merge changes from master for 1.7.2

Signed-off-by: Guenther Schmuelling <[email protected]>

Author: guschmue

README.md

@@ -145,8 +145,10 @@ python -m tf2onnx.convert
     [--target TARGET]
     [--custom-ops list-of-custom-ops]
     [--fold_const]
+    [--large_model]
     [--continue_on_error]
     [--verbose]
+    [--output_frozen_graph]
 ```
 
 ### Parameters
@@ -199,13 +201,17 @@ Only valid with parameter `--saved_model`. If a model contains a list of concret
 
 Only valid with parameter `--saved_model`. When set, creates a zip file containing the ONNX protobuf model and large tensor values stored externally. This allows for converting models that exceed the 2 GB protobuf limit.
 
+#### --output_frozen_graph
+
+Saves the frozen tensorflow graph to file.
+
 #### --target
 
 Some models require special handling to run on some runtimes. In particular, the model may use unsupported data types. Workarounds are activated with ```--target TARGET```. Currently supported values are listed on this [wiki](https://github.com/onnx/tensorflow-onnx/wiki/target). If your model will be run on Windows ML, you should specify the appropriate target value.
 
 #### --fold_const
 
-When set, TensorFlow fold_constants transformation is applied before conversion. This benefits features including Transpose optimization (e.g. Transpose operations introduced during tf-graph-to-onnx-graph conversion will be removed), and RNN unit conversion (for example LSTM). Older TensorFlow version might run into issues with this option depending on the model.
+Deprecated. Constant folding is always enabled.
 
 ### <a name="summarize_graph"></a>Tool to get Graph Inputs & Outputs
 

VERSION_NUMBER

@@ -1 +1 @@
-1.7.1
+1.7.2

ci_build/azure_pipelines/pretrained_model_test.yml

@@ -4,7 +4,7 @@ jobs:
 - template: 'templates/job_generator.yml'
   parameters:
     python_versions: ['3.7']
-    tf_versions: ['1.15.2','2.1.0']
+    tf_versions: ['1.15.2','2.3.0']
     job:
       steps:
       - template: 'pretrained_model_test.yml'
@@ -24,3 +24,4 @@ jobs:
     job:
       steps:
       - template: 'pretrained_model_test.yml'
+

ci_build/azure_pipelines/pylint.yml

@@ -12,3 +12,4 @@ jobs:
           pip freeze
           pylint --rcfile=tools/pylintrc --ignore=version.py --disable=cyclic-import tf2onnx tests/*.py tools -j 0
         displayName: 'Pylint'
+

ci_build/azure_pipelines/unit_test.yml

@@ -16,7 +16,7 @@ stages:
     - template: 'templates/job_generator.yml'
       parameters:
         python_versions: ['3.7']
-        tf_versions: ['1.14.0','1.15.2','2.1.0','2.2.0']
+        tf_versions: ['1.14.0','1.15.2','2.2.0','2.3.0']
         onnx_opsets: ['']
         job:
           steps:
@@ -44,3 +44,4 @@ stages:
         report_coverage: 'True'
 
   - template: 'templates/combine_test_coverage.yml'
+

tests/backend_test_base.py

@@ -30,6 +30,14 @@
 from tf2onnx.graph import ExternalTensorStorage
 
 
+if is_tf2():
+    tf_set_random_seed = tf.compat.v1.set_random_seed
+    tf_tables_initializer = tf.compat.v1.tables_initializer
+else:
+    tf_set_random_seed = tf.set_random_seed
+    tf_tables_initializer = tf.tables_initializer
+
+
 class Tf2OnnxBackendTestBase(unittest.TestCase):
     def setUp(self):
         self.config = get_test_config()
@@ -133,14 +141,13 @@ def run_test_case(self, func, feed_dict, input_names_with_port, output_names_wit
             # use graph to execute the tensorflow func
             #
             with tf_session() as sess:
-                tf.set_random_seed(1)
+                tf_set_random_seed(1)
                 input_list = []
                 for k, v in clean_feed_dict.items():
                     input_list.append(tf_placeholder(name=k, shape=v.shape, dtype=tf.as_dtype(v.dtype)))
                 func(*input_list)
                 variables_lib.global_variables_initializer().run()
-                if not is_tf2():
-                    tf.tables_initializer().run()
+                tf_tables_initializer().run()
                 output_dict = []
                 for out_name in output_names_with_port:
                     output_dict.append(sess.graph.get_tensor_by_name(out_name))