darkon/gradcam/gradcam.py
Unused variable '__class__'
class Gradcam:
Dangerous default value dict() (builtins.dict) as argument
def gradcam(self, sess, input_data, target_index=None, feed_options=dict()):
darkon/influence/influence.py
Unused variable '__class__'
class Influence:
test/test_gradcam.py
Unused variable '__class__'
class TestGradcam(unittest.TestCase):
Unused variable 'net'
net, end_points = resnet_v1.resnet_v1_50(self.inputs, self.nbclasses, is_training=False)
Trailing whitespace
self.assertEqual('resnet_v1_50/block4/unit_3/bottleneck_v1/Relu', self.target_op_name)
Unused variable 'end_points'
net, end_points = vgg.vgg_16(self.inputs, self.nbclasses, is_training=False)
test/test_gradcam_dangling.py
Unused variable 'net'
net, end_points = resnet_v1.resnet_v1_50(inputs, self.nbclasses, is_training=False)
Trailing whitespace
test/test_gradcam_guided_backprop.py
Unused variable 'y'
y = tf.placeholder(tf.int32, name='y_placeholder', shape=[1, 2])
test/test_gradcam_sequence.py
Unused variable '__class__'
class TestGradcamSequence(unittest.TestCase):
Trailing whitespace
graph = tf.get_default_graph()
Trailing whitespace
conv_op_names = darkon.Gradcam.candidate_featuremap_op_names(sess,
Trailing whitespace
prob_op_names = darkon.Gradcam.candidate_predict_op_names(sess, 2,
Unused variable 'ret'
ret = insp.gradcam(sess, self.x_test_batch[0], feed_options={dropout_keep_prob: 1})
test/test_gradcam_util.py
Unused variable 'y'
x, y, cross_entropy = nn_graph(activation=tf.nn.relu)
test/test_influence_dropout.py
Unused variable '__class__'
class MyFeeder(darkon.InfluenceFeeder):
test/test_influence_feeder.py
Unused variable '__class__'
class MyFeeder(darkon.InfluenceFeeder):
Unused variable '__class__'
class TestInfluenceFeeder(unittest.TestCase):
Unused variable '__class__'
class ParentTestFeeder(darkon.InfluenceFeeder):
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