def test_count(self):
expected_map = {
0: 'a',
75: 'b',
124769: 'c',
}
dms = DataMemorySet()
dms._element_map = expected_map
self.assertEqual(dms.count(), 3)
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:
index_additions.append(eval_data2descr[data])
print "Adding %d new descriptors to prob index" % len(index_additions)
img_prob_descr_index.add_many_descriptors(index_additions)
eval_descr2class = img_prob_classifier.classify_async(eval_data2descr.values(),
img_c_mem_factory)
###############################################################################
# The shas that were actually computed
computed_shas = {e.uuid() for e in eval_data2descr}
len(computed_shas)
