Fix nightly build for tf2.1 (#1564)

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

tests/test_api.py

@@ -12,7 +12,7 @@
 import tensorflow as tf
 from onnx import helper
 
-from common import check_tf_min_version, unittest_main, requires_custom_ops, check_opset_min_version
+from common import check_tf_min_version, unittest_main, requires_custom_ops, check_opset_min_version, skip_tf_versions
 from tf2onnx.tf_loader import is_tf2
 from backend_test_base import Tf2OnnxBackendTestBase
 import tf2onnx
@@ -73,12 +73,14 @@ def test_keras_api(self):
         self._test_keras_api(large_model=False)
 
     @check_tf_min_version("1.15")
+    @skip_tf_versions(["2.0", "2.1"], "TF 2 requires 2.2 for large model freezing")
     def test_keras_api_large(self):
         self._test_keras_api(large_model=True)
 
     @requires_custom_ops()
     @check_tf_min_version("1.15")
     @check_opset_min_version(11, "SparseToDense")
+    @skip_tf_versions(["2.1"], "TF 2.1 keras model doesn't work; table not initialized")
     def test_keras_hashtable(self):
 
         feature_cols = [

tests/test_backend.py

@@ -4646,7 +4646,7 @@ def func(starts, limits, deltas):
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: starts_val, _INPUT1: limits_val,
                                                         _INPUT2: deltas_val})
 
-    @check_tf_min_version("2.0", "ragged variant needs tf 2.0")
+    @check_tf_min_version("2.2", "ragged variant needs tf 2.2")
     @check_opset_min_version(13, "Loop over tensor sequences")
     def test_ragged_to_variant(self):
         splits_val = np.array([0, 3, 3, 5, 9, 10], dtype=np.int32)
@@ -4662,7 +4662,7 @@ def func(splits, rt_dense_values):
             return tf.identity(y.row_splits, name=_TFOUTPUT), tf.identity(y.flat_values, name=_TFOUTPUT1)
         self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: splits_val, _INPUT1: dense_vals_val})
 
-    @check_tf_min_version("2.0", "ragged variant needs tf 2.0")
+    @check_tf_min_version("2.2", "ragged variant needs tf 2.2")
     @check_opset_min_version(13, "Loop over tensor sequences")
     def test_ragged_to_variant_unknown_shape(self):
         splits_val = np.array([0, 3, 3, 5, 9, 10], dtype=np.int64)

tests/test_string_ops.py

@@ -111,7 +111,7 @@ def func(x1, x2):
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val1, _INPUT1: x_val2})
 
     @requires_custom_ops("RegexSplitWithOffsets")
-    @check_tf_min_version("2.0", "tensorflow_text")
+    @check_tf_min_version("2.3", "tensorflow_text")
     def test_regex_split_with_offsets(self):
         from tensorflow_text.python.ops.regex_split_ops import (
             gen_regex_split_ops as lib_gen_regex_split_ops)
@@ -134,7 +134,7 @@ def _run_test_case(self, func, output_names_with_port, feed_dict, **kwargs):
                                   use_custom_ops=True, process_args=process_args, **kwargs)
 
     @requires_custom_ops("WordpieceTokenizer")
-    @check_tf_min_version("2.0", "tensorflow_text")
+    @check_tf_min_version("2.3", "tensorflow_text")
     @unittest.skip("Not fixed yet")
     def test_wordpiece_tokenizer(self):
         from tensorflow_text.python.ops.wordpiece_tokenizer import (