUTPUT], {_INPUT: input_x.astype(np.int32), _INPUT1: input_y}, as_session=True,
                             graph_validator=lambda g: check_op_count(g, "ConstantOfShape", 1, disabled=False))
 
+    def test_ones_like(self):
+        input_x = np.random.random_sample([3, 16, 16]).astype(np.float32)
+        input_y = np.array([16, 16, 3]).astype(np.int64)
+
+        def func(x, y):
+            z = tf.reshape(x, y)
+            return tf.ones_like(z, name=_TFOUTPUT)
+
+        self._run_test_case(func, [_OUTPUT], {_INPUT: input_x, _INPUT1: input_y})
+
     @check_opset_min_version(9, "is_nan")
     def test_isnan(self):
         # only compatible with dtype `float32`

tf2onnx/onnx_opset/generator.py

@@ -227,31 +227,50 @@ def version_7(cls, ctx, node, **kwargs):
         ctx.remove_input(node, node.input[1], 1)
 
 
+def _const_like_version_1(ctx, node, value):
+    shapes = node.output_shapes
+    dtypes = node.output_dtypes
+    ctx.remove_node(node.name)
+    casted_input = ctx.make_node("Cast", node.input, attr={'to': onnx_pb.TensorProto.INT64})
+    const_value = ctx.make_const(utils.make_name("value"), np.array(value).astype(np.int64))
+    mul_node = ctx.make_node('Mul', inputs=[casted_input.output[0], const_value.output[0]])
+    ctx.make_node("Cast", inputs=[mul_node.output[0]],
+                  attr={'to': dtypes[0]},
+                  name=node.name, outputs=node.output,
+                  shapes=shapes, dtypes=dtypes)
+
+
+def _const_like_version_9(ctx, node, value):
+    dtypes = node.output_dtypes
+    ctx.remove_node(node.name)
+    shape = ctx.make_node("Shape", node.input).output[0]
+    value_tensor = helper.make_tensor("value", dtypes[0], [1], vals=[value])
+    ctx.make_node("ConstantOfShape", inputs=[shape],
+                  attr={'value': value_tensor},
+                  name=node.name, outputs=node.output,
+                  dtypes=dtypes)
+
+
 @tf_op("ZerosLike")
 class ZerosLike:
     @classmethod
     def version_1(cls, ctx, node, **kwargs):
-        shapes = node.output_shapes
-        dtypes = node.output_dtypes
-        ctx.remove_node(node.name)
-        casted_input = ctx.make_node("Cast", node.input, attr={'to': onnx_pb.TensorProto.INT64})
-        const_zero = ctx.make_const(utils.make_name("zero"), np.array(0).astype(np.int64))
-        mul_node = ctx.make_node('Mul', inputs=[casted_input.output[0], const_zero.output[0]])
-        ctx.make_node("Cast", inputs=[mul_node.output[0]],
-                      attr={'to': dtypes[0]},
-                      name=node.name, outputs=node.output,
-                      shapes=shapes, dtypes=dtypes)
+        _const_like_version_1(ctx, node, 0)
 
     @classmethod
     def version_9(cls, ctx, node, **kwargs):
-        dtypes = node.output_dtypes
-        ctx.remove_node(node.name)
-        shape = ctx.make_node("Shape", node.input).output[0]
-        zero_tensor = helper.make_tensor("value", dtypes[0], [1], vals=[0])
-        ctx.make_node("ConstantOfShape", inputs=[shape],
-                      attr={'value': zero_tensor},
-                      name=node.name, outputs=node.output,
-                      dtypes=dtypes)
+        _const_like_version_9(ctx, node, 0)
+
+
+@tf_op("OnesLike")
+class OnesLike:
+    @classmethod
+    def version_1(cls, ctx, node, **kwargs):
+        _const_like_version_1(ctx, node, 1)
+
+    @classmethod
+    def version_9(cls, ctx, node, **kwargs):
+        _const_like_version_9(ctx, node, 1)
 
 
 @tf_op(["IteratorV2", "FIFOQueueV2"])