def __init__(self,
num_features,
eps=1e-5,
momentum=0.99,
last_gamma=False,
D=-1):
super(MinkowskiSwitchNorm, self).__init__()
self.eps = eps
self.momentum = momentum
self.last_gamma = last_gamma
self.weight = nn.Parameter(torch.ones(1, num_features))
self.bias = nn.Parameter(torch.zeros(1, num_features))
self.mean_weight = nn.Parameter(torch.ones(3))
self.var_weight = nn.Parameter(torch.ones(3))
self.register_buffer('running_mean', torch.zeros(1, num_features))
self.register_buffer('running_var', torch.zeros(1, num_features))
self.mean_in = MinkowskiGlobalPooling(dimension=D)
self.glob_sum = MinkowskiBroadcastAddition(dimension=D)
self.glob_sum2 = MinkowskiBroadcastAddition(dimension=D)
self.glob_mean = MinkowskiGlobalPooling(dimension=D)
self.glob_times = MinkowskiBroadcastMultiplication(dimension=D)
self.D = D
self.reset_parameters()
def __init__(self, num_features, eps=1e-5, D=-1):
super(MinkowskiInstanceNorm, self).__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(1, num_features))
self.bias = nn.Parameter(torch.zeros(1, num_features))
self.mean_in = MinkowskiGlobalPooling(dimension=D)
self.glob_sum = MinkowskiBroadcastAddition(dimension=D)
self.glob_sum2 = MinkowskiBroadcastAddition(dimension=D)
self.glob_mean = MinkowskiGlobalPooling(dimension=D)
self.glob_times = MinkowskiBroadcastMultiplication(dimension=D)
self.D = D
self.reset_parameters()
def test_broadcast(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
coords, feats_glob, labels = data_loader(in_channels)
feats = feats.double()
feats_glob = feats_glob.double()
input = SparseTensor(feats, coords=coords)
pool = MinkowskiGlobalPooling(dimension=D)
input_glob = pool(input)
input_glob.F.requires_grad_()
broadcast = MinkowskiBroadcastAddition(D)
broadcast_mul = MinkowskiBroadcastMultiplication(D)
output = broadcast(input, input_glob)
print(output)
output = broadcast_mul(input, input_glob)
print(output)
# Check backward
fn = MinkowskiBroadcastFunction()
self.assertTrue(
gradcheck(
fn,
(input.F, input_glob.F, OperationType.ADDITION,
input.coords_key, input_glob.coords_key, input.coords_man)))
self.assertTrue(
gradcheck(
fn,
(input.F, input_glob.F, OperationType.MULTIPLICATION,
input.coords_key, input_glob.coords_key, input.coords_man)))
def test_broadcast_gpu(self):
in_channels, D = 2, 2
coords, feats, labels = data_loader(in_channels)
coords, feats_glob, labels = data_loader(in_channels)
feats = feats.double()
feats_glob = feats_glob.double()
input = SparseTensor(feats, coords=coords)
pool = MinkowskiGlobalPooling()
input_glob = pool(input)
input_glob.F.requires_grad_()
broadcast_add = MinkowskiBroadcastAddition()
broadcast_mul = MinkowskiBroadcastMultiplication()
broadcast_cat = MinkowskiBroadcastConcatenation()
cpu_add = broadcast_add(input, input_glob)
cpu_mul = broadcast_mul(input, input_glob)
cpu_cat = broadcast_cat(input, input_glob)
# Check backward
fn = MinkowskiBroadcastFunction()
device = torch.device('cuda')
input = input.to(device)
input_glob = input_glob.to(device)
gpu_add = broadcast_add(input, input_glob)
gpu_mul = broadcast_mul(input, input_glob)
gpu_cat = broadcast_cat(input, input_glob)
self.assertTrue(
torch.prod(gpu_add.F.cpu() - cpu_add.F < 1e-5).item() == 1)
self.assertTrue(
torch.prod(gpu_mul.F.cpu() - cpu_mul.F < 1e-5).item() == 1)
self.assertTrue(
torch.prod(gpu_cat.F.cpu() - cpu_cat.F < 1e-5).item() == 1)
self.assertTrue(
gradcheck(
fn,
(input.F, input_glob.F, OperationType.ADDITION,
input.coords_key, input_glob.coords_key, input.coords_man)))
self.assertTrue(
gradcheck(
fn,
(input.F, input_glob.F, OperationType.MULTIPLICATION,
input.coords_key, input_glob.coords_key, input.coords_man)))
def sparse_global_sum(self, x, y):
return MinkowskiBroadcastAddition(dimension=self.D)(x, y)