def test_configuration(self):
cfg = IndexLabelClassifier.get_default_config()
self.assertEqual(cfg, {'index_to_label_uri': None})
cfg['index_to_label_uri'] = self.FILEPATH_TEST_LABELS
c = IndexLabelClassifier.from_config(cfg)
self.assertEqual(c.get_config(), cfg)
def test_classify(self):
c = IndexLabelClassifier(self.FILEPATH_TEST_LABELS)
m_expected = {
six.b('label_1'): 1,
six.b('label_2'): 2,
six.b('negative'): 3,
six.b('label_3'): 4,
six.b('Kitware'): 5,
six.b('label_4'): 6,
}
d = DescriptorMemoryElement('test', 0)
d.set_vector([1, 2, 3, 4, 5, 6])
m = c._classify(d)
self.assertEqual(m, m_expected)
def test_configurable(self):
c = IndexLabelClassifier(self.FILEPATH_TEST_LABELS)
for inst in configuration_test_helper(c):
assert inst.index_to_label_uri == self.FILEPATH_TEST_LABELS
def test_is_usable(self):
# Should always be available
self.assertTrue(IndexLabelClassifier.is_usable())
def test_get_labels(self):
c = IndexLabelClassifier(self.FILEPATH_TEST_LABELS)
self.assertEqual(c.get_labels(), self.EXPECTED_LABEL_VEC)
def test_new(self):
c = IndexLabelClassifier(self.FILEPATH_TEST_LABELS)
self.assertEqual(c.label_vector, self.EXPECTED_LABEL_VEC)
initialize_logging(logging.getLogger('smqtk'), logging.DEBUG)
eval_data_set = DataMemorySet(EVAL_DATASET)
img_prob_descr_index = MemoryDescriptorIndex(OUTPUT_DESCR_PROB_INDEX)
img_prob_gen = CaffeDescriptorGenerator(CAFFE_DEPLOY,
CAFFE_MODEL,
CAFFE_IMG_MEAN,
'prob',
batch_size=1000,
use_gpu=True,
load_truncated_images=True)
img_c_mem_factory = ClassificationElementFactory(MemoryClassificationElement,
{})
img_prob_classifier = IndexLabelClassifier(CAFFE_LABELS)
eval_data2descr = {}
d_to_proc = set()
for data in eval_data_set:
if not img_prob_descr_index.has_descriptor(data.uuid()):
d_to_proc.add(data)
else:
eval_data2descr[data] = img_prob_descr_index[data.uuid()]
if d_to_proc:
eval_data2descr.update(img_prob_gen.compute_descriptor_async(d_to_proc))
d_to_proc.clear()
assert len(eval_data2descr) == eval_data_set.count()
index_additions = []
for data in d_to_proc:
def test_get_labels(self):
c = IndexLabelClassifier(self.FILEPATH_TEST_LABELS)
self.assertEqual(c.get_labels(), self.EXPECTED_LABEL_VEC)
def test_is_usable(self):
# Should always be available
self.assertTrue(IndexLabelClassifier.is_usable())
def test_is_usable(self):
# Should always be available
nose.tools.assert_true(IndexLabelClassifier.is_usable())
eval_data_set = DataMemorySet(DataFileElement(EVAL_DATASET))
img_prob_descr_set = MemoryDescriptorSet(
DataFileElement(OUTPUT_DESCR_PROB_SET))
img_prob_gen = CaffeDescriptorGenerator(DataFileElement(CAFFE_DEPLOY),
DataFileElement(CAFFE_MODEL),
DataFileElement(CAFFE_IMG_MEAN),
'prob',
batch_size=1000,
use_gpu=True,
load_truncated_images=True)
img_c_mem_factory = ClassificationElementFactory(MemoryClassificationElement,
{})
img_prob_classifier = IndexLabelClassifier(CAFFE_LABELS)
eval_data2descr = {}
d_to_proc = set()
for data in eval_data_set:
if not img_prob_descr_set.has_descriptor(data.uuid()):
d_to_proc.add(data)
else:
eval_data2descr[data] = img_prob_descr_set[data.uuid()]
if d_to_proc:
eval_data2descr.update(
zip(d_to_proc, img_prob_gen.generate_elements(d_to_proc)))
d_to_proc.clear()
assert len(eval_data2descr) == eval_data_set.count()
index_additions = []