e4f44b19fd
* Mark all failed ONNX layer tests as XFail
* Add additional xfailed marks
* Add one more failed tests into XFail
* Add conditions for CPU/GPU failures
* Revert "Add conditions for CPU/GPU failures"
This reverts commit 790524c59c.
* Add failures separation for CPU/GPU
* Replace all xfail with skip
201 lines
6.9 KiB
Python
201 lines
6.9 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 TestNonZero(OnnxRuntimeLayerTest):
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def create_net(self, shape, ir_version):
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"""
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ONNX net IR net
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Input->NonZero->Output => Input->NonZero->Result
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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
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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, shape)
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node_def = onnx.helper.make_node(
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'NonZero',
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inputs=['input'],
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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],
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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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#
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# Create reference IR net
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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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'node': {'kind': 'op', 'type': 'NonZero', 'version': 'opset3',
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'output_type': 'i64'},
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'node_data': {'shape': [len(shape), np.prod(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', 'node'),
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('node', 'node_data'),
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('node_data', 'result')
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])
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return onnx_net, ref_net
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def create_net_const(self, input_value, output_value, precision, ir_version):
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"""
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ONNX net IR net
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Input->Concat(+NonZero const)->Output => Input->Concat(+const)->Result
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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
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concat_axis = 0
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output_shape = list(output_value.shape)
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output_shape[concat_axis] *= 2
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_value.shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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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=input_value.shape,
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vals=input_value.flatten(),
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),
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)
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node_def = onnx.helper.make_node(
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'NonZero',
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inputs=['const1'],
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outputs=['nonzero1']
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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', 'nonzero1'],
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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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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model')
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#
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# Create reference IR net
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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': output_value.shape, 'kind': 'data'},
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'input_const_data': {'kind': 'data', 'value': output_value.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': output_value.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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])
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return onnx_net, ref_net
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test_data = [
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dict(shape=[10, 12]),
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dict(shape=[8, 10, 12]),
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dict(shape=[6, 8, 10, 12]),
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dict(shape=[4, 6, 8, 10, 12])
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]
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test_const_data = [
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dict(
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input_value=np.array([3, 0, 0, 0, 4, 0, 5, 6, 0]).reshape((3, 3)),
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output_value=np.array([0, 1, 2, 2, 0, 1, 0, 1]).reshape(2, 4),
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),
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dict(
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input_value=np.array([0, 1, 0, 1]).reshape((4)),
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output_value=np.array([1, 3]).reshape((1, 2)),
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),
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dict(
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input_value=np.array([0, 1, 0, 1, 1, 0, 1, 0]).reshape((2, 4)),
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output_value=np.array([0, 0, 1, 1, 1, 3, 0, 2]).reshape((2, 4)),
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),
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dict(
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input_value=np.array([1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0]).reshape(
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(2, 3, 3)),
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output_value=np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 2, 2,
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0, 0, 0, 1, 1, 2, 0, 2, 1, 0, 1, 2, 0, 1, 2, 0, 2, 1]).reshape(
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(3, 12)),
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),
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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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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_non_zero(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_const_data)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_non_zero_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net_const(**params, precision=precision, 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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