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, coords=coords)
# Initialize context
conv = MinkowskiConvolution(in_channels,
out_channels,
kernel_size=3,
stride=2,
has_bias=True,
dimension=D)
conv = conv.double()
conv_tr = MinkowskiConvolutionTranspose(out_channels,
in_channels,
kernel_size=2,
stride=2,
has_bias=True,
dimension=D)
conv_tr = conv_tr.double()
input = conv(input)
output = conv_tr(input)
print(output)
# Check backward
fn = MinkowskiConvolutionTransposeFunction()
self.assertTrue(
gradcheck(fn,
(input.F, conv_tr.kernel, input.tensor_stride,
conv_tr.stride, conv_tr.kernel_size, conv_tr.dilation,
conv_tr.region_type_, conv_tr.region_offset_,
input.coords_key, None, input.coords_man)))
def test_gpu(self):
if not torch.cuda.is_available():
return
device = torch.device('cuda')
in_channels, out_channels, D = 2, 3, 2
coords, feats, labels = data_loader(in_channels)
feats = feats.double()
feats.requires_grad_()
input = SparseTensor(feats, coords=coords).to(device)
# Initialize context
conv = MinkowskiConvolution(in_channels,
out_channels,
kernel_size=3,
stride=2,
has_bias=True,
dimension=D).to(device)
conv = conv.double()
conv_tr = MinkowskiConvolutionTranspose(out_channels,
in_channels,
kernel_size=3,
stride=2,
has_bias=True,
dimension=D).to(device)
conv_tr = conv_tr.double()
input = conv(input)
output = conv_tr(input)
print(output)
# Check backward
fn = MinkowskiConvolutionTransposeFunction()
self.assertTrue(
gradcheck(fn,
(input.F, conv_tr.kernel, input.tensor_stride,
conv_tr.stride, conv_tr.kernel_size, conv_tr.dilation,
conv_tr.region_type_, conv_tr.region_offset_,
input.coords_key, None, input.coords_man)))