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How to use test_raising method in grail

Best Python code snippet using grail_python

broadcast_test.py

Source:broadcast_test.py

1# Copyright (C) 2018-2022 Intel Corporation2# SPDX-License-Identifier: Apache-2.03import unittest4import numpy as np5from generator import generator, generate6from openvino.tools.mo.front.common.partial_infer.utils import int64_array, undefined_shape_of_rank7from openvino.tools.mo.graph.graph import Node8from openvino.tools.mo.ops.broadcast import Broadcast9from unit_tests.utils.graph import build_graph, valued_const_with_data, regular_op_with_empty_data, \10    shaped_data11@generator12class BroadcastTest(unittest.TestCase):13    @generate(*[14        ([1], [3, 3], None, 'numpy', [[1, 1, 1], [1, 1, 1], [1, 1, 1]]),15        ([1], [3, 3], None, 'numpy'),16        # shape broadcasting17        ([1], [1, 2], [0], 'explicit'),18        ([1], [1, 2], [-2], 'explicit'),19        ([1, 7], [5, 1, 7, 3], [1, 2], 'explicit'),20        ([2, 1, 3], [2, 1, 3, 3], [0, 1, 2], 'explicit'),21        ([2, 1, 3], [5, 2, 1, 3], [1, 2, 3], 'explicit'),22        # value broadcasting23        ([1], [1, 2], [0], 'explicit', [[1, 1]]),24        ([[3, 1]], [2, 1, 2], [1, 2], 'explicit', [[[3, 1]], [[3, 1]]]),  # ref_shape (2, 1, 2)25        ([[3, 1]], [2, 1, 2], [-2, -1], 'explicit', [[[3, 1]], [[3, 1]]]),  # ref_shape (2, 1, 2)26        ([[[9, 5, 7]], [[9, 5, 7]]], [2, 2, 1, 3], [1, 2, 3], 'explicit',  # in_shape (2, 1, 3)27         [[[[9, 5, 7]], [[9, 5, 7]]], [[[9, 5, 7]], [[9, 5, 7]]]]),  # ref_out_shape (2, 2, 1, 3)28        ([[[9, 5, 7]], [[3, 4, 8]]], [2, 1, 3, 3], [0, 1, 2], 'explicit',  # in_shape (2, 1, 3)29         [[[[9, 9, 9], [5, 5, 5], [7, 7, 7]]], [[[3, 3, 3], [4, 4, 4], [8, 8, 8]]]]),  # ref_out_shape (2, 1, 3, 3)30        # negative tests31        ([1], [2, 2], [0], 'explicit', None, True),32        ([1, 7], [5, 2, 7, 3], [1, 2], 'explicit', None, True),33        ([1, 7], [5, 2, 7, 3], [2, 1], 'explicit', None, True),34        ([1, 7], [5, 2, 7, 3], [-3, -2], 'explicit', None, True),35    ])36    def test_broadcast(self, data, target_shape, axes_mapping=None, mode='numpy', ref_out=None, test_raising=False):37        if ref_out is not None:38            input = valued_const_with_data('data', int64_array(data))39        else:40            input = shaped_data('data', int64_array(data))41        nodes = {42            **input,43            **valued_const_with_data('target_shape', int64_array(target_shape)),44            **regular_op_with_empty_data('broadcast', {'op': 'Broadcast', 'mode': mode}),45        }46        edges = [('data', 'broadcast'),47                 ('target_shape', 'broadcast'),48                 ('broadcast', 'broadcast_d')]49        if axes_mapping is not None:50            nodes.update(**valued_const_with_data('axes_mapping', int64_array(axes_mapping)))51            edges.append(('axes_mapping', 'broadcast'))52        graph = build_graph(nodes, edges)53        broadcast_node = Node(graph, 'broadcast')54        if test_raising:55            self.assertRaises(AssertionError, Broadcast.infer, broadcast_node)56            return57        Broadcast.infer(broadcast_node)58        if ref_out is not None:59            self.assertTrue(np.array_equal(broadcast_node.out_node().value, np.array(ref_out)))60        else:61            self.assertTrue(np.array_equal(broadcast_node.out_node().shape, np.array(target_shape)))62    @generate(*[63        ([1], [3], 'numpy', undefined_shape_of_rank(3)),64        ([1], [3], 'explicit', undefined_shape_of_rank(3)),65        ([1, 2], [3], 'numpy', None, True),66    ])67    def test_broadcast_dynamic(self, data, target_shape_shape, mode='numpy', ref_out_shape=None, test_raising=False):68        nodes = {69            **shaped_data('data', int64_array(data)),70            **shaped_data('target_shape', int64_array(target_shape_shape)),71            **regular_op_with_empty_data('broadcast', {'op': 'Broadcast', 'mode': mode}),72        }73        edges = [('data', 'broadcast'),74                 ('target_shape', 'broadcast'),75                 ('broadcast', 'broadcast_d')]76        graph = build_graph(nodes, edges)77        broadcast_node = Node(graph, 'broadcast')78        if test_raising:79            self.assertRaises(AssertionError, Broadcast.infer, broadcast_node)80            return81        Broadcast.infer(broadcast_node)...

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