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
223 lines
9.2 KiB
Python
223 lines
9.2 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from common.onnx_layer_test_class import OnnxRuntimeLayerTest
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class TestSqueeze(OnnxRuntimeLayerTest):
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def create_squeeze_net(self, axes, input_shape, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Squeeze(axes=0)->Output => Input->Reshape
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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, input_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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node_squeeze_def = onnx.helper.make_node(
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'Squeeze',
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inputs=['input'],
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outputs=['output'],
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axes=axes
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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_squeeze_def],
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'test_squeeze_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_squeeze_model')
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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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return onnx_net, ref_net
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def create_squeeze_net_const(self, axes, input_shape, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Concat(+squeezed const)->Output => Input->Concat(+const)
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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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import numpy as np
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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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const_number = np.prod(input_shape)
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constant = np.random.randint(-127, 127, const_number).astype(float)
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constant = np.reshape(constant, input_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=constant.shape,
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vals=constant.flatten(),
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),
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)
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node_squeeze_def = onnx.helper.make_node(
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'Squeeze',
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inputs=['const1'],
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outputs=['squeeze1'],
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axes=axes
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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', 'squeeze1'],
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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_squeeze_def, node_concat_def],
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'test_squeeze_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_squeeze_model')
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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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return onnx_net, ref_net
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test_data_5D = [
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dict(axes=[0], input_shape=[1, 2, 3, 10, 10], output_shape=[2, 3, 10, 10]),
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dict(axes=[1], input_shape=[2, 1, 3, 10, 10], output_shape=[2, 3, 10, 10]),
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dict(axes=[2], input_shape=[2, 3, 1, 10, 10], output_shape=[2, 3, 10, 10]),
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dict(axes=[3], input_shape=[2, 3, 10, 1, 10], output_shape=[2, 3, 10, 10]),
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dict(axes=[4], input_shape=[2, 3, 10, 10, 1], output_shape=[2, 3, 10, 10]),
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dict(axes=[0, 1], input_shape=[1, 1, 3, 10, 10], output_shape=[3, 10, 10]),
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dict(axes=[0, 2], input_shape=[1, 3, 1, 10, 10], output_shape=[3, 10, 10]),
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dict(axes=[0, 3], input_shape=[1, 3, 10, 1, 10], output_shape=[3, 10, 10]),
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dict(axes=[0, 4], input_shape=[1, 3, 10, 10, 1], output_shape=[3, 10, 10]),
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dict(axes=[1, 2], input_shape=[3, 1, 1, 10, 10], output_shape=[3, 10, 10]),
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dict(axes=[1, 3], input_shape=[3, 1, 10, 1, 10], output_shape=[3, 10, 10]),
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dict(axes=[1, 4], input_shape=[3, 1, 10, 10, 1], output_shape=[3, 10, 10]),
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dict(axes=[2, 3], input_shape=[3, 10, 1, 1, 10], output_shape=[3, 10, 10]),
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dict(axes=[2, 4], input_shape=[3, 10, 1, 10, 1], output_shape=[3, 10, 10]),
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dict(axes=[3, 4], input_shape=[3, 10, 10, 1, 1], output_shape=[3, 10, 10]),
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dict(axes=[0, 1, 2], input_shape=[1, 1, 1, 10, 10], output_shape=[10, 10]),
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dict(axes=[0, 1, 3], input_shape=[1, 1, 10, 1, 10], output_shape=[10, 10]),
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dict(axes=[0, 1, 4], input_shape=[1, 1, 10, 10, 1], output_shape=[10, 10]),
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dict(axes=[0, 2, 3], input_shape=[1, 10, 1, 1, 10], output_shape=[10, 10]),
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dict(axes=[0, 2, 4], input_shape=[1, 10, 1, 10, 1], output_shape=[10, 10]),
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dict(axes=[0, 3, 4], input_shape=[1, 10, 10, 1, 1], output_shape=[10, 10]),
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dict(axes=[1, 2, 3], input_shape=[10, 1, 1, 1, 10], output_shape=[10, 10]),
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dict(axes=[1, 2, 4], input_shape=[10, 1, 1, 10, 1], output_shape=[10, 10]),
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dict(axes=[1, 3, 4], input_shape=[10, 1, 10, 1, 1], output_shape=[10, 10]),
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dict(axes=[2, 3, 4], input_shape=[10, 10, 1, 1, 1], output_shape=[10, 10])]
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test_data_4D = [
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dict(axes=[0], input_shape=[1, 3, 10, 10], output_shape=[3, 10, 10]),
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dict(axes=[1], input_shape=[3, 1, 10, 10], output_shape=[3, 10, 10]),
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dict(axes=[2], input_shape=[3, 10, 1, 10], output_shape=[3, 10, 10]),
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dict(axes=[3], input_shape=[3, 10, 10, 1], output_shape=[3, 10, 10]),
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dict(axes=[0, 1], input_shape=[1, 1, 10, 10], output_shape=[10, 10]),
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dict(axes=[0, 2], input_shape=[1, 10, 1, 10], output_shape=[10, 10]),
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dict(axes=[0, 3], input_shape=[1, 10, 10, 1], output_shape=[10, 10]),
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dict(axes=[1, 2], input_shape=[10, 1, 1, 10], output_shape=[10, 10]),
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dict(axes=[1, 3], input_shape=[10, 1, 10, 1], output_shape=[10, 10]),
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dict(axes=[2, 3], input_shape=[10, 10, 1, 1], output_shape=[10, 10])]
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test_data_3D = [
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dict(axes=[0], input_shape=[1, 10, 10], output_shape=[10, 10]),
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dict(axes=[1], input_shape=[10, 1, 10], output_shape=[10, 10]),
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dict(axes=[2], input_shape=[10, 10, 1], output_shape=[10, 10])]
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@pytest.mark.parametrize("params", test_data_5D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_5D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_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_data_4D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_4D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_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_data_3D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_3D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_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_data_5D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_net_const(**params, ir_version=ir_version), ie_device,
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precision, 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_data_4D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_net_const(**params, ir_version=ir_version), ie_device,
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precision, 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_data_3D)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_squeeze_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_squeeze_net_const(**params, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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