Upgrade test opsets and remove deprecated numpy and version usage

README.md

@@ -17,8 +17,8 @@ The common issues we run into we try to document here [Troubleshooting Guide](Tr
 
 | Build Type | OS | Python | TensorFlow | ONNX opset | Status |
 | ---        | ---    | ---    | ---        | ---        | ---    |
-| Unit Test - Basic | Linux, MacOS<sup>\*</sup>, Windows<sup>\*</sup> | 3.7-3.10 | 1.13-1.15, 2.1-2.9 | 9-16 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=main)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=16&branchName=main) |
-| Unit Test - Full | Linux, MacOS, Windows | 3.7-3.10 | 1.13-1.15, 2.1-2.9 | 9-16 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test-matrix?branchName=main)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=18&branchName=main) | |
+| Unit Test - Basic | Linux, MacOS<sup>\*</sup>, Windows<sup>\*</sup> | 3.7-3.10 | 1.13-1.15, 2.1-2.9 | 13-17 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=main)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=16&branchName=main) |
+| Unit Test - Full | Linux, MacOS, Windows | 3.7-3.10 | 1.13-1.15, 2.1-2.9 | 13-17 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test-matrix?branchName=main)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=18&branchName=main) | |
 <br/>
 
 ## Supported Versions
@@ -27,7 +27,7 @@ The common issues we run into we try to document here [Troubleshooting Guide](Tr
 
 tf2onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found.
 
-We support and test ONNX opset-9 to opset-17. opset-6 to opset-8 should work but we don't test them.
+We support and test ONNX opset-13 to opset-17. opset-6 to opset-12 should work but we don't test them.
 By default we use ```opset-13``` for the resulting ONNX graph.
 
 If you want the graph to be generated with a specific opset, use ```--opset``` in the command line, for example ```--opset 13```.
@@ -43,7 +43,6 @@ You can install tf2onnx on top of tf-1.x or tf-2.x.
 ### Python
 
 We support Python ```3.7-3.10```.
-Note that on windows for Python > 3.7 the protobuf package doesn't use the cpp implementation and is very slow - we recommend to use Python 3.7 for that reason.
 
 ## Prerequisites
 
@@ -83,7 +82,7 @@ or
 
 ```python setup.py develop```
 
-tensorflow-onnx requires onnx-1.5 or better and will install/upgrade onnx if needed.
+tensorflow-onnx requires onnx-1.9 or better and will install/upgrade onnx if needed.
 
 To create a wheel for distribution:
 
@@ -100,7 +99,7 @@ To get started with `tensorflow-onnx`, run the `t2onnx.convert` command, providi
 
 The above command uses a default of `13` for the ONNX opset. If you need a newer opset, or want to limit your model to use an older opset then you can provide the `--opset` argument to the command. If you are unsure about which opset to use, refer to the [ONNX operator documentation](https://github.com/onnx/onnx/releases).
 
-```python -m tf2onnx.convert --saved-model tensorflow-model-path --opset 16 --output model.onnx```
+```python -m tf2onnx.convert --saved-model tensorflow-model-path --opset 17 --output model.onnx```
 
 If your TensorFlow model is in a format other than `saved model`, then you need to provide the inputs and outputs of the model graph.
 

ci_build/azure_pipelines/templates/job_generator.yml

@@ -5,7 +5,7 @@ parameters:
   python_versions: ['3.7']
   tf_versions: ['']
   onnx_versions: ['']
-  onnx_opsets: ['17', '16', '15', '14', '13', '12', '11', '10', '9']
+  onnx_opsets: ['17', '16', '15', '14', '13']
   onnx_backends: {onnxruntime: ['1.12.0']}
   job: {}
   run_setup: 'True'

ci_build/azure_pipelines/templates/unit_test.yml

@@ -1,7 +1,7 @@
 # Run unit test
 
 parameters:
-    onnx_opsets: ['17', '16', '15', '14', '13', '12', '11', '10', '9']
+    onnx_opsets: ['17', '16', '15', '14', '13']
     skip_tflite_tests: 'True'
     skip_tfjs_tests: 'True'
     skip_tf_tests: 'False'

ci_build/azure_pipelines/unit_test-matrix.yml

@@ -7,18 +7,7 @@ stages:
       parameters:
         platforms: ['linux', 'windows']
         python_versions: ['3.7']
-        tf_versions: ['1.14.0']
-        onnx_opsets: ['']
-        job:
-          steps:
-          - template: 'unit_test.yml'
-        report_coverage: 'True'
-
-    - template: 'templates/job_generator.yml'
-      parameters:
-        platforms: ['linux', 'windows']
-        python_versions: ['3.7']
-        tf_versions: ['1.15.2','2.1.0']
+        tf_versions: ['1.14.0', '1.15.2']
         onnx_opsets: ['']
         job:
           steps:

ci_build/azure_pipelines/unit_test.yml

@@ -80,7 +80,7 @@ stages:
     - template: 'templates/job_generator.yml'
       parameters:
         # TFJS tf 2.9
-        python_versions: ['3.9']
+        python_versions: ['3.10']
         tf_versions: ['2.9.1']
         onnx_opsets: ['']
         skip_tfjs_tests: 'False'
@@ -93,7 +93,7 @@ stages:
     - template: 'templates/job_generator.yml'
       parameters:
         # TFLite tf 2.9
-        python_versions: ['3.8']
+        python_versions: ['3.10']
         tf_versions: ['2.9.1']
         onnx_opsets: ['']
         skip_tflite_tests: 'False'
@@ -162,7 +162,7 @@ stages:
         python_versions: ['3.9']
         platforms: ['windows']
         tf_versions: ['2.9.1']
-        onnx_opsets: ['15']
+        onnx_opsets: ['16']
         job:
           steps:
           - template: 'unit_test.yml'

tests/common.py

@@ -9,7 +9,7 @@
 import unittest
 from collections import defaultdict
 
-from distutils.version import LooseVersion
+from packaging.version import Version
 from parameterized import parameterized
 import numpy as np
 import tensorflow as tf
@@ -98,7 +98,7 @@ def _get_backend_version(self):
             pass
 
         if version:
-            version = LooseVersion(version)
+            version = Version(version)
         return version
 
     def __str__(self):
@@ -178,7 +178,7 @@ def check_opset_after_tf_version(tf_version, required_opset, message=""):
     """ Skip if tf_version > max_required_version """
     config = get_test_config()
     reason = _append_message("conversion requires opset {} after tf {}".format(required_opset, tf_version), message)
-    skip = config.tf_version >= LooseVersion(tf_version) and config.opset < required_opset
+    skip = config.tf_version >= Version(tf_version) and config.opset < required_opset
     return unittest.skipIf(skip, reason)
 
 
@@ -284,7 +284,7 @@ def check_tfjs_max_version(max_accepted_version, message=""):
     except ModuleNotFoundError:
         can_import = False
     return unittest.skipIf(can_import and not config.skip_tfjs_tests and \
-         tensorflowjs.__version__ > LooseVersion(max_accepted_version), reason)
+         Version(tensorflowjs.__version__) > Version(max_accepted_version), reason)
 
 def check_tfjs_min_version(min_required_version, message=""):
     """ Skip if tjs_version < min_required_version """
@@ -296,20 +296,20 @@ def check_tfjs_min_version(min_required_version, message=""):
     except ModuleNotFoundError:
         can_import = False
     return unittest.skipIf(can_import and not config.skip_tfjs_tests and \
-         tensorflowjs.__version__ < LooseVersion(min_required_version), reason)
+         Version(tensorflowjs.__version__) < Version(min_required_version), reason)
 
 def check_tf_max_version(max_accepted_version, message=""):
     """ Skip if tf_version > max_required_version """
     config = get_test_config()
     reason = _append_message("conversion requires tf <= {}".format(max_accepted_version), message)
-    return unittest.skipIf(config.tf_version > LooseVersion(max_accepted_version), reason)
+    return unittest.skipIf(config.tf_version > Version(max_accepted_version), reason)
 
 
 def check_tf_min_version(min_required_version, message=""):
     """ Skip if tf_version < min_required_version """
     config = get_test_config()
     reason = _append_message("conversion requires tf >= {}".format(min_required_version), message)
-    return unittest.skipIf(config.tf_version < LooseVersion(min_required_version), reason)
+    return unittest.skipIf(config.tf_version < Version(min_required_version), reason)
 
 
 def skip_tf_versions(excluded_versions, message=""):
@@ -385,7 +385,7 @@ def check_onnxruntime_min_version(min_required_version, message=""):
     config = get_test_config()
     reason = _append_message("conversion requires onnxruntime >= {}".format(min_required_version), message)
     return unittest.skipIf(config.is_onnxruntime_backend and
-                           config.backend_version < LooseVersion(min_required_version), reason)
+                           config.backend_version < Version(min_required_version), reason)
 
 
 def skip_caffe2_backend(message=""):

tests/run_pretrained_models.py

@@ -17,7 +17,7 @@
 import zipfile
 import random
 from collections import namedtuple
-from distutils.version import LooseVersion
+from packaging.version import Version
 
 
 import yaml
@@ -789,7 +789,7 @@ def main():
                 continue
 
             if t.tf_min_version:
-                if tf_utils.get_tf_version() < LooseVersion(str(t.tf_min_version)):
+                if tf_utils.get_tf_version() < Version(str(t.tf_min_version)):
                     logger.info("Skip %s: %s %s", test, "Min TF version needed:", t.tf_min_version)
                     continue
 

tests/test_backend.py

@@ -5,11 +5,11 @@
 
 import os
 import unittest
-from distutils.version import LooseVersion
 from itertools import product
 
 import numpy as np
 from numpy.testing import assert_almost_equal
+from packaging.version import Version
 import tensorflow as tf
 
 from tensorflow.python.ops import lookup_ops
@@ -72,7 +72,7 @@
     matrix_diag_part = tf.compat.v1.matrix_diag_part
     fake_quant_with_min_max_args = tf.quantization.fake_quant_with_min_max_args
     fake_quant_with_min_max_vars = tf.quantization.fake_quant_with_min_max_vars
-elif LooseVersion(tf.__version__) >= "1.13":
+elif Version(tf.__version__) >= Version("1.13"):
     conv2d_backprop_input = tf.compat.v1.nn.conv2d_backprop_input
     conv3d_transpose = tf.compat.v1.nn.conv3d_transpose
     multinomial = tf.compat.v1.random.multinomial
@@ -86,7 +86,7 @@
     quantize_and_dequantize = tf.compat.v1.quantization.quantize_and_dequantize
     resize_nearest_neighbor = tf.compat.v1.image.resize_nearest_neighbor
     resize_bilinear = tf.compat.v1.image.resize_bilinear
-    if LooseVersion(tf.__version__) >= "1.14":
+    if Version(tf.__version__) >= Version("1.14"):
         resize_bilinear_v2 = tf.compat.v2.image.resize
     is_nan = tf.math.is_nan
     is_inf = tf.math.is_inf
@@ -1320,8 +1320,8 @@ def func(x1):
 
     @check_onnxruntime_incompatibility("Add")
     def test_logicaland(self):
-        x_val1 = np.array([1, 0, 1, 1], dtype=np.bool).reshape((2, 2))
-        x_val2 = np.array([0, 1, 1, 1], dtype=np.bool).reshape((2, 2))
+        x_val1 = np.array([1, 0, 1, 1], dtype=bool).reshape((2, 2))
+        x_val2 = np.array([0, 1, 1, 1], dtype=bool).reshape((2, 2))
         def func(x1, x2):
             mi = tf.logical_and(x1, x2)
             return tf.identity(mi, name=_TFOUTPUT)
@@ -3505,9 +3505,9 @@ def func(x):
     def test_where_bool(self):
         x_val = np.array([1, 2, -3, 4, -5], dtype=np.float32)
         true_result = np.array([True, False, True, False, True],
-                               dtype=np.bool)
+                               dtype=bool)
         false_result = np.array([False, True, False, True, True],
-                                dtype=np.bool)
+                                dtype=bool)
         def func(x):
             picks = tf.where(x > -1, true_result, false_result)
             return tf.identity(picks, name=_TFOUTPUT)
@@ -3770,36 +3770,36 @@ def func(input_1, input_2):
         self._run_test_case(func, [_OUTPUT], {_INPUT: input_val_1, _INPUT1: input_val_2}, rtol=1e-4)
 
     def test_logical_not(self):
-        input_val = np.random.randint(0, 2, (10, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (10, 20)).astype(bool)
         def func(x):
             res = tf.logical_not(x)
             return tf.identity(res, name=_TFOUTPUT)
         self._run_test_case(func, [_OUTPUT], {_INPUT: input_val})
 
     def test_reduce_all(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_all(input_tensor=x, keepdims=False)
             res1 = tf.reduce_all(input_tensor=x, axis=[0], keepdims=False)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(input_x):
             res = tf.reduce_all(input_tensor=input_x, keepdims=True)
             res1 = tf.reduce_all(input_tensor=input_x, axis=[0], keepdims=True)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
     def test_reduce_any(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_any(input_tensor=x, keepdims=False)
             res1 = tf.reduce_any(input_tensor=x, axis=[0], keepdims=False)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_any(input_tensor=x, keepdims=True)
             res1 = tf.reduce_any(input_tensor=x, axis=[0], keepdims=True)
@@ -3808,14 +3808,14 @@ def func(x):
 
     @check_opset_min_version(11, "ReduceMin")
     def test_reduce_all_negative_axis(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_all(input_tensor=x, keepdims=False)
             res1 = tf.reduce_all(input_tensor=x, axis=[-1], keepdims=False)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(input_x):
             res = tf.reduce_all(input_tensor=input_x, keepdims=True)
             res1 = tf.reduce_all(input_tensor=input_x, axis=[-1], keepdims=True)
@@ -3824,14 +3824,14 @@ def func(input_x):
 
     @check_opset_min_version(11, "ReduceSum")
     def test_reduce_any_negative_axis(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_any(input_tensor=x, keepdims=False)
             res1 = tf.reduce_any(input_tensor=x, axis=[-1], keepdims=False)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_any(input_tensor=x, keepdims=True)
             res1 = tf.reduce_any(input_tensor=x, axis=[-1], keepdims=True)
@@ -3841,15 +3841,15 @@ def func(x):
     @check_opset_min_version(11, "ReduceSum")
     @check_tf_min_version("1.15")
     def test_reduce_any_empty_axis(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_any(input_tensor=x, keepdims=False)
             res1 = tf.reduce_any(input_tensor=x, axis=[], keepdims=False)
             return tf.identity(res, name=_TFOUTPUT), tf.identity(res1, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: input_val})
 
     def test_reduce_all_scalar_axis(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         def func(x):
             res = tf.reduce_all(input_tensor=x, keepdims=False)
             res1 = tf.reduce_all(input_tensor=x, axis=0, keepdims=False)
@@ -3859,7 +3859,7 @@ def func(x):
     @check_opset_min_version(13, "ReduceSum")
     @check_tf_min_version("1.15")
     def test_reduce_any_nonconst_axis(self):
-        input_val = np.random.randint(0, 2, (2, 20)).astype(np.bool)
+        input_val = np.random.randint(0, 2, (2, 20)).astype(bool)
         y_val = np.array([1], np.int32)
         def func(x, y):
             res = tf.reduce_any(input_tensor=x, axis=y, keepdims=False)

tests/test_onnx_shape_inference.py

@@ -353,7 +353,7 @@ def test_if(self):
         sub = else_subgraph.make_node("Sub", [INPUT1, INPUT3])
         else_subgraph.add_graph_output(sub.output[0])
 
-        cond = graph.make_const("cond", np.array(True, dtype=np.bool))
+        cond = graph.make_const("cond", np.array(True, dtype=bool))
         branches = {"then_branch": then_subgraph, "else_branch": else_subgraph}
         if_node = graph.make_node("If", [cond.output[0]], branches=branches)
 
@@ -381,7 +381,7 @@ def test_loop(self):
         subgraph.add_graph_output(out.output[0])
 
         max_iter = graph.make_const("max_iter", np.array([10], dtype=np.int64))
-        cond_const = graph.make_const("cond_const", np.array([True], dtype=np.bool))
+        cond_const = graph.make_const("cond_const", np.array([True], dtype=bool))
         branches = {"body": subgraph}
         loop = graph.make_node("Loop", [max_iter.output[0], cond_const.output[0], INPUT1],
                                output_count=2, branches=branches)

tests/test_optimizers.py

@@ -988,7 +988,7 @@ def _define_loop_graph(external_inputs):
 
         def _make_loop(external_inputs, outputs):
             trip_cnt = self._make_onnx_const(np.array(10, dtype=np.int64), "trip_cnt")
-            cond = self._make_onnx_const(np.array(True, dtype=np.bool), "cond")
+            cond = self._make_onnx_const(np.array(True, dtype=bool), "cond")
             sub_graph = _define_loop_graph(external_inputs)
             loop_node = helper.make_node("Loop", ["trip_cnt", "cond", "cond"], outputs,
                                          name="loop", body=sub_graph)
@@ -1779,7 +1779,7 @@ def test_identity_in_subgraph_non_graph_output(self):
             ),
         )
 
-        cond_value = np.array(True, dtype=np.bool)
+        cond_value = np.array(True, dtype=bool)
         node3 = helper.make_node(
             'Constant',
             inputs=[],
@@ -1788,7 +1788,7 @@ def test_identity_in_subgraph_non_graph_output(self):
                 name='cond_value',
                 data_type=TensorProto.BOOL,
                 dims=iter_num_value.shape,
-                vals=cond_value.flatten().astype(np.bool).tolist(),
+                vals=cond_value.flatten().astype(bool).tolist(),
             ),
         )