# Copyright (C) 2018-2023 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import numpy as np import pytest from common.layer_test_class import check_ir_version from common.onnx_layer_test_class import OnnxRuntimeLayerTest from unit_tests.utils.graph import build_graph class TestAnd(OnnxRuntimeLayerTest): def _prepare_input(self, inputs_dict): for input in inputs_dict.keys(): inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool) return inputs_dict def create_net(self, shape1, shape2, ir_version): """ ONNX net IR net Input->And with 2nd input->Output => Input->LogicalAnd """ # # Create ONNX model # from onnx import helper from onnx import TensorProto input1 = helper.make_tensor_value_info('input1', TensorProto.BOOL, shape1) input2 = helper.make_tensor_value_info('input2', TensorProto.BOOL, shape2) output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1) node_def = helper.make_node( 'And', inputs=['input1', 'input2'], outputs=['output'] ) # Create the graph (GraphProto) graph_def = helper.make_graph( [node_def], 'test_model', [input1, input2], [output], ) # Create the model (ModelProto) onnx_net = helper.make_model(graph_def, producer_name='test_model') # Create reference IR net ref_net = None if check_ir_version(10, None, ir_version): nodes_attributes = { 'input1': {'kind': 'op', 'type': 'Parameter'}, 'input1_data': {'shape': shape1, 'kind': 'data'}, 'input2': {'kind': 'op', 'type': 'Parameter'}, 'input2_data': {'shape': shape2, 'kind': 'data'}, 'node': {'kind': 'op', 'type': 'LogicalAnd'}, 'node_data': {'shape': shape1, 'kind': 'data'}, 'result': {'kind': 'op', 'type': 'Result'} } ref_net = build_graph(nodes_attributes, [('input1', 'input1_data'), ('input2', 'input2_data'), ('input1_data', 'node'), ('input2_data', 'node'), ('node', 'node_data'), ('node_data', 'result')]) return onnx_net, ref_net def create_net_one_const(self, shape1, shape2, ir_version): """ ONNX net IR net Input->And with const->Output => Input->LogicalAnd """ # # Create ONNX model # from onnx import helper from onnx import TensorProto input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1) output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1) const = np.random.randint(0, 2, shape2).astype(bool) node_const_def = helper.make_node( 'Constant', inputs=[], outputs=['const'], value=helper.make_tensor( name='const_tensor', data_type=TensorProto.BOOL, dims=const.shape, vals=const.flatten(), ), ) node_def = helper.make_node( 'And', inputs=['input', 'const'], outputs=['output'] ) # Create the graph (GraphProto) graph_def = helper.make_graph( [node_const_def, node_def], 'test_model', [input], [output], ) # Create the model (ModelProto) onnx_net = helper.make_model(graph_def, producer_name='test_model') # Create reference IR net ref_net = None if check_ir_version(10, None, ir_version): nodes_attributes = { 'input': {'kind': 'op', 'type': 'Parameter'}, 'input_data': {'shape': shape1, 'kind': 'data'}, 'input_const_data': {'kind': 'data', 'value': const.flatten()}, 'const': {'kind': 'op', 'type': 'Const'}, 'const_data': {'shape': const.shape, 'kind': 'data'}, 'node': {'kind': 'op', 'type': 'LogicalAnd'}, 'node_data': {'shape': shape1, 'kind': 'data'}, 'result': {'kind': 'op', 'type': 'Result'} } ref_net = build_graph(nodes_attributes, [('input', 'input_data'), ('input_const_data', 'const'), ('const', 'const_data'), ('input_data', 'node'), ('const_data', 'node'), ('node', 'node_data'), ('node_data', 'result')]) return onnx_net, ref_net def create_net_const(self, shape1, shape2, ir_version): """ ONNX net IR net Input->Concat with const and const->Output => Input->Concat """ # # Create ONNX model # from onnx import helper from onnx import TensorProto concat_axis = 0 output_shape = list(shape1) output_shape[concat_axis] *= 2 input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1) output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape) const1 = np.random.randint(0, 2, shape1).astype(bool) const2 = np.random.randint(0, 2, shape2).astype(bool) node_const1_def = helper.make_node( 'Constant', inputs=[], outputs=['const1'], value=helper.make_tensor( name='const_tensor', data_type=TensorProto.BOOL, dims=const1.shape, vals=const1.flatten(), ), ) node_const2_def = helper.make_node( 'Constant', inputs=[], outputs=['const2'], value=helper.make_tensor( name='const_tensor', data_type=TensorProto.BOOL, dims=const2.shape, vals=const2.flatten(), ), ) node_def = helper.make_node( 'And', inputs=['const1', 'const2'], outputs=['node_out'] ) node_concat_def = helper.make_node( 'Concat', inputs=['input', 'node_out'], outputs=['output'], axis=concat_axis ) # Create the graph (GraphProto) graph_def = helper.make_graph( [node_const1_def, node_const2_def, node_def, node_concat_def], 'test_model', [input], [output], ) # Create the model (ModelProto) onnx_net = helper.make_model(graph_def, producer_name='test_model') # Create reference IR net constant_calculated = np.logical_and(const1, const2) ref_net = None if check_ir_version(10, None, ir_version): nodes_attributes = { 'input': {'kind': 'op', 'type': 'Parameter'}, 'input_data': {'shape': const1.shape, 'kind': 'data'}, 'input_const_data': {'kind': 'data', 'value': constant_calculated.flatten()}, 'const': {'kind': 'op', 'type': 'Const'}, 'const_data': {'shape': const1.shape, 'kind': 'data'}, 'concat': {'kind': 'op', 'type': 'Concat', 'axis': concat_axis}, 'concat_data': {'shape': output_shape, 'kind': 'data'}, 'result': {'kind': 'op', 'type': 'Result'} } ref_net = build_graph(nodes_attributes, [('input', 'input_data'), ('input_const_data', 'const'), ('const', 'const_data'), ('input_data', 'concat'), ('const_data', 'concat'), ('concat', 'concat_data'), ('concat_data', 'result')]) return onnx_net, ref_net test_data = [dict(shape1=[4, 6], shape2=[4, 6]), dict(shape1=[4, 6, 8], shape2=[4, 6, 8]), dict(shape1=[4, 6, 8, 10], shape2=[4, 6, 8, 10]), dict(shape1=[4, 6, 8, 10, 12], shape2=[4, 6, 8, 10, 12])] @pytest.mark.parametrize("params", test_data) @pytest.mark.nightly def test_and(self, params, ie_device, precision, ir_version, temp_dir, use_old_api): self._test(*self.create_net(**params, ir_version=ir_version), ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api) @pytest.mark.parametrize("params", test_data) @pytest.mark.nightly def test_and_one_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api): self._test(*self.create_net_one_const(**params, ir_version=ir_version), ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api) @pytest.mark.parametrize("params", test_data) @pytest.mark.nightly def test_and_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api): self._test(*self.create_net_const(**params, ir_version=ir_version), ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)