def test_clone_model():
# Create an original model
shape = [2, 2]
parameter_a = ops.parameter(shape, dtype=np.float32, name="A")
parameter_b = ops.parameter(shape, dtype=np.float32, name="B")
model_original = ov.Model(parameter_a + parameter_b,
[parameter_a, parameter_b])
# Make copies of it
model_copy1 = ov.utils.clone_model(model_original)
model_copy2 = model_original.clone()
# Make changes to the copied models' inputs
model_copy1.reshape({"A": [3, 3], "B": [3, 3]})
model_copy2.reshape({"A": [3, 3], "B": [3, 3]})
original_model_shapes = [
single_input.get_shape() for single_input in model_original.inputs
]
model_copy1_shapes = [
single_input.get_shape() for single_input in model_copy1.inputs
]
model_copy2_shapes = [
single_input.get_shape() for single_input in model_copy2.inputs
]
assert original_model_shapes != model_copy1_shapes
assert original_model_shapes != model_copy2_shapes
assert model_copy1_shapes == model_copy2_shapes
def create_model():
# This example shows how to create ov::Function
#
# To construct a model, please follow
# https://docs.openvino.ai/latest/openvino_docs_OV_UG_Model_Representation.html
data = ov.opset8.parameter([3, 1, 2], ov.Type.f32)
res = ov.opset8.result(data)
return ov.Model([res], [data], "model")
def create_simple_model():
# This example shows how to create ov::Function
#
# Parameter--->Multiply--->Add--->Result
# Constant---' /
# Constant---'
data = ov.opset8.parameter([3, 1, 2], ov.Type.f32)
mul_constant = ov.opset8.constant([1.5], ov.Type.f32)
mul = ov.opset8.multiply(data, mul_constant)
add_constant = ov.opset8.constant([0.5], ov.Type.f32)
add = ov.opset8.add(mul, add_constant)
res = ov.opset8.result(add)
return ov.Model([res], [data], "model")
def create_advanced_model():
# Advanced example with multi output operation
#
# Parameter->Split---0-->Result
# | `--1-->Relu-->Result
# `----2-->Result
data = ov.opset8.parameter(ov.Shape([1, 3, 64, 64]), ov.Type.f32)
# Create Constant for axis value
axis_const = ov.opset8.constant(ov.Type.i64, ov.Shape({}), [1])
# Create opset8::Split operation that splits input to three slices across 1st dimension
split = ov.opset8.split(data, axis_const, 3)
# Create opset8::Relu operation that takes 1st Split output as input
relu = ov.opset8.relu(split.output(1))
# Results operations will be created automatically based on provided OutputVector
return ov.Model([split.output(0), relu, split.output[2]], [data], "model")
# | Concat | # | concat | # |____________| # | # | concat_t # | # _______|_______ # | Result | # | result | # |_____________| import openvino.runtime as ov data1 = ov.opset8.parameter([1, 3, 2, 2], ov.Type.i64) data1.friendly_name = "data1" # operation name data1.output(0).name = "data1_t" # tensor name data2 = ov.opset8.parameter([1, 2, 2, 2], ov.Type.i64) data2.friendly_name = "data2" # operation name data2.output(0).name = "data2_t" # tensor name concat = ov.opset8.concat([data1, data2], 1) concat.friendly_name = "concat" # operation name concat.output(0).name = "concat_t" # tensor name result = ov.opset8.result(concat) result.friendly_name = "result" # operation name model = ov.Model(result, [data1, data2], "model_name") #! [ov:graph]
# SPDX-License-Identifier: Apache-2.0
import numpy as np
#! [auto_compilation]
import openvino.runtime as ov
compiled_model = ov.compile_model("model.xml")
#! [auto_compilation]
#! [properties_example]
core = ov.Core()
input_a = ov.opset8.parameter([8])
res = ov.opset8.absolute(input_a)
model = ov.Model(res, [input_a])
compiled = core.compile_model(model, "CPU")
print(model.inputs)
print(model.outputs)
print(compiled.inputs)
print(compiled.outputs)
#! [properties_example]
#! [tensor_basics]
data_float64 = np.ones(shape=(2, 8))
tensor = ov.Tensor(data_float64)
assert tensor.element_type == ov.Type.f64