x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 0.4
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv2(x, edge_index, pseudo))
idx = fps(pos, batch, ratio=0.25)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 1
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv3(x, edge_index, pseudo))
x = global_mean_pool(x, batch)
x = F.elu(self.lin1(x))
x = F.elu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=-1)
train_dataset, test_dataset = get_dataset(num_points=1024)
model = Net(train_dataset.num_classes)
run(train_dataset, test_dataset, model, args.epochs, args.batch_size, args.lr,
args.lr_decay_factor, args.lr_decay_step_size, args.weight_decay)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv2(x, edge_index, pseudo))
idx = fps(pos, batch, ratio=0.25)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 1
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv3(x, edge_index, pseudo))
x = global_mean_pool(x, batch)
x = F.elu(self.lin1(x))
x = F.elu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=-1)
train_dataset, test_dataset = get_dataset(num_points=1024)
model = Net(train_dataset.num_classes)
run(train_dataset, test_dataset, model, args.epochs, args.batch_size, args.lr,
args.lr_decay_factor, args.lr_decay_step_size, args.weight_decay,
args.inference, args.profile)
if args.profile:
rename_profile_file('points', SplineConv.__name__)