def test_garbage(self):
with restore_gc_state():
gc.disable()
a = []
b = []
c = []
a.append(a)
b.append(c)
c.append(b)
del a, b, c
graph = garbage()
self.assertEqual(len(gc.garbage), 0)
self.assertEqual(len(graph), 3)
# A second call to garbage should
# produce nothing new.
graph2 = garbage()
self.assertEqual(gc.garbage, [])
self.assertEqual(len(graph2), 0)
# But if we delete graph then
# a, b and c become collectable again.
del graph
graph = garbage()
self.assertEqual(gc.garbage, [])
self.assertEqual(len(graph), 3)
# Get rid of everything.
del graph, graph2
gc.collect()
graph = garbage()
self.assertEqual(gc.garbage, [])
self.assertEqual(len(graph), 0)
def tearDown(self):
interesting = []
for o in gc.get_objects():
if isinstance(o, (torch.Tensor, Dim, Tensor,
DimList)) and id(o) not in self.interesting:
interesting.append(o)
extra_memory = 0
if 'cuda' in self._testMethodName:
extra_memory += torch.cuda.memory_allocated() - self.mem_allocated
# nolevels = _n_levels_in_use() == 0
if extra_memory != 0 or len(interesting) != 0:
import refcycle
refcycle.garbage().export_image('garbage.pdf')
gc.collect()
# assert nolevels, f"cleanup failed? {_n_levels_in_use()}"
assert extra_memory == 0, f'extra cuda memory left allocated: {extra_memory}'
assert len(interesting) == 0, \
f'extra torch.Tensor, Dim, or Tensor left allocated: {len(interesting)} objects of types:' \
f' { [type(t) for t in interesting] }'
import refcycle
import gc
gc.disable()
gc.collect()
class A(object):
pass
a = A()
b = A()
a.foo = b
b.foo = a
del a, b, A
graph = refcycle.garbage()
graph.export_image('garbage.svg')
graph.export_image('garbage.pdf')
sccs = graph.strongly_connected_components()
sccs
sccs.sort(key=len)
sccs[-1].export_image('scc1.svg')
sccs[-1].export_image('scc1.pdf')
sccs[-2].export_image('scc2.svg')
sccs[-2].export_image('scc2.pdf')
print(graph.source_components())
