def test_sumpooling(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
pool = MinkowskiSumPooling(kernel_size=3, stride=2, dimension=D)
output = pool(input)
print(output)
# Check backward
fn = MinkowskiAvgPoolingFunction()
self.assertTrue(
gradcheck(
fn,
(input.F, input.tensor_stride, pool.stride, pool.kernel_size,
pool.dilation, pool.region_type_, pool.region_offset_, False,
input.coords_key, None, input.coords_man)))
device = torch.device('cuda')
with torch.cuda.device(0):
input = input.to(device)
pool = pool.to(device)
output = pool(input)
print(output)
def test_sumpooling(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
pool = MinkowskiSumPooling(kernel_size=3, stride=2, dimension=D)
output = pool(input)
print(output)
# Check backward
fn = MinkowskiLocalPoolingFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
pool.pooling_mode,
pool.kernel_generator,
input.coordinate_map_key,
output.coordinate_map_key,
input._manager,
),
)
)
input = SparseTensor(feats, coords, device=0)
output = pool(input)
print(output)
self.assertTrue(
gradcheck(
fn,
(
input.F,
pool.pooling_mode,
pool.kernel_generator,
input.coordinate_map_key,
output.coordinate_map_key,
input._manager,
),
)
)
