def test_has(self):
i = MemoryDescriptorIndex()
descrs = [random_descriptor() for _ in xrange(10)]
i.add_many_descriptors(descrs)
ntools.assert_true(i.has_descriptor(descrs[4].uuid()))
ntools.assert_false(i.has_descriptor('not_an_int'))
def test_has(self):
i = MemoryDescriptorIndex()
descrs = [random_descriptor() for _ in range(10)]
i.add_many_descriptors(descrs)
ntools.assert_true(i.has_descriptor(descrs[4].uuid()))
ntools.assert_false(i.has_descriptor('not_an_int'))
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:
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(),
