keras-onnx
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onnx
Convert Sequential model throws "Unsupported dimension type:...
Hi,
I have a binary classification model with this architecture: Built with tf.keras using tensorflow version 2.3.0
def create_model_architecture(input_dimension, layers=[96], act='relu', dr=[0.10], batch_size_value=None):
model = Sequential()
model.add(Input(shape=(input_dimension,), batch_size=batch_size_value))
model.add(Dense(layers[0], activation=act))
model.add(Dropout(dr[0]))
# create output layer
model.add(Dense(1, activation='sigmoid'))
return model
Converting to ONNX (using latest version 1.7.0):
keras_model_single_batch = create_model_architecture(113, batch_size_value=1)
keras_model_single_batch.set_weights(trained_weights)
onnx_model = keras2onnx.convert_keras(keras_model_single_batch)
throws "Unsupported dimension type:" error in data_types.py in method to_onnx_type. But my input shape is (1,113) in this case for the keras model so did not understand how that can be unsupported dimensions.
I updated the onnx conversion package code like below (had to set d = dim.value) and then conversion was successful:
def to_onnx_type(self):
onnx_type = onnx_proto.TypeProto()
onnx_type.tensor_type.elem_type = self._get_element_onnx_type()
for dim in self.shape:
d = dim.value
s = onnx_type.tensor_type.shape.dim.add()
if d is None:
pass
elif isinstance(d, numbers.Integral):
s.dim_value = d
elif isinstance(d, str):
s.dim_param = d
else:
raise ValueError('Unsupported dimension type: %s, see %s' % (
type(d), "https://github.com/onnx/onnx/blob/master/docs/IR.md#" +
"input--output-data-types"))
if getattr(onnx_type, 'denotation', None) is not None:
if self.denotation:
onnx_type.denotation = self.denotation
if self.channel_denotations:
for d, denotation in zip(onnx_type.tensor_type.shape.dim,
self.channel_denotations):
if denotation:
d.denotation = denotation
return onnx_type
Can anyone help explain this. Would rather not modify the package locally. Thanks for a great package!
ENV:
- virtualenv python 3.8
- onnx 1.8.0
- keras2onnx 1.7.0
- tensorflow 2.3.0
Same problem using on Windows: tensorflow==2.3.1 tensorflow-gpu==2.3.1 keras2onnx==1.7.0
