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test_input_node.py
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test_input_node.py
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# -*- coding: utf-8 -*-
# Copyright (C) 2018-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
import numpy as np
from openvino.runtime import Input, RTMap
from openvino._pyopenvino import DescriptorTensor
import openvino.runtime.opset13 as ops
from openvino import Core, OVAny, Shape, PartialShape, Type, Tensor, Symbol
from tests.utils.helpers import get_relu_model
def test_input_type(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
assert isinstance(input_node, Input)
def test_const_output_docs(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
exptected_string = "openvino.runtime.Input wraps ov::Input<Node>"
assert input_node.__doc__ == exptected_string
def test_input_get_index(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
assert input_node.get_index() == 0
def test_input_element_type(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
assert input_node.get_element_type() == Type.f32
def test_input_get_shape(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
assert str(input_node.get_shape()) == str(Shape([1, 3, 32, 32]))
def test_input_get_partial_shape(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
expected_partial_shape = PartialShape([1, 3, 32, 32])
assert input_node.get_partial_shape() == expected_partial_shape
def test_input_get_source_output(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
name = input_node.get_source_output().get_node().get_friendly_name()
assert name == "relu"
def test_input_get_tensor(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
tensor = input_node.get_tensor()
assert isinstance(tensor, DescriptorTensor)
def test_input_get_rt_info(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
rt_info = input_node.get_rt_info()
assert isinstance(rt_info, RTMap)
def test_input_rt_info(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
rt_info = input_node.rt_info
assert isinstance(rt_info, RTMap)
def test_input_replace_source_output(device):
param = ops.parameter([1, 64], Type.i64)
param.output(0).get_tensor().set_names({"a", "b"})
param1 = ops.parameter([1, 64], Type.i64)
param1.output(0).get_tensor().set_names({"c", "d"})
relu = ops.relu(param)
relu.input(0).replace_source_output(param1.output(0))
assert param.output(0).get_tensor().get_names() == {"a", "b"}
assert param1.output(0).get_tensor().get_names() == {"c", "d"}
def test_input_update_rt_info(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
net_input = compiled_model.output(0)
input_node = net_input.get_node().inputs()[0]
rt = input_node.get_rt_info()
rt["test12345"] = "test"
for key, value in input_node.get_rt_info().items():
assert key == "test12345"
assert isinstance(value, OVAny)
def test_tensor_bounds_in_model(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
tensor = compiled_model.output(0).get_tensor()
partial_shape = tensor.get_partial_shape().to_shape()
tensor_size = np.prod(partial_shape)
lower_value = np.zeros(tensor_size, dtype=np.float32)
lower_value_tensor = Tensor(lower_value.reshape(partial_shape))
upper_value = np.ones(tensor_size, dtype=np.float32)
upper_value_tensor = Tensor(upper_value.reshape(partial_shape))
tensor.set_lower_value(lower_value_tensor)
retrieved_lower_value = tensor.get_lower_value().data
tensor.set_upper_value(upper_value_tensor)
retrieved_upper_value = tensor.get_upper_value().data
assert np.array_equal(retrieved_lower_value, lower_value_tensor.data)
assert np.array_equal(retrieved_upper_value, upper_value_tensor.data)
def test_value_symbol_in_model(device):
core = Core()
model = get_relu_model()
compiled_model = core.compile_model(model, device)
tensor = compiled_model.output(0).get_tensor()
partial_shape = tensor.get_partial_shape().to_shape()
tensor_size = np.prod(partial_shape)
values = [Symbol() for _ in range(tensor_size)]
tensor.set_value_symbol(values)
assert tensor.get_value_symbol() == values