Use `object` instead of `np.object` and `str` instead of `np.str`. (#337)

docs/custom_ops.md

@@ -102,7 +102,7 @@ node = onnx.helper.make_node(
 )
 
 text = "Hello world louder"
-inputs = np.array([text], dtype=np.object),
+inputs = np.array([text], dtype=object),
 
 bert_tokenize_result = bert_cased_tokenizer.tokenize(text)
 
@@ -207,7 +207,7 @@ node = onnx.helper.make_node(
 )
 
 text = "Hello world louder"
-token_ids = np.array([bert_cased_tokenizer.tokenize(text)], dtype=np.object),
+token_ids = np.array([bert_cased_tokenizer.tokenize(text)], dtype=object),
 sentences = np.array(text)
 
 
@@ -383,7 +383,7 @@ graph = helper.make_graph(
 model = helper.make_model(
     graph, opset_imports=[helper.make_operatorsetid(domain, 1)])
 
-text = np.array(["unwanted running", "unwantedX running"], dtype=np.object)
+text = np.array(["unwanted running", "unwantedX running"], dtype=object)
 tokens = np.array(['un', '##want', '##ed', 'runn', '##ing', 'un', '##want', '##ed',
                   '[UNK]', 'runn', '##ing'], dtype=object),
 indices = np.array([14, 11, 12, 15, 16, 14, 11, 12, -1, 15, 16], dtype=int32)
@@ -452,7 +452,7 @@ node = onnx.helper.make_node(
     model=model
 )
 
-inputs = np.array(["Hello world", "Hello world louder"], dtype=np.object),
+inputs = np.array(["Hello world", "Hello world louder"], dtype=object),
 nbest_size = np.array([0], dtype=np.float32),
 alpha = np.array([0], dtype=np.float32),
 add_bos = np.array([0], dtype=np.bool_),
@@ -521,7 +521,7 @@ node = onnx.helper.make_node(
     outputs=['tokens'],
 )
 
-inputs = np.array([ "Hello world louder"], dtype=np.object),
+inputs = np.array([ "Hello world louder"], dtype=object),
 tokens = np.array(tokenizer(inputs), dtype=int32)
 
 expect(node, inputs=[inputs],

test/test_pyops.py

@@ -147,7 +147,7 @@ class TestPythonOp(unittest.TestCase):
             res = []
             for x in xs:
                 res.append(sep.join(x))
-            return np.array(res, dtype=np.object)
+            return np.array(res, dtype=object)
 
     def test_python_operator(self):
         so = _ort.SessionOptions()
@@ -222,9 +222,9 @@ class TestPythonOp(unittest.TestCase):
         onnx_model = _create_test_join()
         self.assertIn('op_type: "PyOpJoin"', str(onnx_model))
         sess = _ort.InferenceSession(onnx_model.SerializeToString(), so)
-        arr = np.array([["a", "b"]], dtype=np.object)
+        arr = np.array([["a", "b"]], dtype=object)
         txout = sess.run(None, {'input_1': arr})
-        exp = np.array(["a;b"], dtype=np.object)
+        exp = np.array(["a;b"], dtype=object)
         assert txout[0][0] == exp[0]
 
 

test/test_sentencepiece_ops.py

@@ -298,7 +298,7 @@ class TestPythonOpSentencePiece(unittest.TestCase):
         inputs = dict(
             model=model,
             inputs=np.array(
-                ["Hello world", "Hello world louder"], dtype=np.object),
+                ["Hello world", "Hello world louder"], dtype=object),
             nbest_size=np.array([0], dtype=np.int64),
             alpha=np.array([0], dtype=np.float32),
             add_bos=np.array([0], dtype=np.bool_),
@@ -321,7 +321,7 @@ class TestPythonOpSentencePiece(unittest.TestCase):
         inputs = dict(
             model=model,
             inputs=np.array(
-                ["Hello world", "Hello world louder"], dtype=np.object),
+                ["Hello world", "Hello world louder"], dtype=object),
             nbest_size=np.array([0], dtype=np.int64),
             alpha=np.array([0], dtype=np.float32),
             add_bos=np.array([0], dtype=np.bool_),
@@ -346,7 +346,7 @@ class TestPythonOpSentencePiece(unittest.TestCase):
         inputs = dict(
             model=model,
             inputs=np.array(
-                ["Hello world", "Hello world louder"], dtype=np.object),
+                ["Hello world", "Hello world louder"], dtype=object),
             nbest_size=np.array([0], dtype=np.int64),
             alpha=np.array([0], dtype=np.float32),
             add_bos=np.array([0], dtype=np.bool_),
@@ -380,7 +380,7 @@ class TestPythonOpSentencePiece(unittest.TestCase):
                             model=model,
                             inputs=np.array(
                                 ["Hello world", "Hello world louder"],
-                                dtype=np.object),
+                                dtype=object),
                             nbest_size=np.array(
                                 [nbest_size], dtype=np.int64),
                             alpha=np.array([alpha], dtype=np.float32),
@@ -415,7 +415,7 @@ class TestPythonOpSentencePiece(unittest.TestCase):
             model=model,
             inputs=np.array(
                 ["Hello world", "Hello world louder"],
-                dtype=np.object),
+                dtype=object),
             nbest_size=np.array(
                 [nbest_size], dtype=np.int64),
             alpha=np.array([alpha], dtype=np.float32),

test/test_string_ops.py

@@ -830,19 +830,19 @@ class TestPythonOpString(unittest.TestCase):
     def enumerate_matrix_couples(self):
         for i in range(1, 5):
             shape = (3,) * i
-            a = (np.random.rand(*shape) * 10).astype(np.int32).astype(np.str)
+            a = (np.random.rand(*shape) * 10).astype(np.int32).astype(str)
             yield a, a
             for j in range(i):
                 shape2 = list(shape)
                 shape2[j] = 1
                 b = (np.random.rand(*shape2) * 10).astype(
-                    np.int32).astype(np.str)
+                    np.int32).astype(str)
                 yield a, b
                 for k in range(j+1, i):
                     shape3 = list(shape2)
                     shape3[k] = 1
                     b = (np.random.rand(*shape3) * 10).astype(
-                        np.int32).astype(np.str)
+                        np.int32).astype(str)
                     yield a, b
 
     def test_string_equal_python(self):
@@ -1117,7 +1117,7 @@ class TestPythonOpString(unittest.TestCase):
         cc_sess = _ort.InferenceSession(cc_onnx_model.SerializeToString(), so)
 
         inputs = dict(text=np.array(["unwanted running",
-                                     "unwantedX running"], dtype=np.object))
+                                     "unwantedX running"], dtype=object))
         cc_txout = cc_sess.run(None, inputs)
         exp = [np.array(['un', '##want', '##ed', 'runn', '##ing',
                          'un', '##want', '##ed', '[UNK]', 'runn', '##ing']),

tutorials/tf2onnx_custom_ops_tutorial.ipynb

@@ -365,7 +365,7 @@
     "        for a in list(node.attr.keys()):\n",
     "            del node.attr[a]\n",
     "        # Add the separator as an additional string input\n",
-    "        separator_const = ctx.make_const(utils.make_name('separator_const'), np.array([separator], dtype=np.object))\n",
+    "        separator_const = ctx.make_const(utils.make_name('separator_const'), np.array([separator], dtype=object))\n",
     "        ctx.replace_inputs(node, node.input + [separator_const.output[0]])"
    ],
    "cell_type": "code",
@@ -459,7 +459,7 @@
     "         outputs=[PyCustomOpDef.dt_string])\n",
     "def unsorted_segment_join(x, segment_ids, num_segments):\n",
     "    # The custom op implementation.\n",
-    "    result = np.full([num_segments], '', dtype=np.object)\n",
+    "    result = np.full([num_segments], '', dtype=object)\n",
     "    for s, seg_id in zip(x, segment_ids):\n",
     "        result[seg_id] += s\n",
     "    return result\n",
@@ -473,7 +473,7 @@
     "    for s, seg_id in zip(x, segment_ids):\n",
     "        result[seg_id].append(s)\n",
     "    result_joined = [separator.join(l) for l in result]\n",
-    "    return np.array(result_joined, dtype=np.object)"
+    "    return np.array(result_joined, dtype=object)"
    ]
   },
   {
@@ -485,7 +485,8 @@
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "[array(['javascript', 'carpet'], dtype=object)]\n[array(['java-script', 'car-pet'], dtype=object)]\n"
+      "[array(['javascript', 'carpet'], dtype=object)]\n",
+      "[array(['java-script', 'car-pet'], dtype=object)]\n"
      ]
     }
    ],