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Set default_batch_size to 'None' (#339)
* Set default_batch_size to 'None' * Set default_batch_size to 'None'
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+36
-37
lines changed

15 files changed

+36
-37
lines changed

‎.azure-pipelines/linux-CI-nightly.yml‎

Lines changed: 1 addition & 1 deletion
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@@ -54,7 +54,7 @@ jobs:
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python -c "import svmutil"
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python -c "import onnxconverter_common"
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test '$(python.version)' != '2.7' && python -c "import onnxruntime"
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pytest tests --doctest-modules --junitxml=junit/test-results.xml
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pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
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displayName: 'pytest - onnxmltools'
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- task: PublishTestResults@2

‎.azure-pipelines/linux-conda-CI.yml‎

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@@ -61,7 +61,7 @@ jobs:
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python -c "import svmutil"
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python -c "import onnxconverter_common"
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test '$(python.version)' != '2.7' && python -c "import onnxruntime"
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pytest tests --doctest-modules --junitxml=junit/test-results.xml
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pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
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displayName: 'pytest - onnxmltools'
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- script: |

‎.azure-pipelines/win32-CI-nightly.yml‎

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@@ -58,7 +58,7 @@ jobs:
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call activate py$(python.version)
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set PYTHONPATH=libsvm\python;%PYTHONPATH%
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pip install -e .
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pytest tests --doctest-modules --junitxml=junit/test-results.xml
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pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
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displayName: 'pytest - onnxmltools'
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- task: PublishTestResults@2

‎.azure-pipelines/win32-conda-CI.yml‎

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@@ -71,7 +71,7 @@ jobs:
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call activate py$(python.version)
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set PYTHONPATH=libsvm\python;%PYTHONPATH%
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pip install -e .
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pytest tests --doctest-modules --junitxml=junit/test-results.xml
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pytest tests --ignore=tests/sparkml --doctest-modules --junitxml=junit/test-results.xml
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displayName: 'pytest - onnxmltools'
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- task: PublishTestResults@2

‎onnxmltools/convert/coreml/_parse.py‎

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -448,8 +448,7 @@ def parse_coreml(model, initial_types=None, target_opset=None, custom_conversion
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# Determine the batch size for parsing CoreML model's input and output features. Note that batch size is always
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# missing in all CoreML models.
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default_batch_size = 1 if model.WhichOneof('Type') not in \
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['neuralNetworkClassifier', 'neuralNetworkRegressor', 'neuralNetwork'] else 'None'
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default_batch_size = 'None'
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# Topology is shared by both of CoreML and scikit-learn conversion frameworks, so we have a wrapper class,
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# CoremlModelContainer, to make sure our topology-related functions can seamlessly handle both of CoreML and

‎onnxmltools/convert/libsvm/_parse.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -58,7 +58,7 @@ def parse_libsvm(model, initial_types=None, target_opset=None,
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# Declare a computational graph. It will become a representation of
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# the input scikit-learn model after parsing.
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topology = Topology(raw_model_container,
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topology = Topology(raw_model_container, default_batch_size='None',
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initial_types=initial_types,
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target_opset=target_opset,
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custom_conversion_functions=custom_conversion_functions,

‎onnxmltools/convert/lightgbm/_parse.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -104,7 +104,7 @@ def parse_lightgbm(model, initial_types=None, target_opset=None,
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custom_conversion_functions=None, custom_shape_calculators=None):
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raw_model_container = LightGbmModelContainer(model)
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topology = Topology(raw_model_container,
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topology = Topology(raw_model_container, default_batch_size='None',
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initial_types=initial_types, target_opset=target_opset,
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custom_conversion_functions=custom_conversion_functions,
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custom_shape_calculators=custom_shape_calculators)

‎onnxmltools/convert/sparkml/_parse.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -96,7 +96,7 @@ def parse_sparkml(spark, model, initial_types=None, target_opset=None,
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raw_model_container = SparkmlModelContainer(model)
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# Declare a computational graph. It will become a representation of the input spark-ml model after parsing.
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topology = Topology(raw_model_container,
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topology = Topology(raw_model_container, default_batch_size='None',
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initial_types=initial_types,
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target_opset=target_opset,
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custom_conversion_functions=custom_conversion_functions,

‎onnxmltools/convert/xgboost/_parse.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -70,7 +70,7 @@ def parse_xgboost(model, initial_types=None, target_opset=None,
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custom_conversion_functions=None, custom_shape_calculators=None):
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raw_model_container = XGBoostModelContainer(model)
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topology = Topology(raw_model_container,
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topology = Topology(raw_model_container, default_batch_size='None',
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initial_types=initial_types, target_opset=target_opset,
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custom_conversion_functions=custom_conversion_functions,
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custom_shape_calculators=custom_shape_calculators)

‎onnxmltools/utils/tests_helper.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -224,7 +224,7 @@ def dump_one_class_classification(model, suffix="", folder=None, allow_failure=N
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X = numpy.array(X, dtype=numpy.float32)
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y = [1, 1, 1]
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model.fit(X, y)
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model_onnx, prefix = convert_model(model, 'one_class', [('input', FloatTensorType([1, 2]))])
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model_onnx, prefix = convert_model(model, 'one_class', [('input', FloatTensorType(['None', 2]))])
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return dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
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basename=prefix + "One" + model.__class__.__name__ + suffix)
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@@ -250,7 +250,7 @@ def dump_binary_classification(model, suffix="", folder=None, allow_failure=None
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X = numpy.array(X, dtype=numpy.float32)
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y = [0, 1, 0]
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model.fit(X, y)
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model_onnx, prefix = convert_model(model, 'tree-based binary classifier', [('input', FloatTensorType([1, 2]))])
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model_onnx, prefix = convert_model(model, 'tree-based binary classifier', [('input', FloatTensorType(['None', 2]))])
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dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
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basename=prefix + "Bin" + model.__class__.__name__ + suffix,
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verbose=verbose)
@@ -275,7 +275,7 @@ def dump_multiple_classification(model, suffix="", folder=None, allow_failure=No
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X = numpy.array(X, dtype=numpy.float32)
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y = [0, 1, 2, 1, 1, 2]
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model.fit(X, y)
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model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([1, 2]))])
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model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType(['None', 2]))])
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dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
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basename=prefix + "Mcl" + model.__class__.__name__ + suffix)
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@@ -300,7 +300,7 @@ def dump_multiple_regression(model, suffix="", folder=None, allow_failure=None):
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X = numpy.array(X, dtype=numpy.float32)
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y = numpy.array([[100, 50], [100, 49], [100, 99]], dtype=numpy.float32)
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model.fit(X, y)
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model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType([1, 2]))])
303+
model_onnx, prefix = convert_model(model, 'tree-based multi-output regressor', [('input', FloatTensorType(['None', 2]))])
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dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
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basename=prefix + "MRg" + model.__class__.__name__ + suffix)
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@@ -326,7 +326,7 @@ def dump_single_regression(model, suffix="", folder=None, allow_failure=None):
326326
X = numpy.array(X, dtype=numpy.float32)
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y = numpy.array([100, -10, 50], dtype=numpy.float32)
328328
model.fit(X, y)
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model_onnx, prefix = convert_model(model, 'tree-based regressor', [('input', FloatTensorType([1, 2]))])
329+
model_onnx, prefix = convert_model(model, 'tree-based regressor', [('input', FloatTensorType(['None', 2]))])
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dump_data_and_model(X, model, model_onnx, folder=folder, allow_failure=allow_failure,
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basename=prefix + "Reg" + model.__class__.__name__ + suffix)
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