update docs and code on https://github.com/onnx/keras-onnx no more supported

[pacospace]

Describe the bug fail to convert model to ONNX

Expected behavior model is converted to ONNX

Environment Include all relevant environment information:

1. OS [e.g. Ubuntu 18.04]: fedora 33
2. Python version [e.g. 3.7]: 3.9
3. SparseML version or commit hash [e.g. 0.1.0, f7245c8]: 0.7.0
4. ML framework version(s) [e.g. torch 1.7.1]: tensorflow 2.6.0
5. Other Python package versions [e.g. SparseZoo, DeepSparse, numpy, ONNX]:

   
   [[source]]
   url = "https://pypi.org/simple"
   verify_ssl = true
   name = "pypi"

[packages] tensorflow = "" numpy = "" matplotlib = "" boto3 = "" sparseml = "" keras2onnx = ""

[dev-packages]

[requires] python_version = "3.8"

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6. Other relevant environment information [e.g. hardware, CUDA version]: 

  # Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz
  cpu_family: 6
  cpu_model: 142

**To Reproduce**
1. pip install micropipenv
2. python3 -m venv venv/ && . venv/bin/activate
3. Create Pipfile and Pipfile.lock files using data above
4. micropipenv install
5. python3 scrip.py with the content provided below

import tensorflow as tf

Importing the required Keras modules containing model and layers

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import ( Dense, Conv2D, Dropout, Flatten, MaxPooling2D, )

MNIST dataset parameters.

num_classes = 10 # total classes (0-9 digits).

Training parameters.

batch_size = 128 display_step = 10

Network parameters.

conv1_filters = 32 # number of filters for 1st conv layer. conv2_filters = 64 # number of filters for 2nd conv layer. fc1_units = 1024 # number of neurons for 1st fully-connected layer.

input_shape = (28, 28, 1)

Creating a Sequential Model and adding the layers

conv_net = Sequential() conv_net.add( Conv2D( conv1_filters, kernel_size=(5, 5), padding="same", activation="relu", input_shape=input_shape, ) ) conv_net.add(MaxPooling2D(pool_size=(2, 2))) conv_net.add( Conv2D( conv2_filters, kernel_size=(3, 3), padding="same", activation="relu" ) ) conv_net.add(MaxPooling2D(pool_size=(2, 2))) conv_net.add(Flatten()) # Flattening the 2D arrays for fully connected layers conv_net.add(Dense(fc1_units, activation=tf.nn.relu)) conv_net.add(Dropout(0.5)) conv_net.add(Dense(num_classes, activation=tf.nn.softmax))

conv_net.compile( optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"], )

import os from sparseml.keras.utils import ModelExporter

model = conv_net # fill in with your model exporter = ModelExporter(model, output_dir=os.path.join(".", "onnx-export")) exporter.export_onnx()

**Errors**

---

AttributeError Traceback (most recent call last) /tmp/ipykernel_1459245/4273403008.py in 4 model = conv_net # fill in with your model 5 exporter = ModelExporter(model, output_dir=os.path.join(".", "onnx-export")) ----> 6 exporter.export_onnx()

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/sparseml/keras/utils/exporter.py in export_onnx(self, name, opset, doc_string, debug_mode, raise_on_tf_support, **kwargs) 74 """ 75 if keras2onnx_import_error is not None: ---> 76 raise keras2onnx_import_error 77 78 if raise_on_tf_support:

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/sparseml/keras/utils/exporter.py in 25 26 try: ---> 27 import keras2onnx 28 29 keras2onnx_import_error = None

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/init.py in 32 from .funcbook import set_converter, set_converters 33 ---> 34 from .main import convert_keras 35 from .main import export_tf_frozen_graph 36 from .main import build_io_names_tf2onnx

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/main.py in 11 from .proto import onnx 12 from .topology import convert_topology ---> 13 from .ke2onnx import static_set_ke2onnx_converters 14 from .parser import parse_graph, parse_graph_modeless 15 from .topology import Topology

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/init.py in 4 # license information. 5 ############################################################################### ----> 6 from .main import keras_layer_to_operator 7 from .main import static_set_ke2onnx_converters 8 from .layer_spec import keras_layer_spec

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/main.py in 11 from ..common.onnx_ops import apply_reshape, apply_concat, apply_transpose, apply_flatten, OnnxOperatorBuilder 12 ---> 13 from .activation import convert_keras_activation 14 from .adv_activation import convert_keras_advanced_activation 15 from .batch_norm import convert_keras_batch_normalization

~/.local/share/thoth/kernels/convert-onnx/lib/python3.9/site-packages/keras2onnx/ke2onnx/activation.py in 19 except ImportError: 20 pass ---> 21 if not relu6 and hasattr(keras.applications.mobilenet, 'relu6'): 22 relu6 = keras.applications.mobilenet.relu6 23

AttributeError: module 'tensorflow.python.keras' has no attribute 'applications'

**Additional context**
Following doc from: https://docs.neuralmagic.com/sparseml/source/onnx_export#exporting-keras-to-onnx

[pacospace]

The cause seems to be the package itself that you suggest to install, that works only with certain version of tf and onnx. Docs should be updated to use https://github.com/onnx/tensorflow-onnx.

from https://github.com/onnx/keras-onnx

We stopped active development of keras2onnx and keras2onnx is now frozen to tf-2.3 and onnx-1.10.
To convert your Keras models you can head over to tf2onnx which can convert Tensorflow, Keras, Tflite and Tensorflow.js models. All keras2onnx unit tests have been added to the tf2onnx ci pipeline to make sure there are no avoidable regressions.

[markurtz]

Hi @pacospace, thank you for raising this to our attention. We'll work on the migration of the ONNX export flows for Keras since that is now deprecated along with the docs. I'll update here once that migration is complete. In the interim, our current Keras export flows are tested with the older versions, so if you're willing to downgrade those should continue to work for you.

Thanks, Mark

[pacospace]

Hi @pacospace, thank you for raising this to our attention. We'll work on the migration of the ONNX export flows for Keras since that is now deprecated along with the docs. I'll update here once that migration is complete. In the interim, our current Keras export flows are tested with the older versions, so if you're willing to downgrade those should continue to work for you.

Thanks, Mark

Hi @markurtz, happy to contribute to it. I actually switched to Pytorch where everything works perfectly and managed to deploy a pruned Neural Magic model with deepsparse!

[bfineran]

Closing this issue now. @pacospace great to hear - the keras export migration ended up not landing as keras fell off the roadmap, but happy to take a look at any contributions if you still have them handy.