Merge latest code

CONTRIBUTING.md

@@ -24,14 +24,19 @@ New code *must* be accompanied by unit tests.
 Please see [Coding Conventions and Standards](http://google.github.io/styleguide/pyguide.html)
 
 # Licensing guidelines
-This project welcomes contributions and suggestions. Most contributions require you to
-agree to a Contributor License Agreement (CLA) declaring that you have the right to,
-and actually do, grant us the rights to use your contribution. For details, visit
-https://cla-assistant.io/onnx/tensorflow-onnx.
-
-When you submit a pull request, a CLA-bot will automatically determine whether you need
-to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the
-instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
+This project welcomes contributions and suggestions. The contributions require you to
+agree the Developer Certificate of Origin (DCO) declaring that you have the right to,
+and actually do, grant us the rights to use your contribution.
+
+When you submit a pull request, a DCO-bot will automatically determine whether you need
+to provide a DCO and decorate the PR appropriately.
+
+You are ready to sign your code by using the `-s` flag during your commits.
+
+```sh
+git commit -s
+```
+
 
 # Code of conduct
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

README.md

@@ -9,16 +9,16 @@ __Note: tensorflow.js support was just added. While we tested it with many tfjs
 
 TensorFlow has many more ops than ONNX and occasionally mapping a model to ONNX creates issues.
 
-You find a list of supported Tensorflow ops and their mapping to ONNX [here](support_status.md).
+You find a list of supported TensorFlow ops and their mapping to ONNX [here](support_status.md).
 
 The common issues we run into we try to document here [Troubleshooting Guide](Troubleshooting.md).
 
 <br/>
 
-| Build Type | OS | Python | Tensorflow | ONNX opset | Status |
+| Build Type | OS | Python | TensorFlow | ONNX opset | Status |
 | ---        | ---    | ---    | ---        | ---        | ---    |
-| Unit Test - Basic | Linux, MacOS<sup>\*</sup>, Windows<sup>\*</sup> | 3.6-3.9 | 1.12-1.15, 2.1-2.7 | 9-15 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=16&branchName=master) |
-| Unit Test - Full | Linux, MacOS, Windows | 3.6-3.9 | 1.12-1.15, 2.1-2.7 | 9-15 | [![Build Status](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_apis/build/status/unit_test-matrix?branchName=master)](https://dev.azure.com/tensorflow-onnx/tensorflow-onnx/_build/latest?definitionId=18&branchName=master) | |
+| 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
@@ -27,23 +27,22 @@ 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-15. opset-6 to opset-8 should work but we don't test them.
-By default we use ```opset-9``` for the resulting ONNX graph since most runtimes will support opset-9.
+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```.
 
 ### TensorFlow
 
-We support ```tf-1.x graphs``` and ```tf-2.x```. To keep our test matrix manageable we test tf2onnx running on top of ```tf-1.12 or better```.
+We support ```tf-1.x graphs``` and ```tf-2.x```. To keep our test matrix manageable we test tf2onnx running on top of ```tf-1.13 or better```.
 
 When running under tf-2.x tf2onnx will use the tensorflow V2 controlflow.
 
 You can install tf2onnx on top of tf-1.x or tf-2.x.
 
 ### Python
 
-We support Python ```3.6-3.9```.
-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.
+We support Python ```3.7-3.10```.
 
 ## 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:
 
@@ -98,9 +97,9 @@ To get started with `tensorflow-onnx`, run the `t2onnx.convert` command, providi
 
 ```python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx```
 
-The above command uses a default of `9` 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).
+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 13 --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.
 
@@ -118,7 +117,7 @@ You find an end-to-end tutorial for ssd-mobilenet [here](tutorials/ConvertingSSD
 
 We recently added support for tflite. You convert ```tflite``` models via command line, for example:
 
-```python -m tf2onnx.convert --opset 13 --tflite tflite--file --output model.onnx```
+```python -m tf2onnx.convert --opset 16 --tflite tflite--file --output model.onnx```
 
 ## CLI reference
 
@@ -187,7 +186,7 @@ ONNX requires default values for graph inputs to be constant, while Tensorflow's
 
 #### --opset
 
-By default we use the opset 9 to generate the graph. By specifying ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 13``` would create a onnx graph that uses only ops available in opset 13. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
+By default we use the opset 13 to generate the graph. By specifying ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 17``` would create a onnx graph that uses only ops available in opset 17. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
 
 #### --dequantize
 
@@ -268,7 +267,7 @@ optional arguments:
 ```
 ```run_pretrained_models.py``` will run the TensorFlow model, captures the TensorFlow output and runs the same test against the specified ONNX backend after converting the model.
 
-If the option ```--perf csv-file``` is specified, we'll capture the timeing for inferece of tensorflow and onnx runtime and write the result into the given csv file.
+If the option ```--perf csv-file``` is specified, we'll capture the timing for inference of tensorflow and onnx runtime and write the result into the given csv file.
 
 You call it for example with:
 ```
@@ -292,8 +291,8 @@ import tf2onnx
 model_proto, external_tensor_storage = tf2onnx.convert.from_keras(model,
                 input_signature=None, opset=None, custom_ops=None,
                 custom_op_handlers=None, custom_rewriter=None,
-                inputs_as_nchw=None, extra_opset=None shape_override=None,
-                target=None, large_model=False, output_path=None)
+                inputs_as_nchw=None, outputs_as_nchw=None, extra_opset=None,
+                shape_override=None, target=None, large_model=False, output_path=None)
 
     Args:
         model: the tf.keras model we want to convert
@@ -307,7 +306,8 @@ model_proto, external_tensor_storage = tf2onnx.convert.from_keras(model,
         custom_rewriter: list of custom graph rewriters
         extra_opset: list of extra opset's, for example the opset's used by custom ops
         shape_override: dict with inputs that override the shapes given by tensorflow
-        inputs_as_nchw: transpose inputs in list from nchw to nhwc
+        inputs_as_nchw: transpose inputs in list from nhwc to nchw
+        outputs_as_nchw: transpose outputs in list from nhwc to nchw
         large_model: use the ONNX external tensor storage format
         output_path: save model to output_path
 
@@ -323,8 +323,8 @@ import tf2onnx
 
 model_proto, external_tensor_storage = tf2onnx.convert.from_function(function,
                 input_signature=None, opset=None, custom_ops=None,
-                custom_op_handlers=None, custom_rewriter=None,
-                inputs_as_nchw=None, extra_opset=None, shape_override=None,
+                custom_op_handlers=None, custom_rewriter=None, inputs_as_nchw=None,
+                outputs_as_nchw=None, extra_opset=None, shape_override=None,
                 target=None, large_model=False, output_path=None)
 
     Args:
@@ -339,7 +339,8 @@ model_proto, external_tensor_storage = tf2onnx.convert.from_function(function,
         custom_rewriter: list of custom graph rewriters
         extra_opset: list of extra opset's, for example the opset's used by custom ops
         shape_override: dict with inputs that override the shapes given by tensorflow
-        inputs_as_nchw: transpose inputs in list from nchw to nhwc
+        inputs_as_nchw: transpose inputs in list from nhwc to nchw
+        outputs_as_nchw: transpose outputs in list from nhwc to nchw
         large_model: use the ONNX external tensor storage format
         output_path: save model to output_path
 
@@ -354,7 +355,7 @@ import tf2onnx
 model_proto, external_tensor_storage = tf2onnx.convert.from_graph_def(graph_def,
                 name=None, input_names=None, output_names=None, opset=None,
                 custom_ops=None, custom_op_handlers=None, custom_rewriter=None, 
-                inputs_as_nchw=None, extra_opset=None,
+                inputs_as_nchw=None, outputs_as_nchw=None, extra_opset=None,
                 shape_override=None, target=None, large_model=False,
                 output_path=None)
 
@@ -369,7 +370,39 @@ model_proto, external_tensor_storage = tf2onnx.convert.from_graph_def(graph_def,
         custom_rewriter: list of custom graph rewriters
         extra_opset: list of extra opset's, for example the opset's used by custom ops
         shape_override: dict with inputs that override the shapes given by tensorflow
-        inputs_as_nchw: transpose inputs in list from nchw to nhwc
+        inputs_as_nchw: transpose inputs in list from nhwc to nchw
+        outputs_as_nchw: transpose outputs in list from nhwc to nchw
+        large_model: use the ONNX external tensor storage format
+        output_path: save model to output_path
+
+    Returns:
+        An ONNX model_proto and an external_tensor_storage dict.
+```
+
+### from_tflite
+```
+import tf2onnx
+
+model_proto, external_tensor_storage = tf2onnx.convert.from_tflite(tflite_path,
+                input_names=None, output_names=None, opset=None, custom_ops=None, custom_op_handlers=None,
+                custom_rewriter=None, inputs_as_nchw=None, outputs_as_nchw=None, extra_opset=None,
+                shape_override=None, target=None, large_model=False, output_path=None):
+
+    Args:
+        tflite_path: the tflite model file full path
+        input_names: list of input names
+        output_names: list of output names
+        opset: the opset to be used for the ONNX model, default is the latest
+        custom_ops: if a model contains ops not recognized by onnx runtime,
+            you can tag these ops with a custom op domain so that the
+            runtime can still open the model. Type is a dictionary `{op name: domain}`.
+        custom_op_handlers: dictionary of custom ops handlers
+        custom_rewriter: list of custom graph rewriters
+        inputs_as_nchw: transpose inputs in list from nhwc to nchw
+        outputs_as_nchw: transpose outputs in list from nhwc to nchw
+        extra_opset: list of extra opset's, for example the opset's used by custom ops
+        shape_override: dict with inputs that override the shapes given by tensorflow
+        target: list of workarounds applied to help certain platforms
         large_model: use the ONNX external tensor storage format
         output_path: save model to output_path
 

Troubleshooting.md

@@ -18,11 +18,11 @@ To get this fixed you can open an issue or send us a PR with a fix.
 
 Sometimes there is no direct mapping from tensorflow to ONNX. We took care are of the most common cases. But for less frequently used ops there might be a mapping missing. To get this fixed there 2 options:
 
-a) in tf2onnx you can compose the op out of different ops. A good example for this the [Erf op](https://github.com/onnx/tensorflow-onnx/blob/master/tf2onnx/onnx_opset/math.py#L317). Before opset-9 this tf2onnx composes Erf with other ONNX ops.
+a) in tf2onnx you can compose the op out of different ops. A good example for this the [Erf op](https://github.com/onnx/tensorflow-onnx/blob/main/tf2onnx/onnx_opset/math.py#L317). Before opset-9 this tf2onnx composes Erf with other ONNX ops.
 
 b) You request the missing op to be added to [ONNX](https://github.com/onnx/onnx). After it is added to ONNX and some runtime implements it we'll add it to tf2onnx. You can see that this happened for the Erf Op. Starting with opset-9, ONNX added it - tf2onnx no longer composes the op and instead passes it to ONNX.
 
-c) The op is too complex to compose and it's to exotic to add to ONNX. In that cases you can use a custom op to implement it. Custom ops are documented in the [README](README.md) and there is an example [here](https://github.com/onnx/tensorflow-onnx/blob/master/examples/custom_op_via_python.py). There are 2 flavors of it:
+c) The op is too complex to compose and it's to exotic to add to ONNX. In that cases you can use a custom op to implement it. Custom ops are documented in the [README](README.md) and there is an example [here](https://github.com/onnx/tensorflow-onnx/blob/main/examples/custom_op_via_python.py). There are 2 flavors of it:
 - you could compose the functionality by using multiple ONNX ops.
 - you can implement the op in your runtime as custom op (assuming that most runtimes do have such a mechanism) and then map it in tf2onnx as custom op.
 
@@ -31,7 +31,7 @@ c) The op is too complex to compose and it's to exotic to add to ONNX. In that c
 There is a common group of errors that reports ```get tensor value: ... must be Const```.
 The reason for this is that there is a dynamic input of a tensorflow op but the equivalent ONNX op uses a static attribute. In other words in tensorflow that input is only known at runtime but in ONNX it need to be known at graph creation time.
 
-An example of this is the [ONNX Slice operator before opset-10](https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Slice-1) - the start and end of the slice are static attributes that need to be known at graph creation. In tensorflow the [strided slice op](https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/strided-slice) allows dynamic inputs. tf2onnx will try to find the real value of begin and end of the slice and can find them in most cases. But if those are real dynamic values calculate at runtime it will result in the message ```get tensor value: ... must be Const```.
+An example of this is the [ONNX Slice operator before opset-10](https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Slice-1) - the start and end of the slice are static attributes that need to be known at graph creation. In tensorflow the [strided slice op](https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/strided-slice) allows dynamic inputs. tf2onnx will try to find the real value of begin and end of the slice and can find them in most cases. But if those are real dynamic values calculate at runtime it will result in the message ```get tensor value: ... must be Const```.
 
 You can pass the options ```--fold_const```(removed after tf2onnx-1.9.3) in the tf2onnx command line that allows tf2onnx to apply more aggressive constant folding which will increase chances to find a constant.
 

VERSION_NUMBER

@@ -1 +1 @@
-1.10.0
+1.12.0