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Upgrade to opset 14 (#723)
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jiafatom authored Jun 9, 2021
1 parent 383e431 commit f044d7c
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Showing 10 changed files with 21 additions and 19 deletions.
4 changes: 2 additions & 2 deletions .azure-pipelines/linux-CI-keras-applications-nightly.yml
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Expand Up @@ -38,7 +38,7 @@ jobs:

Python37:
python.version: '3.7.3'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
INSTALL_KERAS: pip install keras==2.3.1
UNINSTALL_KERAS:
INSTALL_TENSORFLOW: pip install tensorflow==1.15.0
Expand All @@ -60,7 +60,7 @@ jobs:

Python38-tf2:
python.version: '3.8'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
INSTALL_KERAS:
UNINSTALL_KERAS: pip uninstall keras -y
INSTALL_TENSORFLOW: pip install tensorflow==2.2.0
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6 changes: 3 additions & 3 deletions .azure-pipelines/linux-conda-CI-tf-keras.yml
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Expand Up @@ -36,13 +36,13 @@ jobs:
python.version: '3.8'
ONNX_PATH: onnx==1.7.0
TENSORFLOW_PATH: tensorflow-cpu==2.2.0
INSTALL_ORT:
INSTALL_ORT: pip install onnxruntime==1.6.0

Python38-tf2.3:
python.version: '3.8'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
TENSORFLOW_PATH: tensorflow-cpu==2.3.0
INSTALL_ORT: pip install onnxruntime==1.7.0
INSTALL_ORT: pip install onnxruntime==1.8.0

maxParallel: 3

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4 changes: 2 additions & 2 deletions .azure-pipelines/linux-conda-CI.yml
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Expand Up @@ -29,10 +29,10 @@ jobs:

Python37-tf1150:
python.version: '3.7'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
KERAS: keras==2.4.3
TENSORFLOW_PATH: tensorflow==1.15.0
INSTALL_ORT: pip install onnxruntime==1.6.0
INSTALL_ORT: pip install onnxruntime==1.8.0

# UT for keras 2.3 need tensorflow <= 2.0.0
Python37-tf200:
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6 changes: 3 additions & 3 deletions .azure-pipelines/win32-CI-keras-applications-nightly.yml
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Expand Up @@ -39,7 +39,7 @@ jobs:

Python37:
python.version: '3.7'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
INSTALL_KERAS: pip install keras==2.3.1
UNINSTALL_KERAS:
INSTALL_TENSORFLOW: pip install tensorflow==1.14.0
Expand All @@ -61,11 +61,11 @@ jobs:

Python38-tf2:
python.version: '3.8'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
INSTALL_KERAS:
UNINSTALL_KERAS: pip uninstall keras -y
INSTALL_TENSORFLOW: pip install tensorflow==2.2.0
INSTALL_ORT: pip install onnxruntime==1.7.0
INSTALL_ORT: pip install onnxruntime==1.8.0
INSTALL_KERAS_RESNET: pip install keras-resnet
INSTALL_TRANSFORMERS: pip install transformers==3.4.0
NIGHTLY_BUILD_TEST: python run_all_v2.py
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6 changes: 3 additions & 3 deletions .azure-pipelines/win32-conda-CI-tf-keras.yml
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Expand Up @@ -36,13 +36,13 @@ jobs:
python.version: '3.8'
ONNX_PATH: onnx==1.7.0
TENSORFLOW_PATH: tensorflow-cpu==2.2.0
INSTALL_ORT:
INSTALL_ORT: pip install onnxruntime==1.6.0

Python38-tf2.3:
python.version: '3.8'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
TENSORFLOW_PATH: tensorflow-cpu==2.3.0
INSTALL_ORT: pip install onnxruntime==1.7.0
INSTALL_ORT: pip install onnxruntime==1.8.0

maxParallel: 3

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4 changes: 2 additions & 2 deletions .azure-pipelines/win32-conda-CI.yml
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Expand Up @@ -38,10 +38,10 @@ jobs:

Python38-tf220:
python.version: '3.8'
ONNX_PATH: onnx==1.8.0
ONNX_PATH: onnx==1.9.0
KERAS: keras==2.4.3
TENSORFLOW_PATH: tensorflow==2.2.0
INSTALL_ORT:
INSTALL_ORT: pip install onnxruntime==1.8.0

maxParallel: 3

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2 changes: 1 addition & 1 deletion keras2onnx/__init__.py
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Expand Up @@ -4,7 +4,7 @@
keras2onnx
This package converts keras models into ONNX for use with any inference engine supporting ONNX
"""
__version__ = "1.8.0"
__version__ = "1.9.0"
__author__ = "Microsoft Corporation"
__producer__ = "keras2onnx"

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3 changes: 2 additions & 1 deletion keras2onnx/_builtin.py
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Expand Up @@ -796,7 +796,8 @@ def _convert_tf_fused_batch_norm_core(scope, operator, container):
input_dim = len(_cal_tensor_shape(node.inputs[0]))
epsilon = node.get_attr('epsilon')
attrs = {'epsilon': epsilon, 'momentum': 0.9, 'spatial': 1}
outputs_num = min(5, len(node.outputs))
# ORT assumes opitonal outputs indicate training mode. So we should use one output for inference.
outputs_num = 1

if _is_nhwc(node):
input_perm = [0, input_dim - 1] + list(range(1, input_dim - 1))
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3 changes: 2 additions & 1 deletion keras2onnx/ke2onnx/batch_norm.py
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Expand Up @@ -65,7 +65,8 @@ def convert_keras_batch_normalization(scope, operator, container):

if skip_transpose:
# If no transpose is required, we can simply use the output of ONNX BatchNorm as the final outcome
apply_batch_norm(scope, input_tensor_names, operator.output_full_names[0:5], container,
# ORT assumes opitonal outputs indicate training mode. So we should use one output for inference.
apply_batch_norm(scope, input_tensor_names, operator.output_full_names[0], container,
operator_name=operator.full_name, epsilon=epsilon, is_test=is_test,
momentum=momentum, spatial=spatial)
else:
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2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -2,5 +2,5 @@ numpy
protobuf
requests
onnx
onnxconverter-common>=1.8.0
onnxconverter-common>=1.9.0
fire

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