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
148 lines
4.2 KiB
Python
148 lines
4.2 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 TestScale(OnnxRuntimeLayerTest):
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def create_net(self, shape, scale, ir_version):
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"""
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ONNX net IR net
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Input->Scale->Output => Input->Power
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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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'Scale',
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inputs=['input'],
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outputs=['output'],
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scale=scale
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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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return onnx_net, ref_net
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def create_net_const(self, shape, scale, precision, ir_version):
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"""
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ONNX net IR net
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Input->Concat(+scaled 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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output_shape = shape.copy()
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output_shape[concat_axis] *= 2
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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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constant = np.random.randint(-127, 127, 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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node_def = onnx.helper.make_node(
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'Scale',
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inputs=['const1'],
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outputs=['scale'],
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scale=scale
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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', 'scale'],
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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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ir_const = constant.flatten() * scale
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if precision == 'FP16':
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ir_const = ir_const.astype(np.float16)
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ref_net = None
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return onnx_net, ref_net
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test_data = [dict(shape=[10, 12], scale=0.1),
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dict(shape=[8, 10, 12], scale=0.9),
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dict(shape=[6, 8, 10, 12], scale=1.5),
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dict(shape=[4, 6, 8, 10, 12], scale=4.5)]
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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_scale(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_data)
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@pytest.mark.nightly
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@pytest.mark.skip(reason='GREEN_SUITE')
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def test_scale_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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