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 = []