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update docs and code on https://github.com/onnx/keras-onnx no more supported #387

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pacospace opened this issue Sep 14, 2021 · 4 comments
Closed

update docs and code on https://github.com/onnx/keras-onnx no more supported #387

pacospace opened this issue Sep 14, 2021 · 4 comments
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@bfineran
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bug Something isn't working

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@pacospace
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pacospace commented Sep 14, 2021 •
edited

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"

{
    "_meta": {
        "hash": {
            "sha256": "069674fd2b1303856705f9458bbfbf53bcc634401d9dc7391276a1cffadad39a"
        },
        "pipfile-spec": 6,
        "requires": {
            "python_version": "3.8"
        },
        "sources": [
            {
                "name": "pypi",
                "url": "https://pypi.org/simple",
                "verify_ssl": true
            }
        ]
    },
    "default": {
        "absl-py": {
            "hashes": [
                "sha256:62bd4e248ddb19d81aec8f9446b407ff37c8175c2ba88266a7afa9b4ce4a333b",
                "sha256:6953272383486044699fd0e9f00aad167a27e08ce19aae66c6c4b10e7e767793"
            ],
            "version": "==0.13.0"
        },
        "argon2-cffi": {
            "hashes": [
                "sha256:165cadae5ac1e26644f5ade3bd9c18d89963be51d9ea8817bd671006d7909057",
                "sha256:217b4f0f853ccbbb5045242946ad2e162e396064575860141b71a85eb47e475a",
                "sha256:245f64a203012b144b7b8c8ea6d468cb02b37caa5afee5ba4a10c80599334f6a",
                "sha256:4ad152c418f7eb640eac41ac815534e6aa61d1624530b8e7779114ecfbf327f8",
                "sha256:566ffb581bbd9db5562327aee71b2eda24a1c15b23a356740abe3c011bbe0dcb",
                "sha256:65213a9174320a1aee03fe826596e0620783966b49eb636955958b3074e87ff9",
                "sha256:bc513db2283c385ea4da31a2cd039c33380701f376f4edd12fe56db118a3b21a",
                "sha256:c7a7c8cc98ac418002090e4add5bebfff1b915ea1cb459c578cd8206fef10378",
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                "sha256:f710b61103d1a1f692ca3ecbd1373e28aa5e545ac625ba067ff2feca1b2bb870",
                "sha256:fa7e7d1fc22514a32b1761fdfa1882b6baa5c36bb3ef557bdd69e6fc9ba14a41"
            ],
            "markers": "python_version >= '3.5'",
            "version": "==21.1.0"
        },
        "astunparse": {
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            "version": "==1.6.3"
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            "version": "==21.2.0"
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        "wheel": {
            "hashes": [
                "sha256:21014b2bd93c6d0034b6ba5d35e4eb284340e09d63c59aef6fc14b0f346146fd",
                "sha256:e2ef7239991699e3355d54f8e968a21bb940a1dbf34a4d226741e64462516fad"
            ],
            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
            "version": "==0.37.0"
        },
        "widgetsnbextension": {
            "hashes": [
                "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7",
                "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"
            ],
            "version": "==3.5.1"
        },
        "wrapt": {
            "hashes": [
                "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7"
            ],
            "version": "==1.12.1"
        }
    },
    "develop": {}
}
  1. 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 <module>
      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 <module>
     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 <module>
     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 <module>
     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 <module>
      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 <module>
     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 <module>
     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 pacospace added the bug Something isn't working label Sep 14, 2021
@pacospace pacospace mentioned this issue Sep 14, 2021
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@pacospace
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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.

@pacospace pacospace changed the title fail to convert model to ONNX update docs on https://github.com/onnx/keras-onnx no more supported Sep 14, 2021
@pacospace pacospace changed the title update docs on https://github.com/onnx/keras-onnx no more supported update docs and code on https://github.com/onnx/keras-onnx no more supported Sep 15, 2021
@markurtz
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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
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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
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bfineran commented Feb 1, 2023

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.

@bfineran bfineran closed this as completed Feb 1, 2023
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