enforce onnx conversion in CI

.circleci/config.yml

@@ -18,12 +18,17 @@ jobs:
       pip_constraints:
         type: string
         description: Constraints file that is passed to "pip install". We constraint older versions of libraries for older python runtime, in order to help ensure compatibility.
+      enforce_onnx_conversion:
+        type: integer
+        default: 0
+        description: Whether to raise an exception if ONNX models couldn't be saved.
     executor: << parameters.executor >>
     working_directory: ~/repo
 
     # Run additional numpy checks on unit tests
     environment:
       TEST_ENFORCE_NUMPY_FLOAT32: 1
+      TEST_ENFORCE_ONNX_CONVERSION: << parameters.enforce_onnx_conversion >>
 
     steps:
       - checkout
@@ -217,6 +222,8 @@ workflows:
           pyversion: 3.7.3
           # Test python 3.7 with the newest supported versions
           pip_constraints: test_constraints_max_tf1_version.txt
+          # Make sure ONNX conversion passes here (recent version of tensorflow 1.x)
+          enforce_onnx_conversion: 1
       - build_python:
           name: python_3.7.3+tf2
           executor: python373

docs/Unity-Inference-Engine.md

@@ -33,7 +33,7 @@ There are currently two supported model formats:
  * ONNX (`.onnx`) files use an [industry-standard open format](https://onnx.ai/about.html) produced by the [tf2onnx package](https://github.com/onnx/tensorflow-onnx).
 
 Export to ONNX is currently considered beta. To enable it, make sure `tf2onnx>=1.5.5` is installed in pip.
-tf2onnx does not currently support tensorflow 2.0.0 or later.
+tf2onnx does not currently support tensorflow 2.0.0 or later, or earlier than 1.12.0.
 
 ## Using the Unity Inference Engine
 

ml-agents/mlagents/model_serialization.py

@@ -1,5 +1,8 @@
+from distutils.util import strtobool
+import os
 import logging
 from typing import Any, List, Set, NamedTuple
+from distutils.version import LooseVersion
 
 try:
     import onnx
@@ -18,6 +21,11 @@
 from tensorflow.python.framework import graph_util
 from mlagents.trainers import tensorflow_to_barracuda as tf2bc
 
+if LooseVersion(tf.__version__) < LooseVersion("1.12.0"):
+    # ONNX is only tested on 1.12.0 and later
+    ONNX_EXPORT_ENABLED = False
+
+
 logger = logging.getLogger("mlagents.trainers")
 
 POSSIBLE_INPUT_NODES = frozenset(
@@ -67,18 +75,28 @@ def export_policy_model(
         logger.info(f"Exported {settings.model_path}.nn file")
 
     # Save to onnx too (if we were able to import it)
-    if ONNX_EXPORT_ENABLED and settings.convert_to_onnx:
-        try:
-            onnx_graph = convert_frozen_to_onnx(settings, frozen_graph_def)
-            onnx_output_path = settings.model_path + ".onnx"
-            with open(onnx_output_path, "wb") as f:
-                f.write(onnx_graph.SerializeToString())
-            logger.info(f"Converting to {onnx_output_path}")
-        except Exception:
-            logger.exception(
-                "Exception trying to save ONNX graph. Please report this error on "
-                "https://github.com/Unity-Technologies/ml-agents/issues and "
-                "attach a copy of frozen_graph_def.pb"
+    if ONNX_EXPORT_ENABLED:
+        if settings.convert_to_onnx:
+            try:
+                onnx_graph = convert_frozen_to_onnx(settings, frozen_graph_def)
+                onnx_output_path = settings.model_path + ".onnx"
+                with open(onnx_output_path, "wb") as f:
+                    f.write(onnx_graph.SerializeToString())
+                logger.info(f"Converting to {onnx_output_path}")
+            except Exception:
+                # Make conversion errors fatal depending on environment variables (only done during CI)
+                if _enforce_onnx_conversion():
+                    raise
+                logger.exception(
+                    "Exception trying to save ONNX graph. Please report this error on "
+                    "https://github.com/Unity-Technologies/ml-agents/issues and "
+                    "attach a copy of frozen_graph_def.pb"
+                )
+
+    else:
+        if _enforce_onnx_conversion():
+            raise RuntimeError(
+                "ONNX conversion enforced, but couldn't import dependencies."
             )
 
 
@@ -203,3 +221,16 @@ def _process_graph(settings: SerializationSettings, graph: tf.Graph) -> List[str
     for n in nodes:
         logger.info("\t" + n)
     return nodes
+
+
+def _enforce_onnx_conversion() -> bool:
+    env_var_name = "TEST_ENFORCE_ONNX_CONVERSION"
+    if env_var_name not in os.environ:
+        return False
+
+    val = os.environ[env_var_name]
+    try:
+        # This handles e.g. "false" converting reasonably to False
+        return strtobool(val)
+    except Exception:
+        return False

test_constraints_max_tf1_version.txt

@@ -3,6 +3,5 @@
 # For projects with upper bounds, we should periodically update this list to the latest release version
 grpcio>=1.23.0
 numpy>=1.17.2
-# Temporary workaround for https://github.com/tensorflow/tensorflow/issues/36179 and https://github.com/tensorflow/tensorflow/issues/36188
-tensorflow>=1.14.0,<1.15.1
+tensorflow>=1.15.2,<2.0.0
 h5py>=2.10.0

test_constraints_min_version.txt

@@ -3,5 +3,5 @@ grpcio==1.11.0
 numpy==1.14.1
 Pillow==4.2.1
 protobuf==3.6
-tensorflow==1.7
+tensorflow==1.7.0
 h5py==2.9.0

test_requirements.txt

@@ -3,6 +3,4 @@ pytest>4.0.0,<6.0.0
 pytest-cov==2.6.1
 pytest-xdist
 
-# Tests install onnx and tf2onnx, but this doesn't support tensorflow>=2.0.0
-# Since we test tensorflow2.0 with python3.7, exclude it based on the python version
-tf2onnx>=1.5.5; python_version < '3.7'
+tf2onnx>=1.5.5