def test_build_index_with_cache(self):
# Empty memory data elements for storage
empty_data = 'base64://'
f = FlannNearestNeighborsIndex(empty_data, empty_data, empty_data)
# Internal elements should initialize have zero-length byte values
self.assertEqual(len(f._index_elem.get_bytes()), 0)
self.assertEqual(len(f._index_param_elem.get_bytes()), 0)
self.assertEqual(len(f._descr_cache_elem.get_bytes()), 0)
# Make unit vectors, one for each feature dimension.
dim = 8
test_descriptors = []
for i in range(dim):
v = numpy.zeros(dim, float)
v[i] = 1.
d = DescriptorMemoryElement('unit', i)
d.set_vector(v)
test_descriptors.append(d)
f.build_index(test_descriptors)
# Internal elements should not have non-zero byte values.
self.assertGreater(len(f._index_elem.get_bytes()), 0)
self.assertGreater(len(f._index_param_elem.get_bytes()), 0)
self.assertGreater(len(f._descr_cache_elem.get_bytes()), 0)
def test_build_index(self):
# Empty memory data elements for storage
empty_data = 'base64://'
f = FlannNearestNeighborsIndex(empty_data, empty_data, empty_data)
# Internal elements should initialize have zero-length byte values
self.assertEqual(len(f._index_elem.get_bytes()), 0)
self.assertEqual(len(f._index_param_elem.get_bytes()), 0)
self.assertEqual(len(f._descr_cache_elem.get_bytes()), 0)
# Make unit vectors, one for each feature
dim = 8
test_descriptors = []
for i in range(dim):
v = numpy.zeros(dim, float)
v[i] = 1.
d = DescriptorMemoryElement('unit', i)
d.set_vector(v)
test_descriptors.append(d)
f.build_index(test_descriptors)
# Internal elements should not have non-zero byte values.
self.assertGreater(len(f._index_elem.get_bytes()), 0)
self.assertGreater(len(f._index_param_elem.get_bytes()), 0)
self.assertGreater(len(f._descr_cache_elem.get_bytes()), 0)
def test_has_model_data_valid_uris(self, _m_flann_lfm):
# Mocking flann data loading that occurs in constructor when given
# non-empty URI targets
f = FlannNearestNeighborsIndex(
'base64://bW9kZWxEYXRh', # 'modelData'
'base64://cGFyYW1EYXRh', # 'paramData'
'base64://ZGVzY3JEYXRh', # 'descrData'
)
self.assertTrue(f._has_model_data())
def test_has_model_data_valid_uris(self, _m_flann_lfm):
# Mocking flann data loading that occurs in constructor when given
# non-empty URI targets
f = FlannNearestNeighborsIndex(
'base64://bW9kZWxEYXRh', # 'modelData'
'base64://cGFyYW1EYXRh', # 'paramData'
'base64://ZGVzY3JEYXRh', # 'descrData'
)
self.assertTrue(f._has_model_data())
def test_load_flann_model_empty_data_elements(self):
# Construct index with valid, but empty, data URIs instances
empty_data = 'base64://'
f = FlannNearestNeighborsIndex(empty_data, empty_data, empty_data)
# Load method should do nothing but set PID since given data was
# empty.
f._load_flann_model()
self.assertIsNone(f._descr_cache)
self.assertIsNone(f._flann)
self.assertIsNone(f._flann_build_params)
self.assertIsNotNone(f._pid)
def test_load_flann_model_empty_data_elements(self):
# Construct index with valid, but empty, data URIs instances
empty_data = 'base64://'
f = FlannNearestNeighborsIndex(empty_data, empty_data, empty_data)
# Load method should do nothing but set PID since given data was
# empty.
f._load_flann_model()
self.assertIsNone(f._descr_cache)
self.assertIsNone(f._flann)
self.assertIsNone(f._flann_build_params)
self.assertIsNotNone(f._pid)
def test_configuration(self):
index_filepath = '/index_filepath'
para_filepath = '/param_fp'
descr_cache_fp = '/descrcachefp'
# Make configuration based on default
c = FlannNearestNeighborsIndex.get_default_config()
c['index_filepath'] = index_filepath
c['parameters_filepath'] = para_filepath
c['descriptor_cache_filepath'] = descr_cache_fp
c['distance_method'] = 'hik'
c['random_seed'] = 42
# Build based on configuration
index = FlannNearestNeighborsIndex.from_config(c)
ntools.assert_equal(index._index_filepath, index_filepath)
ntools.assert_equal(index._index_param_filepath, para_filepath)
ntools.assert_equal(index._descr_cache_filepath, descr_cache_fp)
c2 = index.get_config()
ntools.assert_equal(c, c2)
def test_configuration(self):
index_filepath = '/index_filepath'
para_filepath = '/param_fp'
descr_cache_fp = '/descrcachefp'
# Make configuration based on default
c = FlannNearestNeighborsIndex.get_default_config()
c['index_filepath'] = index_filepath
c['parameters_filepath'] = para_filepath
c['descriptor_cache_filepath'] = descr_cache_fp
c['distance_method'] = 'hik'
c['random_seed'] = 42
# Build based on configuration
index = FlannNearestNeighborsIndex.from_config(c)
ntools.assert_equal(index._index_filepath, index_filepath)
ntools.assert_equal(index._index_param_filepath, para_filepath)
ntools.assert_equal(index._descr_cache_filepath, descr_cache_fp)
c2 = index.get_config()
ntools.assert_equal(c, c2)
def test_configuration(self):
self._make_cache_files()
self._clean_cache_files()
# Make configuration based on default
c = FlannNearestNeighborsIndex.get_default_config()
c['index_filepath'] = self.FLANN_INDEX_CACHE
c['parameters_filepath'] = self.FLANN_PARAMETER_CACHE
c['descriptor_cache_filepath'] = self.FLANN_DESCR_CACHE
c['distance_method'] = 'hik'
c['random_seed'] = 42
# Build based on configuration
index = FlannNearestNeighborsIndex.from_config(c)
ntools.assert_equal(index._index_filepath, self.FLANN_INDEX_CACHE)
ntools.assert_equal(index._index_param_filepath,
self.FLANN_PARAMETER_CACHE)
ntools.assert_equal(index._descr_cache_filepath,
self.FLANN_DESCR_CACHE)
c2 = index.get_config()
ntools.assert_equal(c, c2)
def test_configuration(self):
index_filepath = '/index_filepath'
para_filepath = '/param_fp'
descr_cache_fp = '/descrcachefp'
c = FlannNearestNeighborsIndex(
index_uri=index_filepath,
parameters_uri=para_filepath,
descriptor_cache_uri=descr_cache_fp,
distance_method='hik',
random_seed=42,
)
for inst in configuration_test_helper(
c): # type: FlannNearestNeighborsIndex
assert inst._index_uri == index_filepath
assert inst._index_param_uri == para_filepath
assert inst._descr_cache_uri == descr_cache_fp
assert inst._distance_method == 'hik'
assert inst._rand_seed == 42
def test_build_index_no_descriptors(self):
f = FlannNearestNeighborsIndex()
self.assertRaises(ValueError, f.build_index, [])
import random
import unittest
import nose.tools as ntools
import numpy
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
from smqtk.algorithms.nn_index.flann import FlannNearestNeighborsIndex
from smqtk.utils.file_utils import make_tempfile
__author__ = "*****@*****.**"
# Don't bother running tests of the class is not usable
if FlannNearestNeighborsIndex.is_usable():
class TestFlannIndex (unittest.TestCase):
FLANN_INDEX_CACHE = None
FLANN_PARAMETER_CACHE = None
FLANN_DESCR_CACHE = None
RAND_SEED = 42
@classmethod
def _make_cache_files(cls):
cls.FLANN_INDEX_CACHE = make_tempfile('.flann')
cls.FLANN_PARAMETER_CACHE = make_tempfile('.pickle')
cls.FLANN_DESCR_CACHE = make_tempfile('.pickle')
def test_has_model_data_empty_elements(self):
f = FlannNearestNeighborsIndex('', '', '')
self.assertFalse(f._has_model_data())
import os
import random
import unittest
import nose.tools as ntools
import numpy
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
from smqtk.algorithms.nn_index.flann import FlannNearestNeighborsIndex
__author__ = "*****@*****.**"
# Don't bother running tests of the class is not usable
if FlannNearestNeighborsIndex.is_usable():
class TestFlannIndex(unittest.TestCase):
RAND_SEED = 42
def _make_inst(self, dist_method):
"""
Make an instance of FlannNearestNeighborsIndex
"""
return FlannNearestNeighborsIndex(distance_method=dist_method,
random_seed=self.RAND_SEED)
def test_known_descriptors_euclidean_unit(self):
dim = 5
###
import unittest.mock as mock
import random
import unittest
import numpy
import pytest
from smqtk.algorithms import NearestNeighborsIndex
from smqtk.algorithms.nn_index.flann import FlannNearestNeighborsIndex
from smqtk.representation.descriptor_element.local_elements import \
DescriptorMemoryElement
from smqtk.utils.configuration import configuration_test_helper
# Don't bother running tests of the class is not usable
@pytest.mark.skipif(not FlannNearestNeighborsIndex.is_usable(),
reason="FlannNearestNeighborsIndex does not report as "
"usable.")
class TestFlannIndex(unittest.TestCase):
RAND_SEED = 42
def _make_inst(self, dist_method):
"""
Make an instance of FlannNearestNeighborsIndex
"""
return FlannNearestNeighborsIndex(distance_method=dist_method,
random_seed=self.RAND_SEED)
def test_impl_findable(self):
# Already here because the implementation is reporting itself as
def test_has_model_data_empty_elements(self):
f = FlannNearestNeighborsIndex('', '', '')
self.assertFalse(f._has_model_data())
def test_has_model_data_no_uris(self):
f = FlannNearestNeighborsIndex()
self.assertFalse(f._has_model_data())
def _make_inst(self, dist_method):
"""
Make an instance of FlannNearestNeighborsIndex
"""
return FlannNearestNeighborsIndex(distance_method=dist_method,
random_seed=self.RAND_SEED)
def test_has_model_data_no_uris(self):
f = FlannNearestNeighborsIndex()
self.assertFalse(f._has_model_data())
