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Python API Overview

Loading an ONNX Model

import onnx

onnx_model = onnx.load('path/to/the/model.onnx')
# `onnx_model` is a ModelProto struct

Runnable IPython notebooks:

Saving an ONNX Model

import onnx

onnx_model = ... # Your model in memory as ModelProto

# Save the ONNX model
onnx.save(onnx_model, 'path/to/the/model.onnx')

Runnable IPython notebooks:

Manipulating TensorProto and Numpy Array

import numpy
import onnx
from onnx import numpy_helper

# Preprocessing: create a Numpy array
numpy_array = numpy.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=float)
print('Original Numpy array:\n{}\n'.format(numpy_array))

# Convert the Numpy array to a TensorProto
tensor = numpy_helper.from_array(numpy_array)
print('TensorProto:\n{}'.format(tensor))

# Convert the TensorProto to a Numpy array
new_array = numpy_helper.to_array(tensor)
print('After round trip, Numpy array:\n{}\n'.format(numpy_array))

# Save the TensorProto
with open('tensor.pb', 'wb') as f:
    f.write(tensor.SerializeToString())

# Load a TensorProto
new_tensor = onnx.TensorProto()
with open('tensor.pb', 'rb') as f:
    new_tensor.ParseFromString(f.read())
print('After saving and loading, new TensorProto:\n{}'.format(new_tensor))

Runnable IPython notebooks:

Creating an ONNX Model Using Helper Functions

import onnx
from onnx import helper
from onnx import AttributeProto, TensorProto, GraphProto


# The protobuf definition can be found here:
# https://github.com/onnx/onnx/blob/master/onnx/onnx.proto


# Create one input (ValueInfoProto)
X = helper.make_tensor_value_info('X', TensorProto.FLOAT, [1, 2])

# Create one output (ValueInfoProto)
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1, 4])

# Create a node (NodeProto)
node_def = helper.make_node(
    'Pad', # node name
    ['X'], # inputs
    ['Y'], # outputs
    mode='constant', # attributes
    value=1.5,
    pads=[0, 1, 0, 1],
)

# Create the graph (GraphProto)
graph_def = helper.make_graph(
    [node_def],
    'test-model',
    [X],
    [Y],
)

# Create the model (ModelProto)
model_def = helper.make_model(graph_def, producer_name='onnx-example')

print('The model is:\n{}'.format(model_def))
onnx.checker.check_model(model_def)
print('The model is checked!')

Runnable IPython notebooks:

Checking an ONNX Model

import onnx

# Preprocessing: load the ONNX model
model_path = 'path/to/the/model.onnx'
onnx_model = onnx.load(model_path)

print('The model is:\n{}'.format(onnx_model))

# Check the model
onnx.checker.check_model(onnx_model)
print('The model is checked!')

Runnable IPython notebooks:

Optimizing an ONNX Model

import onnx
from onnx import optimizer

# Preprocessing: load the model to be optimized.
model_path = 'path/to/the/model.onnx'
original_model = onnx.load(model_path)

print('The model before optimization:\n{}'.format(original_model))

# A full list of supported optimization passes can be found using get_available_passes()
all_passes = optimizer.get_available_passes()
print("Available optimization passes:")
for p in all_passes:
    print(p)
print()

# Pick one pass as example
passes = ['fuse_consecutive_transposes']

# Apply the optimization on the original model
optimized_model = optimizer.optimize(original_model, passes)

print('The model after optimization:\n{}'.format(optimized_model))

# One can also apply the default passes on the (serialized) model
# Check the default passes here: https://github.com/onnx/onnx/blob/master/onnx/optimizer.py#L43
optimized_model = optimizer.optimize(original_model)

Runnable IPython notebooks:

Running Shape Inference on an ONNX Model

import onnx
from onnx import helper, shape_inference
from onnx import TensorProto


# Preprocessing: create a model with two nodes, Y's shape is unknown
node1 = helper.make_node('Transpose', ['X'], ['Y'], perm=[1, 0, 2])
node2 = helper.make_node('Transpose', ['Y'], ['Z'], perm=[1, 0, 2])

graph = helper.make_graph(
    [node1, node2],
    'two-transposes',
    [helper.make_tensor_value_info('X', TensorProto.FLOAT, (2, 3, 4))],
    [helper.make_tensor_value_info('Z', TensorProto.FLOAT, (2, 3, 4))],
)

original_model = helper.make_model(graph, producer_name='onnx-examples')

# Check the model and print Y's shape information
onnx.checker.check_model(original_model)
print('Before shape inference, the shape info of Y is:\n{}'.format(original_model.graph.value_info))

# Apply shape inference on the model
inferred_model = shape_inference.infer_shapes(original_model)

# Check the model and print Y's shape information
onnx.checker.check_model(inferred_model)
print('After shape inference, the shape info of Y is:\n{}'.format(inferred_model.graph.value_info))

Runnable IPython notebooks:

Converting Version of an ONNX Model within Default Domain (""/"ai.onnx")

import onnx
from onnx import version_converter, helper

# Preprocessing: load the model to be converted.
model_path = 'path/to/the/model.onnx'
original_model = onnx.load(model_path)

print('The model before conversion:\n{}'.format(original_model))

# A full list of supported adapters can be found here:
# https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21
# Apply the version conversion on the original model
converted_model = version_converter.convert_version(original_model, <int target_version>)

print('The model after conversion:\n{}'.format(converted_model))

Utility Functions

Polishing the Model

Function polish_model runs model checker, optimizer, shape inference engine on the model, and also strips the doc_string for you.

import onnx
import onnx.utils


model = onnx.load('path/to/the/model.onnx')
polished_model = onnx.utils.polish_model(model)