274 lines
10 KiB
Python
274 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 TestReduceL1L2(OnnxRuntimeLayerTest):
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def create_reduce_lp(self, shape, axes, keep_dims, reduce_p, ir_version):
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"""
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ONNX net IR net
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Input->ReduceLX(axes)->Output => Input->ReduceLX
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"""
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#
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# Create ONNX model
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#
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import onnx
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from onnx import helper
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from onnx import TensorProto, OperatorSetIdProto
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output_shape = shape.copy()
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_axes = axes.copy() if axes is not None else list(range(len(shape)))
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for axis in _axes:
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output_shape[axis] = 1
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if not keep_dims:
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output_shape = [dim for dim in output_shape if dim != 1]
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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args = dict(keepdims=keep_dims)
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if axes:
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args['axes'] = axes
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node_def = onnx.helper.make_node(
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"ReduceL" + str(reduce_p),
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inputs=['input'],
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outputs=['output'],
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**args
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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],
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[output],
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)
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# Set ONNX Opset
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onnx_opset = OperatorSetIdProto()
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onnx_opset.domain = ""
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# ONNX opset with `axes` as attribute in ONNX Reduce ops
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onnx_opset.version = 11
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model', opset_imports=[onnx_opset])
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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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': shape, 'kind': 'data'},
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'input_data_1': {'shape': [len(_axes)], 'value': _axes, 'kind': 'data'},
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'const_1': {'kind': 'op', 'type': 'Const'},
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'const_data_1': {'shape': [len(_axes)], 'kind': 'data'},
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'reduce': {'kind': 'op', 'type': "ReduceL" + str(reduce_p), 'keep_dims': keep_dims},
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'reduce_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_data_1', 'const_1'),
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('const_1', 'const_data_1'),
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('input_data', 'reduce'),
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('const_data_1', 'reduce'),
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('reduce', 'reduce_data'),
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('reduce_data', 'result')
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])
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return onnx_net, ref_net
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def create_reduce_lp_const(self, shape, axes, keep_dims, reduce_p, ir_version):
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"""
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ONNX net IR net
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Input->ReduceLX(axes)->Output => Input->ReduceLX
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"""
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#
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# Create ONNX model
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#
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import onnx
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from onnx import helper
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from onnx import TensorProto, OperatorSetIdProto
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output_shape = shape.copy()
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_axes = axes.copy() if axes is not None else list(range(len(shape)))
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for axis in _axes:
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output_shape[axis] = 1
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if not keep_dims:
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output_shape = [dim for dim in output_shape if dim != 1]
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if len(output_shape) == 0:
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output_shape = [1]
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concat_axis = 0
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concat_output_shape = output_shape.copy()
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concat_output_shape[concat_axis] *= 2
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
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constant = np.random.randn(*shape).astype(float)
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node_const_def = onnx.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.FLOAT,
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dims=constant.shape,
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vals=constant.flatten(),
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),
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)
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args = dict(keepdims=keep_dims)
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if axes:
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args['axes'] = axes
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node_def = onnx.helper.make_node(
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"ReduceL" + str(reduce_p),
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inputs=['const1'],
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outputs=['reduce'],
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**args
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)
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node_concat_def = onnx.helper.make_node(
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'Concat',
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inputs=['input', 'reduce'],
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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_const_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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# Set ONNX Opset
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onnx_opset = OperatorSetIdProto()
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onnx_opset.domain = ""
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# ONNX opset with `axes` as attribute in ONNX Reduce ops
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onnx_opset.version = 11
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model', opset_imports=[onnx_opset])
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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constant = np.power(
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np.sum(a=np.abs(np.power(constant, reduce_p)), axis=tuple(_axes), keepdims=keep_dims),
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1 / reduce_p)
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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': output_shape, 'kind': 'data'},
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'input_const_data': {'kind': 'data', 'value': constant.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': constant.shape, 'kind': 'data'},
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'concat': {'kind': 'op', 'type': 'Concat', 'axis': concat_axis},
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'concat_data': {'shape': concat_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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])
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return onnx_net, ref_net
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test_data_precommit = [
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dict(shape=[2, 4, 6, 8], axes=[-3, -1, -2]),
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dict(shape=[2, 4, 6, 8, 10], axes=[-4, -2]),
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]
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test_data = [
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dict(shape=[8], axes=None),
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dict(shape=[8], axes=[0]),
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dict(shape=[2, 4, 6], axes=None),
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dict(shape=[2, 4, 6], axes=[1]),
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dict(shape=[2, 4, 6], axes=[-2]),
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dict(shape=[2, 4, 6], axes=[-2, -1]),
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dict(shape=[2, 4, 6, 8], axes=[0]),
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dict(shape=[2, 4, 6, 8], axes=[-3, -1, -2]),
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dict(shape=[2, 4, 6, 8, 10], axes=None),
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dict(shape=[2, 4, 6, 8, 10], axes=[-2]),
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dict(shape=[2, 4, 6, 8, 10], axes=[1, 3]),
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dict(shape=[2, 4, 6, 8, 10], axes=[-4, -2]),
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]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.parametrize("reduce_p", [1, 2])
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@pytest.mark.precommit
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def test_reduce_lp_precommit(self, params, keep_dims, reduce_p, ie_device, precision,
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ir_version, temp_dir, use_old_api):
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self._test(*self.create_reduce_lp(**params, keep_dims=keep_dims, reduce_p=reduce_p,
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ir_version=ir_version),
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ie_device, precision, ir_version, 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.parametrize("keep_dims", [True, False])
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@pytest.mark.parametrize("reduce_p", [1, 2])
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@pytest.mark.nightly
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def test_reduce_lp(self, params, keep_dims, reduce_p, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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if ie_device == 'GPU':
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pytest.skip('GREEN_SUITE')
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self._test(*self.create_reduce_lp(**params, keep_dims=keep_dims, reduce_p=reduce_p,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.parametrize("reduce_p", [1, 2])
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@pytest.mark.precommit
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def test_reduce_lp_const_precommit(self, params, keep_dims, reduce_p, ie_device, precision,
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ir_version, temp_dir, use_old_api):
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self._test(
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*self.create_reduce_lp_const(**params, keep_dims=keep_dims, reduce_p=reduce_p,
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ir_version=ir_version),
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ie_device, precision, ir_version, 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.parametrize("keep_dims", [True, False])
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@pytest.mark.parametrize("reduce_p", [1, 2])
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_reduce_lp_const(self, params, keep_dims, reduce_p, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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self._test(*self.create_reduce_lp_const(**params, keep_dims=keep_dims, reduce_p=reduce_p,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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