def test_evaluate_sg(self):
paddle_model = mobilenet_v2(pretrained=True)
img_path = 'imgs/catdog.jpg'
sg = it.SmoothGradInterpreter(paddle_model, device='cpu')
exp = sg.interpret(img_path,
n_samples=5,
noise_amount=0.1,
visual=False,
labels=None,
save_path=None)
evaluator = it.DeletionInsertion(paddle_model, device='cpu')
r = evaluator.evaluate(img_path, exp)
def test_shape(self):
paddle_model = mobilenet_v2(pretrained=True)
img_path = 'imgs/catdog.jpg'
algo = it.SmoothGradInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path,
n_samples=1,
resize_to=256,
crop_to=224,
visual=False)
result = np.array([*exp.shape])
assert_arrays_almost_equal(self, result, np.array([1, 3, 224, 224]))
def test_algo(self):
paddle_model = mobilenet_v2(pretrained=True)
np.random.seed(42)
img_path = np.random.randint(0,
255,
size=(1, 64, 64, 3),
dtype=np.uint8)
algo = it.SmoothGradInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path, n_samples=5, visual=False)
result = np.array([exp.mean(), exp.std(), exp.min(), exp.max()])
desired = np.array(
[5.1930110e-06, 4.0496187e-03, -2.3638349e-02, 3.2775488e-02])
assert_arrays_almost_equal(self, result, desired)
def test_visual(self):
import matplotlib
matplotlib.use('agg') # non-GUI, for skipping.
paddle_model = mobilenet_v2(pretrained=True)
img_path = 'imgs/catdog.jpg'
algo = it.SmoothGradInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path,
n_samples=1,
resize_to=256,
crop_to=224,
visual=True,
save_path='tmp.jpg')
result = np.array([*exp.shape])
assert_arrays_almost_equal(self, result, np.array([1, 3, 224, 224]))
os.remove('tmp.jpg')