Anton Chetverikov
e6e7f5158a
* Fix Mish and SoftPlus value propagation functions * Add unit tests for SoftPlus & Mish operations value propagation functions
131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from extensions.ops.activation_ops import Elu, SoftPlus, Mish
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph
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class TestActivationOp(unittest.TestCase):
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nodes_attributes = {
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'node_1': {
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'shape': np.array([4]),
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'value': None
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},
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'activation_node': {
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'op': 'Activation',
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'kind': 'op',
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'operation': None
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},
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'node_3': {
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'shape': None
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}
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}
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def test_activation_elu_infer(self):
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graph = build_graph(self.nodes_attributes,
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[
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('node_1', 'activation_node'),
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('activation_node', 'node_3')
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],
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{
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'node_1': {
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'value': np.array([6, -4, -2, -1])
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},
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'activation_node': {
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'operation': 'elu',
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'alpha': 1.0,
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},
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'node_3': {
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'value': None
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}
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})
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graph.graph['layout'] = 'NCHW'
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activation_node = Node(graph, 'activation_node')
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Elu.infer(activation_node)
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exp_shape = np.array([4])
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res_shape = graph.node['node_3']['shape']
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res_value = graph.node['node_3']['value']
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exp_value = np.array([6., -0.98168436, -0.86466472, -0.63212056])
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for i, value in enumerate(exp_shape):
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self.assertEqual(res_shape[i], value)
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for i, value in enumerate(exp_value):
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self.assertAlmostEqual(res_value[i], value)
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def test_activation_softplus_infer(self):
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graph = build_graph(self.nodes_attributes,
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[
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('node_1', 'activation_node'),
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('activation_node', 'node_3')
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],
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{
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'node_1': {
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'value': np.array([-1.0, 0.0, 1.0, 20.0])
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},
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'activation_node': {
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'op': 'SoftPlus',
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'operation': SoftPlus.operation,
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},
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'node_3': {
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'value': None
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}
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})
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graph.graph['layout'] = 'NCHW'
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activation_node = Node(graph, 'activation_node')
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SoftPlus.infer(activation_node)
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exp_shape = np.array([4])
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res_shape = graph.node['node_3']['shape']
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res_value = graph.node['node_3']['value']
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exp_value = np.array([0.3132617, 0.6931472, 1.3132617, 20.0])
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for i, value in enumerate(exp_shape):
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self.assertEqual(res_shape[i], value)
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for i, value in enumerate(exp_value):
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self.assertAlmostEqual(res_value[i], value)
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def test_activation_mish_infer(self):
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graph = build_graph(self.nodes_attributes,
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[
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('node_1', 'activation_node'),
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('activation_node', 'node_3')
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],
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{
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'node_1': {
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'value': np.array([-1.0, 0.0, 1.0, 20.0])
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},
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'activation_node': {
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'op': 'Mish',
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'operation': Mish.operation,
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},
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'node_3': {
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'value': None
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}
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})
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graph.graph['layout'] = 'NCHW'
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activation_node = Node(graph, 'activation_node')
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Mish.infer(activation_node)
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exp_shape = np.array([4])
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res_shape = graph.node['node_3']['shape']
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res_value = graph.node['node_3']['value']
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exp_value = np.array([-0.30340146, 0.0, 0.8650984, 20.0])
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for i, value in enumerate(exp_shape):
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self.assertEqual(res_shape[i], value)
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for i, value in enumerate(exp_value):
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self.assertAlmostEqual(res_value[i], value)
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