def test_avgpooling_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, coords=coords)
pool = MinkowskiAvgPooling(kernel_size=3, stride=2, dimension=D)
output = pool(input)
print(output)
device = torch.device('cuda')
with torch.cuda.device(0):
input = input.to(device)
pool = pool.to(device)
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_, True,
input.coords_key, None, input.coords_man)))
def test_unpooling_gpu(self):
if not torch.cuda.is_available():
return
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
input = SparseTensor(feats, coords=coords)
conv = MinkowskiConvolution(in_channels,
out_channels,
kernel_size=3,
stride=2,
dimension=D)
conv = conv.double()
unpool = MinkowskiPoolingTranspose(kernel_size=3,
stride=2,
dimension=D)
input = conv(input)
output = unpool(input)
print(output)
# Check backward
fn = MinkowskiPoolingTransposeFunction()
self.assertTrue(
gradcheck(fn, (input.F, input.tensor_stride, unpool.stride,
unpool.kernel_size, unpool.dilation,
unpool.region_type_, unpool.region_offset_, False,
input.coords_key, None, input.coords_man)))
device = torch.device('cuda')
with torch.cuda.device(0):
input = input.to(device)
output = unpool(input)
print(output)
# Check backward
fn = MinkowskiAvgPoolingFunction()
self.assertTrue(
gradcheck(fn, (input.F, input.tensor_stride, unpool.stride,
unpool.kernel_size, unpool.dilation,
unpool.region_type_, unpool.region_offset_, True,
input.coords_key, None, input.coords_man)))