def test_add_new_element(self):
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20)[None, ...]
om.add_new_element(t1, 1)
npt.assert_equal(utils.t2a(om.M), np.arange(10, 20).reshape((1, 10)))
npt.assert_equal(om.seq_ids, np.arange(1, 2))
g_set = set([1])
self.assertEqual(set(om.G.nodes), g_set)
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20)[None, ...]
t2 = torch.arange(20, 30)[None, ...]
t3 = torch.arange(30, 40)[None, ...]
om.add_new_element(t1, 1)
om.add_new_element(t2, 2)
om.add_new_element(t3, 3)
npt.assert_equal(utils.t2a(om.M), np.arange(10, 40).reshape((3, 10)))
npt.assert_equal(om.seq_ids, np.arange(1, 4))
g_set = set([1, 2, 3])
self.assertEqual(set(om.G.nodes), g_set)
def test_predict(self):
data = simpledataset()
sup = simplesupervisor(data[1])
agent = ag.ActiveAgent(0.5,
1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
output = [
agent.process_next([data[0][itx]], data[2][itx])
for itx in range(len(data[0]))
]
predictions = [agent.predict([d])[1] for d in data[0]]
is_known = [agent.predict([d])[0] for d in data[0]]
npt.assert_equal(np.concatenate(predictions), data[2])
all_pred = agent.predict(list(data[0]))
npt.assert_equal(np.concatenate(is_known), all_pred[0])
npt.assert_equal(np.concatenate(predictions), all_pred[1])
npt.assert_equal(agent.predict(list(data[0])), all_pred)
def test_process_next_active(self):
data = simpledataset()
sup = simplesupervisor(data[1])
agent = ag.ActiveAgent(0.5,
1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
output = [
agent.process_next([data[0][itx]], data[2][itx])
for itx in range(len(data[0]))
]
asked_sup = np.array([o[2] for o in output])
for itx in range(len(output)):
with self.subTest(n=itx):
if itx > 0:
self.assertTrue(output[itx][1] < itx)
self.assertTrue(len(agent.sup_mem), asked_sup.sum())
def test_refine(self):
data = simpledataset()
sup = simplesupervisor(data[1])
def refine(agent):
optim = torch.optim.sgd()
l = loss.ContrastiveLoss()
e = ag.create_siamese_trainer(agent, optim, l)
sampler = samp.SeadableRandomSampler(agent.sup_mem, 1)
data_loader = torch.utils.data.DataLoader(agent.sup_mem,
sampler=sampler)
e.run(data_loader, max_epochs=2)
agent = ag.Agent(1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate,
refine=refine)
for itx in range(len(data[0])):
with self.subTest(n=itx):
out = agent.process_next([data[0][itx]], data[2][itx])
if itx < 2 and itx != 0:
self.assertTrue(out[2])
self.assertEqual(len(agent.obj_mem), itx + 1)
self.assertEqual(len(agent.sup_mem), itx)
def test_process_next_internals(self):
data = simpledataset()
sup = simplesupervisor(data[1])
agent = ag.Agent(1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
for itx in range(len(data[0])):
with self.subTest(n=itx):
out = agent.process_next([data[0][itx]], data[2][itx])
if itx < 2 and itx != 0:
self.assertTrue(out[2])
self.assertEqual(len(agent.obj_mem), itx + 1)
self.assertEqual(len(agent.sup_mem), itx)
data = simpledataset()
sup = simplesupervisor(data[1])
def af(seed, sup):
torch.manual_seed(seed)
agent = ag.Agent(seed,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
return agent
def tets_len(self):
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20)
t2 = torch.arange(20, 30)
t3 = torch.arange(30, 40)
om.add_new_element(t1, 1)
om.add_new_element(t2, 2)
om.add_new_element(t3, 3)
self.assertEqual(len(om), 3)
self.assertEqual(om.sequences, 3)
def test_get_something(self):
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20)
t2 = torch.arange(20, 30)
t3 = torch.arange(30, 40)
om.add_new_element(t1, 1)
om.add_new_element(t2, 2)
om.add_new_element(t3, 3)
npt.assert_equal(om.get_sid(0), 1)
npt.assert_equal(utils.t2a(om.get_embed(0)), utils.t2a(t1))
def test_supervision(self):
data = simpledataset()
sup = simplesupervisor(data[1])
agent = ag.ActiveAgent(1.0,
1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
output = np.array([
agent.process_next([data[0][itx]], data[2][itx])
for itx in range(len(data[0]))
])
self.assertTrue(output[1:, 2].any())
agent = ag.ActiveAgent(0.01,
1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
output = np.array([
agent.process_next([data[0][itx]], data[2][itx])
for itx in range(len(data[0]))
])
self.assertFalse(output[1:, 2].all())
def test_get_knn(self):
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20).float()
t2 = torch.arange(20, 30).float()
t3 = torch.arange(40, 50).float()
om.add_new_element(t1, 1)
om.add_new_element(t2, 2)
om.add_new_element(t3, 3)
npt.assert_equal(utils.t2a(om.get_knn(t1, k=1)[1]), 0)
npt.assert_equal(utils.t2a(om.get_knn(t1, k=2)[1]), np.array([[0, 1]]))
t12 = torch.stack([t1, t2])
npt.assert_equal(utils.t2a(om.get_knn(t12, k=1)[1]),
np.array([[0], [1]]))
npt.assert_equal(utils.t2a(om.get_knn(t12, k=2)[1]),
np.array([[0, 1], [1, 0]]))
def test_add_neighbors(self):
om = ag.ObjectsMemory()
t1 = torch.arange(10, 20)[None, ...]
t2 = torch.arange(20, 30)[None, ...]
t3 = torch.arange(30, 40)[None, ...]
om.add_new_element(t1, 1)
om.add_new_element(t2, 2)
om.add_new_element(t3, 3)
om.add_neighbors(3, [0])
npt.assert_equal(utils.t2a(om.M), np.arange(10, 40).reshape((3, 10)))
npt.assert_equal(om.seq_ids, np.arange(1, 4))
g_set = set([1, 2, 3])
self.assertEqual(set(om.G.nodes), g_set)
e_set = set([(1, 3)])
self.assertEqual(set([tuple(sorted(e)) for e in om.G.edges]), e_set)
def test_process_next_out(self):
data = simpledataset()
sup = simplesupervisor(data[1])
agent = ag.Agent(1,
ag.ObjectsMemory(),
ag.SupervisionMemory(),
simplemodel(),
sup,
bootstrap=2,
max_neigh_check=1,
add_seen_element=ag.add_seen_separate)
output = [
agent.process_next([data[0][itx]], data[2][itx])
for itx in range(len(data[0]))
]
for itx in range(1, len(output)):
with self.subTest(n=itx):
self.assertTrue(output[itx][1] < itx)
self.assertTrue(output[itx][2])