112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
# Copyright (C) 2018-2021 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 TestInstanceNormalization(OnnxRuntimeLayerTest):
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def create_net(self, shape, epsilon, precision, ir_version):
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"""
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ONNX net IR net
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Input->InstanceNormalization->Output => Input->MVN->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.randn(shape[1]).astype(np.float)
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bias_const = np.random.randn(shape[1]).astype(np.float)
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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'],
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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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args = dict()
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if epsilon:
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args['epsilon'] = epsilon
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node_def = helper.make_node(
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'InstanceNormalization',
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inputs=['input', 'scale', 'bias'],
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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_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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test_data_precommit = [
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dict(shape=[1, 1, 4, 6], epsilon=0.001),
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dict(shape=[1, 1, 2, 4, 6], epsilon=0.001)]
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test_data = [
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dict(shape=[1, 1, 4, 6], epsilon=None),
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dict(shape=[1, 1, 4, 6], epsilon=0.001),
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dict(shape=[1, 2, 4, 6], epsilon=None),
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dict(shape=[1, 2, 4, 6], epsilon=0.001),
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dict(shape=[2, 3, 4, 6], epsilon=None),
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dict(shape=[2, 3, 4, 6], epsilon=0.001),
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dict(shape=[1, 1, 2, 4, 6], epsilon=None),
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dict(shape=[1, 1, 2, 4, 6], epsilon=0.001),
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dict(shape=[1, 2, 4, 6, 6], epsilon=None),
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dict(shape=[1, 2, 4, 6, 6], epsilon=0.001),
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dict(shape=[2, 3, 4, 6, 6], epsilon=None),
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dict(shape=[2, 3, 4, 6, 6], epsilon=0.001)]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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def test_instance_normalization(self, params, ie_device, precision, ir_version, temp_dir):
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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)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_instance_normalization(self, params, ie_device, precision, ir_version, temp_dir):
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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)
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