def test_four(self):
"""Equal duty cycle, boost factor=0, k=3, batch size=2."""
x = self.x2
expected = torch.zeros_like(x)
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
expected[1, 1, 0, 0] = x[1, 1, 0, 0]
expected[1, 1, 0, 1] = x[1, 1, 0, 1]
expected[1, 2, 1, 1] = x[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
result = F.kwinners2d(x,
self.duty_cycle,
k=3,
boost_strength=0.0,
local=False,
break_ties=break_ties)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def forward(self, x):
if self.n == 0:
self.n = np.prod(x.shape[1:])
if not self.local:
self.k = int(round(self.n * self.percent_on))
self.k_inference = int(round(self.n * self.percent_on_inference))
if self.training:
x = F.kwinners2d(x, self.duty_cycle, self.k,
self._cached_boost_strength, self.local,
self.break_ties, self.relu, self.inplace)
self.update_duty_cycle(x)
else:
x = F.kwinners2d(x, self.duty_cycle, self.k_inference,
self._cached_boost_strength, self.local,
self.break_ties, self.relu, self.inplace)
return x