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
135 lines
4.6 KiB
Python
135 lines
4.6 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.onnx_layer_test_class import OnnxRuntimeLayerTest
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class TestBatchNormalization(OnnxRuntimeLayerTest):
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def create_net(self, shape, epsilon, precision, ir_version, opset=None):
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"""
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ONNX net IR net
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Input->BatchNormalization->Output => Input->ScaleShift(Power)
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"""
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#
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# Create ONNX model
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#
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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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scale_const = np.random.randint(128, 256, shape[1]).astype(np.float32) / 128.
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bias_const = np.random.randint(0, 128, shape[1]).astype(np.float32) / 128.
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mean_const = np.random.randint(-127, 127, shape[1]).astype(np.float32)
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var_const = np.random.randint(128, 256, shape[1]).astype(np.float32) / 128.
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node_scale_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['scale_const'],
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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=scale_const.shape,
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vals=scale_const.flatten(),
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),
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)
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node_bias_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['bias'],
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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=bias_const.shape,
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vals=bias_const.flatten(),
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),
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)
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node_mean_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['mean'],
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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=mean_const.shape,
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vals=mean_const.flatten(),
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),
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)
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node_var_def = helper.make_node(
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'Constant',
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inputs=[],
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outputs=['var'],
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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=var_const.shape,
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vals=var_const.flatten(),
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),
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)
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args = dict(epsilon=epsilon)
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if opset == 6:
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args['is_test'] = 1;
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node_def = helper.make_node(
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'BatchNormalization',
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inputs=['input', 'scale_const', 'bias', 'mean', 'var'],
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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_scale_def, node_bias_def, node_mean_def, node_var_def, 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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args = dict(producer_name='test_model')
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if opset:
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args['opset_imports'] = [helper.make_opsetid("", opset)]
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onnx_net = helper.make_model(graph_def, **args)
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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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test_data = [dict(shape=[1, 1, 4, 6], epsilon=0.001),
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dict(shape=[1, 2, 4, 6], epsilon=0.001),
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dict(shape=[2, 3, 4, 6], epsilon=0.001)]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_bn(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**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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@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_bn_opset6(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, precision=precision, opset=6, 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.nightly
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def test_bn_opset7(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, precision=precision, opset=7, 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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