def test_gpu(self):
print(f"{self.__class__.__name__}: test_gpu")
if not torch.cuda.is_available():
return
device = torch.device('cuda')
in_channels, D = 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
# Create random coordinates with tensor stride == 2
out_coords, tensor_stride = get_random_coords()
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords).to(device)
conv = MinkowskiChannelwiseConvolution(
in_channels,
kernel_size=3,
stride=1,
bias=False,
dimension=D).double().to(device)
print('Initial input: ', input)
output = conv(input)
print('Conv output: ', output)
def test(self):
print(f"{self.__class__.__name__}: test")
in_channels, D = 3, 2
coords, feats, labels = data_loader(in_channels, batch_size=2)
# Create random coordinates with tensor stride == 2
out_coords, tensor_stride = get_random_coords()
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords)
conv = MinkowskiChannelwiseConvolution(
in_channels,
kernel_size=3,
stride=1,
bias=False,
dimension=D).double()
print('Initial input: ', input)
output = conv(input)
print('Conv output: ', output)
output.F.sum().backward()
print(input.F.grad)
def test(self):
print(f"{self.__class__.__name__}: test")
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coordinates=coords)
# Initialize context
conv = MinkowskiChannelwiseConvolution(
in_channels, kernel_size=3, stride=2, bias=True, dimension=D
)
conv = conv.double()
output = conv(input)
print(output)
self.assertEqual(input.coordinate_map_key.get_tensor_stride(), [1, 1])
self.assertEqual(output.coordinate_map_key.get_tensor_stride(), [2, 2])