def test_cv_multiple_inputs(self):
paddle_model = mobilenet_v2(pretrained=True)
img_path = ['imgs/catdog.jpg', 'imgs/catdog.jpg']
algo = it.IntGradCVInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path, steps=3, num_random_trials=2, resize_to=256, crop_to=224, visual=False)
result = np.array([*exp.shape])
assert_arrays_almost_equal(self, result, np.array([2, 3, 224, 224]))
def test_save(self):
import matplotlib
matplotlib.use('agg') # non-GUI, for skipping.
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.IntGradCVInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path, steps=3, num_random_trials=2, visual=True, save_path='tmp.jpg')
os.remove('tmp.jpg')
def test_algo_random(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.IntGradCVInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path, steps=3, num_random_trials=2, baselines='random', visual=False)
result = np.array([exp.mean(), exp.std(), exp.min(), exp.max()])
desired = np.array([ 4.9712045e-05, 2.4339566e-03, -1.9965716e-02, 2.3791827e-02])
assert_arrays_almost_equal(self, result, desired)
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.IntGradCVInterpreter(paddle_model, device='cpu')
exp = algo.interpret(img_path, steps=3, num_random_trials=2, visual=False)
result = np.array([exp.mean(), exp.std(), exp.min(), exp.max()])
print(repr(result))
desired = np.array([ 4.1104166e-05, 2.1593589e-03, -1.8412363e-02, 2.2863150e-02])
assert_arrays_almost_equal(self, result, desired)
def int_grad_example():
def paddle_model(data):
class_num = 1000
model = ResNet50()
logits = model.net(input=data, class_dim=class_num)
probs = fluid.layers.softmax(logits, axis=-1)
return probs
img_path = 'assets/fireboat.png'
#https://github.com/PaddlePaddle/models/tree/release/1.8/PaddleCV/image_classification
ig = it.IntGradCVInterpreter(paddle_model, "assets/ResNet50_pretrained",
True)
gradients = ig.interpret(img_path,
label=None,
baseline='random',
steps=50,
num_random_trials=2,
visual=True,
save_path='ig_test.jpg')