0c9abf43a9
* Updated copyright headers
* Revert "Fixed linker warnings in docs snippets on Windows (#15119)"
This reverts commit 372699ec49.
282 lines
10 KiB
Python
282 lines
10 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.layer_test_class import check_ir_version
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from common.onnx_layer_test_class import OnnxRuntimeLayerTest
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from unit_tests.utils.graph import build_graph
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class TestOr(OnnxRuntimeLayerTest):
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def _prepare_input(self, inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
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return inputs_dict
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def create_net(self, shape1, shape2, ir_version):
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"""
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ONNX net IR net
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Input->Or with 2nd input->Output => Input->LogicalOr
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"""
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#
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# Create ONNX model
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#
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from onnx import helper
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from onnx import TensorProto
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input1 = helper.make_tensor_value_info('input1', TensorProto.BOOL, shape1)
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input2 = helper.make_tensor_value_info('input2', TensorProto.BOOL, shape2)
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output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1)
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node_def = helper.make_node(
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'Or',
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inputs=['input1', 'input2'],
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outputs=['output']
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_def],
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'test_model',
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[input1, input2],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model')
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# Create reference IR net
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ref_net = None
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if check_ir_version(10, None, ir_version):
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nodes_attributes = {
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'input1': {'kind': 'op', 'type': 'Parameter'},
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'input1_data': {'shape': shape1, 'kind': 'data'},
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'input2': {'kind': 'op', 'type': 'Parameter'},
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'input2_data': {'shape': shape2, 'kind': 'data'},
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'node': {'kind': 'op', 'type': 'LogicalOr'},
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'node_data': {'shape': shape1, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input1', 'input1_data'),
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('input2', 'input2_data'),
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('input1_data', 'node'),
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('input2_data', 'node'),
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('node', 'node_data'),
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('node_data', 'result')])
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return onnx_net, ref_net
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def create_net_one_const(self, shape1, shape2, ir_version):
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"""
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ONNX net IR net
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Input->Or with const->Output => Input->LogicalOr
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"""
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#
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# Create ONNX model
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#
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from onnx import helper
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from onnx import TensorProto
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input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
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output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1)
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const = np.random.randint(0, 2, shape2).astype(bool)
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node_const_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['const'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.BOOL,
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dims=const.shape,
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vals=const.flatten(),
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),
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)
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node_def = helper.make_node(
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'Or',
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inputs=['input', 'const'],
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outputs=['output']
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_const_def, node_def],
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'test_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model')
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# Create reference IR net
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ref_net = None
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if check_ir_version(10, None, ir_version):
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': shape1, 'kind': 'data'},
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'input_const_data': {'kind': 'data', 'value': const.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': const.shape, 'kind': 'data'},
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'node': {'kind': 'op', 'type': 'LogicalOr'},
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'node_data': {'shape': shape1, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_const_data', 'const'),
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('const', 'const_data'),
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('input_data', 'node'),
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('const_data', 'node'),
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('node', 'node_data'),
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('node_data', 'result')])
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return onnx_net, ref_net
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def create_net_const(self, shape1, shape2, ir_version):
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"""
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ONNX net IR net
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Input->Concat with const or const->Output => Input->Concat
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"""
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#
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# Create ONNX model
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#
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from onnx import helper
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from onnx import TensorProto
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concat_axis = 0
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output_shape = list(shape1)
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output_shape[concat_axis] *= 2
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input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
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output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape)
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const1 = np.random.randint(0, 2, shape1).astype(bool)
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const2 = np.random.randint(0, 2, shape2).astype(bool)
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node_const1_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['const1'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.BOOL,
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dims=const1.shape,
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vals=const1.flatten(),
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),
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)
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node_const2_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['const2'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.BOOL,
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dims=const2.shape,
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vals=const2.flatten(),
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),
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)
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node_def = helper.make_node(
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'Or',
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inputs=['const1', 'const2'],
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outputs=['node_out']
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)
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node_concat_def = helper.make_node(
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'Concat',
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inputs=['input', 'node_out'],
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outputs=['output'],
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axis=concat_axis
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_const1_def, node_const2_def, node_def, node_concat_def],
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'test_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model')
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# Create reference IR net
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constant_calculated = np.logical_or(const1, const2)
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ref_net = None
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if check_ir_version(10, None, ir_version):
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': const1.shape, 'kind': 'data'},
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'input_const_data': {'kind': 'data', 'value': constant_calculated.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': const1.shape, 'kind': 'data'},
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'concat': {'kind': 'op', 'type': 'Concat', 'axis': concat_axis},
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'concat_data': {'shape': output_shape, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_const_data', 'const'),
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('const', 'const_data'),
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('input_data', 'concat'),
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('const_data', 'concat'),
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('concat', 'concat_data'),
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('concat_data', 'result')])
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return onnx_net, ref_net
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test_data_precommit = [dict(shape1=[2, 3, 4], shape2=[2, 3, 4]),
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dict(shape1=[2, 4, 6, 8, 10], shape2=[2, 4, 6, 8, 10])]
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test_data = [dict(shape1=[4, 6], shape2=[4, 6]),
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dict(shape1=[4, 6, 8], shape2=[4, 6, 8]),
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dict(shape1=[4, 6, 8, 10], shape2=[4, 6, 8, 10]),
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dict(shape1=[4, 6, 8, 10, 12], shape2=[4, 6, 8, 10, 12])]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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def test_or_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_or(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_or_one_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net_one_const(**params, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_or_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net_const(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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