Ruslan Nugmanov
236778aeec
* refactor of renaming libraries in layer tests * 1. adds check for old API and new FE usafe 2. refactor of api_2 arg * fix for tf_NMS test preprocessing * take libs path from LD_LIBRARY_PATH env * convert str to Path object * use wheels path to libs * print lib paths * print lib paths * use ov_frontend_path env * also check if file to rename exists * removes redundant prints * copy instead of rename * 1. copy instead of rename 2. adds some details to readme
101 lines
4.1 KiB
Python
101 lines
4.1 KiB
Python
# Copyright (C) 2018-2022 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 TestWhere(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(np.bool)
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return inputs_dict
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def create_net(self, condition_shape, shape_than, else_shape, ir_version):
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"""
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ONNX net IR net
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Input->Where->Output => Input->Select
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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_cond = helper.make_tensor_value_info('input_cond', TensorProto.BOOL, condition_shape)
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input_than = helper.make_tensor_value_info('input_than', TensorProto.BOOL, shape_than)
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input_else = helper.make_tensor_value_info('input_else', TensorProto.BOOL, else_shape)
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output = helper.make_tensor_value_info('output', TensorProto.BOOL, condition_shape)
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node_def = helper.make_node(
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'Where',
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inputs=['input_cond', 'input_than', 'input_else'],
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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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[input_cond, input_than, input_else],
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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_cond': {'kind': 'op', 'type': 'Parameter'},
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'input_cond_data': {'shape': condition_shape, 'kind': 'data'},
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'input_than': {'kind': 'op', 'type': 'Parameter'},
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'input_than_data': {'shape': shape_than, 'kind': 'data'},
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'input_else': {'kind': 'op', 'type': 'Parameter'},
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'input_else_data': {'shape': else_shape, 'kind': 'data'},
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'node': {'kind': 'op', 'type': 'Select'},
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'node_data': {'shape': condition_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_cond', 'input_cond_data'),
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('input_than', 'input_than_data'),
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('input_else', 'input_else_data'),
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('input_cond_data', 'node'),
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('input_than_data', 'node'),
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('input_else_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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test_data = [dict(condition_shape=[4, 6], shape_than=[4, 6], else_shape=[4, 6]),
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dict(condition_shape=[4, 6], shape_than=[4, 6], else_shape=[1, 6]),
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dict(condition_shape=[15, 3, 5], shape_than=[15, 1, 5], else_shape=[15, 3, 5]),
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dict(condition_shape=[2, 3, 4, 5], shape_than=[], else_shape=[2, 3, 4, 5]),
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dict(condition_shape=[2, 3, 4, 5], shape_than=[5], else_shape=[2, 3, 4, 5]),
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dict(condition_shape=[2, 3, 4, 5], shape_than=[2, 1, 1, 5],
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else_shape=[2, 3, 4, 5]),
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dict(condition_shape=[2, 3, 4, 5], shape_than=[2, 3, 4, 5],
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else_shape=[1, 3, 1, 5]),
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]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_where(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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