if args.skip_detection:
dataset = torch_utils.FaceDataset(x, preprocess)
else:
imgs = pipeline.load_faces(x, gpu=args.gpu)
dataset = torch_utils.FaceDataset(imgs, preprocess, x_image=True)
res = torch_utils.evaluate(model, loader(dataset), gpu=args.gpu)
misc.display_result(x, misc.softmax(res))
else:
if tw_face.data_exist():
print('evaluate the model on Taiwanese faces')
tw_loader = lambda data: loader(
torch_utils.FaceDatasetWithLabel(*data, preprocess), )
data = tw_face.read_data('young')
torch_utils.eval_acc(model,
tw_loader(data),
data[1],
'TW face young',
gpu=args.gpu)
data = tw_face.read_data('old')
torch_utils.eval_acc(model,
tw_loader(data),
data[1],
'TW face old',
gpu=args.gpu)
else:
print(
'please download Taiwanese faces data first, or use --input / --input_folder to specify inputs'
def test_label(self):
x, y = tw_face.read_data()
for yy in y.flat:
self.assertIn(yy, range(7))
def test_readable(self):
x, y = tw_face.read_data()
for xx in x:
Image.open(xx).close()
def test_all_count(self):
x, y = tw_face.read_data()
self.assertEqual(len(x), self.young_size + self.old_size)
self.assertEqual(y.shape, (self.young_size + self.old_size, ))
def test_young_count(self):
x, y = tw_face.read_data('young')
self.assertEqual(len(x), self.young_size)
self.assertEqual(y.shape, (self.young_size, ))
def test_old_count(self):
x, y = tw_face.read_data('old')
self.assertEqual(len(x), self.old_size)
self.assertEqual(y.shape, (self.old_size, ))