def setUp(self):
self.acts = np.array([[0, 1., 2.]])
self.examples = [None, None, None]
self.concepts = ['c1', 'c2']
self.target = 't1'
self.class_id = 0
self.bottleneck = 'bn'
self.cav_dir = None
self.hparams = {'model_type': 'linear', 'alpha': .01}
self.cav = CAV(self.concepts, self.bottleneck, self.hparams)
self.cav.cavs = [[
1.,
2.,
3.,
]]
self.activation_generator = None
self.mymodel = TcavTest_model()
self.act_gen = TcavTest_ActGen(self.mymodel)
self.random_counterpart = 'random500_1'
self.mytcav = TCAV(None, self.target, self.concepts, [self.bottleneck],
self.act_gen, [self.hparams['alpha']])
self.mytcav_random_counterpart = TCAV(None, self.target, self.concepts,
[self.bottleneck], self.act_gen,
[self.hparams['alpha']],
self.random_counterpart)
def setUp(self):
self.acts = np.array([[0, 1., 2.]])
self.concepts = ['c1', 'c2']
self.target = 't1'
self.class_id = 0
self.bottleneck = 'bn'
self.cav_dir = None
self.hparams = tf.contrib.training.HParams(model_type='linear',
alpha=.01)
self.cav = CAV(self.concepts, self.bottleneck, self.hparams)
self.cav.cavs = [[
1.,
2.,
3.,
]]
self.activation_generator = None
self.mymodel = TcavTest_model()
self.act_gen = TcavTest_ActGen(self.mymodel)
self.random_counterpart = 'random500_1'
self.mytcav = TCAV(None, self.target, self.concepts, [self.bottleneck],
self.act_gen, [self.hparams.alpha])
self.mytcav_random_counterpart = TCAV(None, self.target, self.concepts,
[self.bottleneck], self.act_gen,
[self.hparams.alpha],
self.random_counterpart)
def setUp(self):
"""Makes a cav instance and writes it to tmp direcotry.
The cav instance uses preset values.
"""
self.hparams = {
'model_type': 'linear',
'alpha': .01,
'max_iter': 1000,
'tol': 1e-3
}
self.concepts = ['concept1', 'concept2']
self.bottleneck = 'bottleneck'
self.accuracies = {'concept1': 0.8, 'concept2': 0.5, 'overall': 0.65}
self.cav_vecs = [[1, 2, 3], [4, 5, 6]]
self.test_subdirectory = os.path.join(FLAGS.tcav_test_tmpdir, 'test')
self.cav_dir = self.test_subdirectory
self.cav_file_name = CAV.cav_key(self.concepts, self.bottleneck,
self.hparams['model_type'],
self.hparams['alpha']) + '.pkl'
self.save_path = os.path.join(self.cav_dir, self.cav_file_name)
self.cav = CAV(self.concepts, self.bottleneck, self.hparams)
# pretend that it was trained and cavs are stored
self.cav.cavs = np.array(self.cav_vecs)
shape = (1, 3)
self.acts = {
concept: {
self.bottleneck: np.tile(i * np.ones(shape), (4, 1))
}
for i, concept in enumerate(self.concepts)
}
if os.path.exists(self.cav_dir):
shutil.rmtree(self.cav_dir)
os.mkdir(self.cav_dir)
with tf.io.gfile.GFile(self.save_path, 'w') as pkl_file:
pickle.dump(
{
'concepts': self.concepts,
'bottleneck': self.bottleneck,
'hparams': self.hparams,
'accuracies': self.accuracies,
'cavs': self.cav_vecs,
'saved_path': self.save_path
}, pkl_file)