147 lines
4.8 KiB
Python
147 lines
4.8 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 mo.front.caffe.extractors.batchnorm import batch_norm_ext
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from mo.front.common.partial_infer.elemental import copy_shape_infer
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from mo.utils.unittest.extractors import FakeParam, FakeModelLayer
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class FakeBNProtoLayer:
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def __init__(self, eps):
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self.batch_norm_param = FakeParam('eps', eps)
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class TestShapesParsing(unittest.TestCase):
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def test_bn_ext_no_ml_no_pb(self):
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self.assertRaises(AssertionError, batch_norm_ext, None, None)
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def test_bn_ext_no_ml(self):
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res = batch_norm_ext(FakeBNProtoLayer(10), None)
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exp_res = {
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'op': 'BatchNormalization',
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'type': 'BatchNormalization',
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'eps': 10,
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'infer': copy_shape_infer
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}
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self.assertEqual(res, exp_res)
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def test_bn_ext_ml_one_blob(self):
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self.assertRaises(AssertionError, batch_norm_ext, FakeBNProtoLayer(10), FakeModelLayer([np.array([1, 2])]))
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def test_bn_ext_ml_two_blobs(self):
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mean_blob = np.array([1., 2.])
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variance_blob = np.array([3., 4.])
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blobs = [mean_blob, variance_blob]
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res = batch_norm_ext(FakeBNProtoLayer(10),
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FakeModelLayer(blobs))
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exp_res = {
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'type': 'BatchNormalization',
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'eps': 10,
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'infer': copy_shape_infer,
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'mean': mean_blob,
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'variance': variance_blob,
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'embedded_inputs': [
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(1, 'gamma', {
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'bin': 'gamma'
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}),
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(2, 'beta', {
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'bin': 'beta'
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}),
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(3, 'mean', {
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'bin': 'biases'
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}),
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(4, 'variance', {
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'bin': 'weights'
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})
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]
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}
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for i in exp_res:
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if i in ('mean', 'variance'):
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np.testing.assert_array_equal(res[i], exp_res[i])
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else:
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self.assertEqual(res[i], exp_res[i])
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def test_bn_ext_ml_three_blobs(self):
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mean_blob = np.array([1., 2.])
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variance_blob = np.array([3., 4.])
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scale_blob = np.array([5., ])
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blobs = [mean_blob, variance_blob, scale_blob]
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res = batch_norm_ext(FakeBNProtoLayer(10),
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FakeModelLayer(blobs))
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exp_res = {
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'type': 'BatchNormalization',
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'eps': 10,
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'infer': copy_shape_infer,
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'mean': mean_blob * 0.2,
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'variance': variance_blob * 0.2,
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'embedded_inputs': [
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(1, 'gamma', {
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'bin': 'gamma'
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}),
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(2, 'beta', {
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'bin': 'beta'
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}),
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(3, 'mean', {
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'bin': 'biases'
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}),
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(4, 'variance', {
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'bin': 'weights'
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})
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]
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}
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for i in exp_res:
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if i in ('mean', 'variance'):
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np.testing.assert_array_equal(res[i], exp_res[i])
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else:
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self.assertEqual(res[i], exp_res[i])
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def test_bn_ext_ml_three_blobs_zero_scale(self):
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mean_blob = np.array([1., 2.])
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variance_blob = np.array([3., 4.])
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scale_blob = np.array([0., ])
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blobs = [mean_blob, variance_blob, scale_blob]
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res = batch_norm_ext(FakeBNProtoLayer(10),
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FakeModelLayer(blobs))
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exp_res = {
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'type': 'BatchNormalization',
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'eps': 10,
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'infer': copy_shape_infer,
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'mean': mean_blob * 0.,
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'variance': variance_blob * 0.,
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'embedded_inputs': [
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(1, 'gamma', {
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'bin': 'gamma'
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}),
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(2, 'beta', {
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'bin': 'beta'
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}),
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(3, 'mean', {
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'bin': 'biases'
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}),
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(4, 'variance', {
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'bin': 'weights'
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})
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]
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}
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for i in exp_res:
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if i in ('mean', 'variance'):
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np.testing.assert_array_equal(res[i], exp_res[i])
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else:
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self.assertEqual(res[i], exp_res[i]) |