def test_global_avgpool(self):
in_channels = 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
pool = MinkowskiGlobalPooling()
output = pool(input)
print(output)
# Check backward
fn = MinkowskiGlobalPoolingFunction()
self.assertTrue(
gradcheck(fn, (input.F, True, GlobalPoolingMode.INDEX_SELECT,
input.coords_key, None, input.coords_man)))
self.assertTrue(
gradcheck(fn, (input.F, True, GlobalPoolingMode.SPARSE,
input.coords_key, None, input.coords_man)))
coords, feats, labels = data_loader(in_channels, batch_size=1)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
pool = MinkowskiGlobalPooling()
output = pool(input)
print(output)
# Check backward
fn = MinkowskiGlobalPoolingFunction()
self.assertTrue(
gradcheck(fn, (input.F, True, GlobalPoolingMode.AUTO,
input.coords_key, None, input.coords_man)))
def test_gpu(self):
if not torch.cuda.is_available():
return
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coordinates=coords)
pool = MinkowskiGlobalMaxPooling()
output = pool(input)
print(output)
if not torch.cuda.is_available():
return
input = SparseTensor(feats, coordinates=coords, device=0)
output = pool(input)
print(output)
# Check backward
fn = MinkowskiGlobalPoolingFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
pool.pooling_mode,
input.coordinate_map_key,
output.coordinate_map_key,
input._manager,
),
)
)
def test_global_avgpool(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 = MinkowskiGlobalPooling(dimension=D)
output = pool(input)
print(output)
# Check backward
fn = MinkowskiGlobalPoolingFunction()
self.assertTrue(
gradcheck(
fn, (input.F, True, input.coords_key, None, input.coords_man)))
def test(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords)
pool = MinkowskiGlobalAvgPooling()
output = pool(input)
print(output)
# Check backward
fn = MinkowskiGlobalPoolingFunction()
self.assertTrue(
gradcheck(
fn,
(
input.F,
pool.pooling_mode,
input.coordinate_map_key,
output.coordinate_map_key,
input._manager,
),
))
