Replace deprecated np.object with object

Author: vvolhejn

tests/backend_test_base.py

@@ -112,7 +112,7 @@ def assert_results_equal(self, expected, actual, rtol, atol, mtol=None,
                              check_value=True, check_shape=True, check_dtype=True):
         for expected_val, actual_val in zip(expected, actual):
             if check_value:
-                if expected_val.dtype == np.object:
+                if expected_val.dtype == object:
                     # TFLite pads strings with nul bytes
                     decode = np.vectorize(lambda x: x.replace(b'\x00', b'').decode('UTF-8'))
                     expected_val_str = decode(expected_val)

tests/run_pretrained_models.py

@@ -525,7 +525,7 @@ def run_tflite():
                         inputs[k] = np_value.astype(expected_dtype)
                     else:
                         if expected_dtype == "string":
-                            inputs[k] = self.make_input(v).astype(np.str).astype(np.object)
+                            inputs[k] = self.make_input(v).astype(np.str).astype(object)
                         else:
                             inputs[k] = self.make_input(v).astype(expected_dtype)
 

tests/test_backend.py

@@ -5230,7 +5230,7 @@ def func(value, filters, output_shape):
     def test_hashtable_lookup(self):
         filnm = "vocab.tmp"
         words = ["apple", "pear", "banana", "cherry", "grape"]
-        query = np.array(['cherry'], dtype=np.object)
+        query = np.array(['cherry'], dtype=object)
         with open(filnm, "w") as f:
             for word in words:
                 f.write(word + "\n")
@@ -5247,7 +5247,7 @@ def func(query_holder):
     def test_hashtable_lookup_const(self):
         filnm = "vocab.tmp"
         words = ["apple", "pear", "banana", "cherry ♥", "grape"]
-        query_val = np.array(['cherry ♥', 'banana'], dtype=np.object).reshape((1, 2, 1))
+        query_val = np.array(['cherry ♥', 'banana'], dtype=object).reshape((1, 2, 1))
         with open(filnm, "w", encoding='UTF-8') as f:
             for word in words:
                 f.write(word + "\n")
@@ -5264,7 +5264,7 @@ def func():
     def test_hashtable_size(self):
         filnm = "vocab.tmp"
         words = ["apple", "pear", "banana", "cherry", "grape"]
-        query = np.array(['cherry'], dtype=np.object)
+        query = np.array(['cherry'], dtype=object)
         with open(filnm, "w") as f:
             for word in words:
                 f.write(word + "\n")
@@ -5853,10 +5853,10 @@ def func(x):
             return tf.identity(op_, name=_TFOUTPUT)
 
         # tf gets this wrong and returns fp32 instead of int
-        x_val = np.array("123", dtype=np.object)
+        x_val = np.array("123", dtype=object)
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
-        x_val = np.array("123.1", dtype=np.object)
+        x_val = np.array("123.1", dtype=object)
         # can't check the values because in onnx they are padded with 0, in tf they are not
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, check_value=False)
 

tests/test_internals.py

@@ -107,10 +107,10 @@ def test_insert_node2(self):
     def test_make_const_string(self):
         graph_proto = self.sample_net()
         g = GraphUtil.create_graph_from_onnx_graph(graph_proto)
-        arr1 = np.array("test", np.object)
-        arr2 = np.array([["A", "B"], ["C", "D"]], np.object)
-        arr3 = np.array(b"test", np.object)
-        arr4 = np.array([[b"A", b"B"], [b"C", b"D"]], np.object)
+        arr1 = np.array("test", object)
+        arr2 = np.array([["A", "B"], ["C", "D"]], object)
+        arr3 = np.array(b"test", object)
+        arr4 = np.array([[b"A", b"B"], [b"C", b"D"]], object)
         const1 = g.make_const("const1", arr1)
         const2 = g.make_const("const2", arr2)
         const3 = g.make_const("const3", arr3)

tf2onnx/custom_opsets/string_ops.py

@@ -53,8 +53,8 @@ def version_1(cls, ctx, node, **kwargs):
         rewrite = node.get_attr_str("rewrite")
         utils.make_sure(node.get_attr_value("replace_global") != 0,
                         "Can not convert StaticRegexReplace if replace_global is False")
-        pattern_node = ctx.make_const(utils.make_name("pattern"), np.array([pattern], np.object))
-        rewrite_node = ctx.make_const(utils.make_name("rewrite"), np.array([rewrite], np.object))
+        pattern_node = ctx.make_const(utils.make_name("pattern"), np.array([pattern], object))
+        rewrite_node = ctx.make_const(utils.make_name("rewrite"), np.array([rewrite], object))
         del node.attr["pattern"]
         del node.attr["rewrite"]
         del node.attr["replace_global"]
@@ -69,7 +69,7 @@ def version_1(cls, ctx, node, **kwargs):
         if separator is None:
             separator = b''
         separator = separator.decode('UTF-8')
-        separator_node = ctx.make_const(utils.make_name("separator"), np.array([separator], np.object))
+        separator_node = ctx.make_const(utils.make_name("separator"), np.array([separator], object))
         axis_node = ctx.make_const(utils.make_name("axis"), np.array([0], np.int64))
         inps_with_shapes = [i for i in node.input if ctx.get_shape(i) != []]
         shape_node = None

tf2onnx/graph.py

@@ -582,7 +582,7 @@ def make_const(self, name, np_val, skip_conversion=False, raw=True):
             raw: whether to store data at field of raw_data or the specific field according to its dtype
         """
         np_val_flat = np_val.flatten()
-        is_bytes = np_val.dtype == np.object and len(np_val_flat) > 0 and isinstance(np_val_flat[0], bytes)
+        is_bytes = np_val.dtype == object and len(np_val_flat) > 0 and isinstance(np_val_flat[0], bytes)
         if raw and not is_bytes:
             onnx_tensor = numpy_helper.from_array(np_val, name)
         else:

tf2onnx/optimizer/reshape_optimizer.py

@@ -54,7 +54,7 @@ def _optimize_reshape(self, node, graph):
                         symbolic_shape.append(SymbolicTensorElement.from_variable(i))
                     else:
                         symbolic_shape.append(SymbolicTensorElement.from_const(d))
-                feed_dict[n.output[0]] = np.array(symbolic_shape, np.object)
+                feed_dict[n.output[0]] = np.array(symbolic_shape, object)
         try:
             symbolic_res = SymbolicExecutor(graph).compute_outputs([node.input[1]], feed_dict)
         except SymbolicExecutionException:

tf2onnx/symbolic_executor.py

@@ -136,7 +136,7 @@ def compute_squeeze_unsqueeze(self, node, feed_dict):
 
     def compute_cast(self, node, feed_dict):
         inp = feed_dict[node.input[0]]
-        if inp.dtype == np.object:
+        if inp.dtype == object:
             return [inp]
         np_dtype = utils.ONNX_TO_NUMPY_DTYPE[node.get_attr("to").i]
         return [inp.astype(np_dtype)]
@@ -181,7 +181,7 @@ def compute_concat(self, node, feed_dict):
     def compute_gather(self, node, feed_dict):
         data = feed_dict[node.input[0]]
         indices = feed_dict[node.input[1]]
-        if indices.dtype == np.object:
+        if indices.dtype == object:
             raise SymbolicExecutionException("Gather requires non-symbolic indices")
         axis = node.get_attr_value("axis", 0)
         return [np.take(data, indices, axis=axis)]

tf2onnx/tf_utils.py

@@ -50,15 +50,15 @@
 def tf_to_onnx_tensor(tensor, name=""):
     """Convert tensorflow tensor to onnx tensor."""
     np_data = get_tf_tensor_data(tensor)
-    if np_data.dtype == np.object:
+    if np_data.dtype == object:
         # assume np_data is string, numpy_helper.from_array accepts ndarray,
         # in which each item is of str while the whole dtype is of object.
         try:
             # Faster but fails on Unicode
-            np_data = np_data.astype(np.str).astype(np.object)
+            np_data = np_data.astype(np.str).astype(object)
         except UnicodeDecodeError:
             decode = np.vectorize(lambda x: x.decode('UTF-8'))
-            np_data = decode(np_data).astype(np.object)
+            np_data = decode(np_data).astype(object)
         except:  # pylint: disable=bare-except
             raise RuntimeError("Not support type: {}".format(type(np_data.flat[0])))
     return numpy_helper.from_array(np_data, name=name)

tf2onnx/tflite_utils.py

@@ -271,7 +271,7 @@ def read_int(offset):
     string_list = []
     for i in range(count):
         string_list.append(buffer_bytes[offset_list[i]:offset_list[i+1]].decode("utf-8"))
-    return numpy_helper.from_array(np.array(string_list, dtype=np.object).reshape(shape))
+    return numpy_helper.from_array(np.array(string_list, dtype=object).reshape(shape))
 
 
 def op_has_scalar_output(input_shapes, optype, attr):