def test_saliency(self):
fe.estimator.enable_deterministic(200)
label_mapping = {
'airplane': 0,
'automobile': 1,
'bird': 2,
'cat': 3,
'deer': 4,
'dog': 5,
'frog': 6,
'horse': 7,
'ship': 8,
'truck': 9
}
batch_size = 32
train_data, eval_data = cifar10.load_data()
pipeline = fe.Pipeline(test_data=train_data,
batch_size=batch_size,
ops=[Normalize(inputs="x", outputs="x")],
num_process=0)
weight_path = os.path.abspath(
os.path.join(__file__, "..", "resources", "lenet_cifar10_tf.h5"))
model = fe.build(model_fn=lambda: LeNet(input_shape=(32, 32, 3)),
optimizer_fn="adam",
weights_path=weight_path)
network = fe.Network(
ops=[ModelOp(model=model, inputs="x", outputs="y_pred")])
save_dir = tempfile.mkdtemp()
traces = [
Saliency(model=model,
model_inputs="x",
class_key="y",
model_outputs="y_pred",
samples=5,
label_mapping=label_mapping),
ImageSaver(inputs="saliency", save_dir=save_dir)
]
estimator = fe.Estimator(pipeline=pipeline,
network=network,
epochs=5,
traces=traces,
log_steps=1000)
estimator.test()
ans_img_path = os.path.abspath(
os.path.join(__file__, "..", "resources", "saliency_figure.png"))
ans_img = img_to_rgb_array(ans_img_path)
output_img_path = os.path.join(save_dir, "saliency_test_epoch_5.png")
output_img = img_to_rgb_array(output_img_path)
self.assertTrue(check_img_similar(output_img, ans_img))
def test_tf_output(self):
cutmix = CutMixBatch(inputs='x', outputs=['x', 'lam'])
cutmix.beta = MockBetaDistribution('tf')
cutmix.uniform = MockUniformDistribution('tf')
mixed_images = cutmix.forward(data=self.tf_input, state={})
images = mixed_images[0].numpy()
lam = mixed_images[1].numpy()
with self.subTest('First mixed image'):
self.assertTrue(
check_img_similar(images[0],
img_to_rgb_array(self.cutmix_output1)))
with self.subTest('Second mixed image'):
self.assertTrue(
check_img_similar(images[1],
img_to_rgb_array(self.cutmix_output2)))
with self.subTest('lambda value'):
self.assertEqual(round(float(lam), 2), 0.65)
def setUpClass(cls):
cls.color_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_color.png")))
cls.hw_ratio_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_height_width.png")))
cls.bb_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_bounding_box.png")))
cls.mixed_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_mixed.png")))
cls.text_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_text.png")))
cls.title_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_title.png")))
cls.float_img_ans = img_to_rgb_array(
os.path.abspath(
os.path.join(__file__, "..", "resources",
"test_show_image_check_float.png")))
def test_paint_numpy(self):
output_test = self.img_data.paint_numpy()
output_test = np.squeeze(output_test, axis=0)
output = img_to_rgb_array(self.output_img)
self.assertTrue(check_img_similar(output, output_test))
def test_paint_figure(self):
fig = self.img_data.paint_figure()
output = img_to_rgb_array(self.output_img)
output_test = fig_to_rgb_array(fig)
self.assertTrue(check_img_similar(output, output_test))