def test_sum_pool(self):
in_shape = (3, 3, 1)
net_init = pooling.SumPooling((2, 2))
net_rng = self._seed
out_shape = net_init.spec(state.Shape(in_shape)).shape
layer = net_init.init(net_rng, state.Shape(in_shape))
x = np.array([[-1, 0, -1], [0, 1, 0], [-1, 0, -1]])
x = np.reshape(x, in_shape)
result = layer(x)
self.assertEqual(result.shape, out_shape)
np.testing.assert_equal(result, np.zeros(out_shape))
def test_sum_pool_batched(self):
in_shape = (3, 3, 1)
batch_size = 10
batch_in_shape = (batch_size,) + in_shape
net_init = pooling.SumPooling((2, 2))
net_rng = self._seed
with self.assertRaises(ValueError):
_ = net_init.spec(state.Shape(batch_in_shape)).shape
out_shape = net_init.spec(state.Shape(in_shape)).shape
batch_out_shape = (batch_size,) + out_shape
layer = net_init.init(net_rng, state.Shape(in_shape))
x = np.tile(np.array([[-1, 0, -1], [0, 1, 0], [-1, 0, -1]])[None],
(batch_size, 1, 1))
x = np.reshape(x, batch_in_shape)
with self.assertRaises(ValueError):
layer(x)
result = jax.vmap(layer)(x)
self.assertEqual(result.shape, batch_out_shape)
np.testing.assert_equal(result, np.zeros(batch_out_shape))