def test_graph_store():
graph_store = MyGraphStore()
edge_index = torch.LongTensor([[0, 1], [1, 2]])
adj = SparseTensor(row=edge_index[0], col=edge_index[1])
def assert_equal_tensor_tuple(expected, actual):
assert len(expected) == len(actual)
for i in range(len(expected)):
assert torch.equal(expected[i], actual[i])
# We put all three tensor types: COO, CSR, and CSC, and we get them back
# to confirm that `GraphStore` works as intended.
coo = adj.coo()[:-1]
csr = adj.csr()[:-1]
csc = adj.csc()[-2::-1] # (row, colptr)
# Put:
graph_store['edge', EdgeLayout.COO] = coo
graph_store['edge', 'csr'] = csr
graph_store['edge', 'csc'] = csc
# Get:
assert_equal_tensor_tuple(coo, graph_store['edge', 'coo'])
assert_equal_tensor_tuple(csr, graph_store['edge', 'csr'])
assert_equal_tensor_tuple(csc, graph_store['edge', 'csc'])
# Get attrs:
edge_attrs = graph_store.get_all_edge_attrs()
assert len(edge_attrs) == 3
with pytest.raises(KeyError):
_ = graph_store['edge_2', 'coo']
def forward(self, x: Tensor, M: SparseTensor, batch: OptTensor = None):
""""""
if batch is None:
batch = x.new_zeros(x.size(0), dtype=torch.long)
row, col, edge_weight = M.coo()
score1 = (x * self.p).sum(dim=-1)
score2 = scatter_add(edge_weight, col, dim=0, dim_size=x.size(0))
score = self.beta[0] * score1 + self.beta[1] * score2
if self.min_score is None:
score = self.nonlinearity(score)
else:
score = softmax(score, batch)
perm = topk(score, self.ratio, batch, self.min_score)
x = x[perm] * score[perm].view(-1, 1)
x = self.multiplier * x if self.multiplier != 1 else x
edge_index = torch.stack([col, row], dim=0)
edge_index, edge_attr = filter_adj(edge_index, edge_weight, perm,
num_nodes=score.size(0))
return x, edge_index, edge_attr, batch[perm], perm, score[perm]
def forward(self, x, edge_index, edge_weight=None, batch=None):
""""""
N = x.size(0)
edge_index, edge_weight = add_remaining_self_loops(edge_index,
edge_weight,
fill_value=1,
num_nodes=N)
if batch is None:
batch = edge_index.new_zeros(x.size(0))
x = x.unsqueeze(-1) if x.dim() == 1 else x
x_pool = x
if self.GNN is not None:
x_pool = self.gnn_intra_cluster(x=x,
edge_index=edge_index,
edge_weight=edge_weight)
x_pool_j = x_pool[edge_index[0]]
x_q = scatter(x_pool_j, edge_index[1], dim=0, reduce='max')
x_q = self.lin(x_q)[edge_index[1]]
score = self.att(torch.cat([x_q, x_pool_j], dim=-1)).view(-1)
score = F.leaky_relu(score, self.negative_slope)
score = softmax(score, edge_index[1], num_nodes=N)
# Sample attention coefficients stochastically.
score = F.dropout(score, p=self.dropout, training=self.training)
v_j = x[edge_index[0]] * score.view(-1, 1)
x = scatter(v_j, edge_index[1], dim=0, reduce='add')
# Cluster selection.
fitness = self.gnn_score(x, edge_index).sigmoid().view(-1)
perm = topk(fitness, self.ratio, batch)
x = x[perm] * fitness[perm].view(-1, 1)
batch = batch[perm]
# Graph coarsening.
row, col = edge_index
A = SparseTensor(row=row,
col=col,
value=edge_weight,
sparse_sizes=(N, N))
S = SparseTensor(row=row, col=col, value=score, sparse_sizes=(N, N))
S = S[:, perm]
A = S.t() @ A @ S
if self.add_self_loops:
A = A.fill_diag(1.)
else:
A = A.remove_diag()
row, col, edge_weight = A.coo()
edge_index = torch.stack([row, col], dim=0)
return x, edge_index, edge_weight, batch, perm
def graclus_coarsen(A: SparseTensor, level: int):
row, col, wgt = A.coo()
coarsen_cluster = []
for i in range(level):
cluster = graclus_cluster(row, col, wgt)
_, cluster = cluster.unique(return_inverse=True)
(row, col), wgt = pool_edge(cluster, torch.stack([row, col]), wgt)
coarsen_cluster.append(cluster.cpu().numpy())
return row, col, wgt, coarsen_cluster
def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
""""""
if x.dim() > 1:
assert (x.sum(dim=-1) == 1).sum() == x.size(0)
x = x.argmax(dim=-1) # one-hot -> integer.
assert x.dtype == torch.long
adj_t = edge_index
if not isinstance(adj_t, SparseTensor):
adj_t = SparseTensor(row=edge_index[1],
col=edge_index[0],
sparse_sizes=(x.size(0), x.size(0)))
out = []
_, col, _ = adj_t.coo()
deg = adj_t.storage.rowcount().tolist()
for node, neighbors in zip(x.tolist(), x[col].split(deg)):
idx = hash(tuple([node] + neighbors.sort()[0].tolist()))
if idx not in self.hashmap:
self.hashmap[idx] = len(self.hashmap)
out.append(self.hashmap[idx])
return torch.tensor(out, device=x.device)
def __dropout_adj__(self, sparse_adj: SparseTensor,
dropout_adj_prob: float):
# number of nodes
N = sparse_adj.size(0)
# sparse adj matrix to dense adj matrix
row, col, edge_attr = sparse_adj.coo()
edge_index = torch.stack([row, col], dim=0)
# dropout adjacency matrix -> generalization
edge_index, edge_attr = dropout_adj(edge_index,
edge_attr=edge_attr,
p=dropout_adj_prob,
force_undirected=True,
training=self.training)
# because dropout removes self-loops (due to force_undirected=True), make sure to add them back again
edge_index, edge_attr = add_remaining_self_loops(edge_index,
edge_weight=edge_attr,
fill_value=0.00,
num_nodes=N)
# dense adj matrix to sparse adj matrix
sparse_adj = SparseTensor.from_edge_index(edge_index,
edge_attr=edge_attr,
sparse_sizes=(N, N))
return sparse_adj
class GraphSAINTSampler(object):
r"""The GraphSAINT sampler base class from the `"GraphSAINT: Graph
Sampling Based Inductive Learning Method"
<https://arxiv.org/abs/1907.04931>`_ paper.
Given a graph in a :obj:`data` object, this class samples nodes and
constructs subgraphs that can be processed in a mini-batch fashion.
Normalization coefficients for each mini-batch are given via
:obj:`node_norm` and :obj:`edge_norm` data attributes.
.. note::
See :class:`torch_geometric.data.GraphSAINTNodeSampler`,
:class:`torch_geometric.data.GraphSAINTEdgeSampler` and
:class:`torch_geometric.data.GraphSAINTRandomWalkSampler` for
currently supported samplers.
For an example of using GraphSAINT sampling, see
`examples/graph_saint.py <https://github.com/rusty1s/pytorch_geometric/
blob/master/examples/graph_saint.py>`_.
Args:
data (torch_geometric.data.Data): The graph data object.
batch_size (int): The approximate number of samples per batch to load.
num_steps (int, optional): The number of iterations.
(default: :obj:`1`)
sample_coverage (int): How many samples per node should be used to
compute normalization statistics. (default: :obj:`50`)
save_dir (string, optional): If set, will save normalization
statistics to the :obj:`save_dir` directory for faster re-use.
(default: :obj:`None`)
num_workers (int, optional): How many subprocesses to use for data
sampling.
:obj:`0` means that the data will be sampled in the main process.
(default: :obj:`0`)
"""
def __init__(self, data, batch_size, num_steps=1, sample_coverage=50,
save_dir=None, num_workers=0):
assert data.edge_index is not None
assert 'node_norm' not in data
assert 'edge_norm' not in data
self.N = N = data.num_nodes
self.E = data.num_edges
self.adj = SparseTensor(row=data.edge_index[0], col=data.edge_index[1],
value=data.edge_attr, sparse_sizes=(N, N))
self.data = copy.copy(data)
self.data.edge_index = None
self.data.edge_attr = None
self.batch_size = batch_size
self.num_steps = num_steps
self.sample_coverage = sample_coverage
self.num_workers = num_workers
self.__count__ = 0
if self.num_workers > 0:
self.__sample_queue__ = Queue()
self.__sample_workers__ = []
for _ in range(self.num_workers):
worker = Process(target=self.__put_sample__,
args=(self.__sample_queue__, ))
worker.daemon = True
worker.start()
self.__sample_workers__.append(worker)
path = osp.join(save_dir or '', self.__filename__)
if save_dir is not None and osp.exists(path):
self.node_norm, self.edge_norm = torch.load(path)
else:
self.node_norm, self.edge_norm = self.__compute_norm__()
if save_dir is not None:
torch.save((self.node_norm, self.edge_norm), path)
if self.num_workers > 0:
self.__data_queue__ = Queue()
self.__data_workers__ = []
for _ in range(self.num_workers):
worker = Process(target=self.__put_data__,
args=(self.__data_queue__, ))
worker.daemon = True
worker.start()
self.__data_workers__.append(worker)
@property
def __filename__(self):
return f'{self.__class__.__name__.lower()}_{self.sample_coverage}.pt'
def __sample_nodes__(self, num_examples):
raise NotImplementedError
def __sample__(self, num_examples):
node_samples = self.__sample_nodes__(num_examples)
samples = []
for node_idx in node_samples:
node_idx = node_idx.unique()
adj, edge_idx = self.adj.saint_subgraph(node_idx)
samples.append((node_idx, edge_idx, adj))
return samples
def __compute_norm__(self):
node_count = torch.zeros(self.N, dtype=torch.float)
edge_count = torch.zeros(self.E, dtype=torch.float)
num_samples = total_sampled_nodes = 0
pbar = tqdm(total=self.N * self.sample_coverage)
pbar.set_description('Compute GraphSAINT normalization')
while total_sampled_nodes < self.N * self.sample_coverage:
num_sampled_nodes = 0
if self.num_workers > 0:
for _ in range(200):
node_idx, edge_idx, _ = self.__sample_queue__.get()
node_count[node_idx] += 1
edge_count[edge_idx] += 1
num_sampled_nodes += node_idx.size(0)
else:
samples = self.__sample__(200)
for node_idx, edge_idx, _ in samples:
node_count[node_idx] += 1
edge_count[edge_idx] += 1
num_sampled_nodes += node_idx.size(0)
pbar.update(num_sampled_nodes)
total_sampled_nodes += num_sampled_nodes
num_samples += 200
pbar.close()
row, col, _ = self.adj.coo()
edge_norm = (node_count[col] / edge_count).clamp_(0, 1e4)
edge_norm[torch.isnan(edge_norm)] = 0.1
node_count[node_count == 0] = 0.1
node_norm = num_samples / node_count / self.N
return node_norm, edge_norm
def __get_data_from_sample__(self, sample):
node_idx, edge_idx, adj = sample
data = self.data.__class__()
data.num_nodes = node_idx.size(0)
row, col, value = adj.coo()
data.edge_index = torch.stack([row, col], dim=0)
data.edge_attr = value
for key, item in self.data:
if item.size(0) == self.N:
data[key] = item[node_idx]
elif item.size(0) == self.E:
data[key] = item[edge_idx]
else:
data[key] = item
data.node_norm = self.node_norm[node_idx]
data.edge_norm = self.edge_norm[edge_idx]
return data
def __put_sample__(self, queue):
while True:
sample = self.__sample__(1)[0]
queue.put(sample)
def __put_data__(self, queue):
while True:
sample = self.__sample_queue__.get()
data = self.__get_data_from_sample__(sample)
queue.put(data)
def __next__(self):
if self.__count__ < len(self):
self.__count__ += 1
if self.num_workers > 0:
data = self.__data_queue__.get()
else:
sample = self.__sample__(1)[0]
data = self.__get_data_from_sample__(sample)
return data
else:
raise StopIteration
def __len__(self):
return self.num_steps
def __iter__(self):
self.__count__ = 0
return self
class GraphSAINTSampler(torch.utils.data.DataLoader):
r"""The GraphSAINT sampler base class from the `"GraphSAINT: Graph
Sampling Based Inductive Learning Method"
<https://arxiv.org/abs/1907.04931>`_ paper.
Given a graph in a :obj:`data` object, this class samples nodes and
constructs subgraphs that can be processed in a mini-batch fashion.
Normalization coefficients for each mini-batch are given via
:obj:`node_norm` and :obj:`edge_norm` data attributes.
.. note::
See :class:`torch_geometric.data.GraphSAINTNodeSampler`,
:class:`torch_geometric.data.GraphSAINTEdgeSampler` and
:class:`torch_geometric.data.GraphSAINTRandomWalkSampler` for
currently supported samplers.
For an example of using GraphSAINT sampling, see
`examples/graph_saint.py <https://github.com/rusty1s/pytorch_geometric/
blob/master/examples/graph_saint.py>`_.
Args:
data (torch_geometric.data.Data): The graph data object.
batch_size (int): The approximate number of samples per batch.
num_steps (int, optional): The number of iterations per epoch.
(default: :obj:`1`)
sample_coverage (int): How many samples per node should be used to
compute normalization statistics. (default: :obj:`0`)
save_dir (string, optional): If set, will save normalization
statistics to the :obj:`save_dir` directory for faster re-use.
(default: :obj:`None`)
log (bool, optional): If set to :obj:`False`, will not log any
pre-processing progress. (default: :obj:`True`)
**kwargs (optional): Additional arguments of
:class:`torch.utils.data.DataLoader`, such as :obj:`batch_size` or
:obj:`num_workers`.
"""
def __init__(self,
data,
batch_size: int,
num_steps: int = 1,
sample_coverage: int = 0,
save_dir: Optional[str] = None,
log: bool = True,
**kwargs):
assert data.edge_index is not None
assert 'node_norm' not in data
assert 'edge_norm' not in data
self.num_steps = num_steps
self.__batch_size__ = batch_size
self.sample_coverage = sample_coverage
self.log = log
self.N = N = data.num_nodes
self.E = data.num_edges
self.adj = SparseTensor(row=data.edge_index[0],
col=data.edge_index[1],
value=torch.arange(
self.E, device=data.edge_index.device),
sparse_sizes=(N, N))
self.data = copy.copy(data)
self.data.edge_index = None
super(GraphSAINTSampler, self).__init__(self,
batch_size=1,
collate_fn=self.__collate__,
**kwargs)
if self.sample_coverage > 0:
path = osp.join(save_dir or '', self.__filename__)
if save_dir is not None and osp.exists(path): # pragma: no cover
self.node_norm, self.edge_norm = torch.load(path)
else:
self.node_norm, self.edge_norm = self.__compute_norm__()
if save_dir is not None: # pragma: no cover
torch.save((self.node_norm, self.edge_norm), path)
@property
def __filename__(self):
return f'{self.__class__.__name__.lower()}_{self.sample_coverage}.pt'
def __len__(self):
return self.num_steps
def __sample_nodes__(self, batch_size):
raise NotImplementedError
def __getitem__(self, idx):
node_idx = self.__sample_nodes__(self.__batch_size__).unique()
adj, _ = self.adj.saint_subgraph(node_idx)
return node_idx, adj
def __collate__(self, data_list):
assert len(data_list) == 1
node_idx, adj = data_list[0]
data = self.data.__class__()
data.num_nodes = node_idx.size(0)
row, col, edge_idx = adj.coo()
data.edge_index = torch.stack([row, col], dim=0)
for key, item in self.data:
if isinstance(item, torch.Tensor) and item.size(0) == self.N:
data[key] = item[node_idx]
elif isinstance(item, torch.Tensor) and item.size(0) == self.E:
data[key] = item[edge_idx]
else:
data[key] = item
if self.sample_coverage > 0:
data.node_norm = self.node_norm[node_idx]
data.edge_norm = self.edge_norm[edge_idx]
return data
def __compute_norm__(self):
node_count = torch.zeros(self.N, dtype=torch.float)
edge_count = torch.zeros(self.E, dtype=torch.float)
loader = torch.utils.data.DataLoader(self,
batch_size=200,
collate_fn=lambda x: x,
num_workers=self.num_workers)
if self.log: # pragma: no cover
pbar = tqdm(total=self.N * self.sample_coverage)
pbar.set_description('Compute GraphSAINT normalization')
num_samples = total_sampled_nodes = 0
while total_sampled_nodes < self.N * self.sample_coverage:
for data in loader:
for node_idx, adj in data:
edge_idx = adj.storage.value()
node_count[node_idx] += 1
edge_count[edge_idx] += 1
total_sampled_nodes += node_idx.size(0)
if self.log: # pragma: no cover
pbar.update(node_idx.size(0))
num_samples += self.num_steps
if self.log: # pragma: no cover
pbar.close()
row, _, edge_idx = self.adj.coo()
t = torch.empty_like(edge_count).scatter_(0, edge_idx, node_count[row])
edge_norm = (t / edge_count).clamp_(0, 1e4)
edge_norm[torch.isnan(edge_norm)] = 0.1
node_count[node_count == 0] = 0.1
node_norm = num_samples / node_count / self.N
return node_norm, edge_norm
def sample_adj(self, adj: SparseTensor) -> SparseTensor:
row, col, _ = adj.coo()
deg = degree(row, num_nodes=adj.size(0))
prob = (self.max_sample * (1. / deg))[row]
mask = torch.rand_like(prob) < prob
return adj.masked_select_nnz(mask, layout='coo')
class MySAINTSampler(object):
r"""A new random-walk sampler for GraphSAINT that samples initial nodes
by iterating over node permutations. The benefit is that we can leverage
this sampler for subgraph-based inference.
Args:
data (torch_geometric.data.Data): The graph data object.
batch_size (int): The number of walks to sample per batch.
walk_length (int): The length of each random walk.
sample_coverage (int): How many samples per node should be used to
compute normalization statistics. (default: :obj:`50`)
save_dir (string, optional): If set, will save normalization
statistics to the :obj:`save_dir` directory for faster re-use.
(default: :obj:`None`)
log (bool, optional): If set to :obj:`False`, will not log any
progress. (default: :obj:`True`)
"""
def __init__(self, data, batch_size, sample_type='random_walk', walk_length=2, sample_coverage=50,
save_dir=None, log=True):
assert data.edge_index is not None
assert 'node_norm' not in data
assert 'edge_norm' not in data
assert sample_type in ('node', 'random_walk')
self.N = N = data.num_nodes
self.E = data.num_edges
self.adj = SparseTensor(row=data.edge_index[0], col=data.edge_index[1],
value=data.edge_attr, sparse_sizes=(N, N))
self.data = copy.copy(data)
self.data.edge_index = None
self.data.edge_attr = None
self.sample_type = sample_type
self.batch_size = batch_size
# self.num_steps = num_steps
self.walk_length = walk_length
self.sample_coverage = sample_coverage
self.log = log
path = osp.join(save_dir or '', self.__filename__)
if save_dir is not None and osp.exists(path): # pragma: no cover
self.node_norm, self.edge_norm = torch.load(path)
else:
self.node_norm, self.edge_norm = self.__compute_norm__()
if save_dir is not None: # pragma: no cover
torch.save((self.node_norm, self.edge_norm), path)
@property
def __filename__(self):
return f'{self.__class__.__name__.lower()}_{self.sample_coverage}.pt'
def __sample_nodes__(self):
"""Sampling initial nodes by iterating over the random permutation of nodes"""
tmp_map = torch.arange(self.N, dtype=torch.long)
all_n_id = torch.randperm(self.N, dtype=torch.long)
node_samples = []
for s_id in range(0, self.N, self.batch_size):
init_n_id = all_n_id[s_id:s_id+self.batch_size] # [batch_size]
if self.sample_type == 'random_walk':
n_id = self.adj.random_walk(init_n_id, self.walk_length) # [batch_size, walk_length+1]
n_id = n_id.flatten().unique() # [num_nodes_in_subgraph]
tmp_map[n_id] = torch.arange(n_id.size(0), dtype=torch.long)
res_n_id = tmp_map[init_n_id]
elif self.sample_type == 'node':
n_id = init_n_id
res_n_id = torch.arange(n_id.size(0), dtype=torch.long)
else:
raise ValueError('Unsupported value type {}'.format(self.sample_type))
node_samples.append((n_id, res_n_id))
return node_samples
def __sample__(self, num_epoches):
samples = []
for _ in range(num_epoches):
node_samples = self.__sample_nodes__()
for n_id, res_n_id in node_samples:
adj, e_id = self.adj.saint_subgraph(n_id)
samples.append((n_id, e_id, adj, res_n_id))
return samples
def __compute_norm__(self):
node_count = torch.zeros(self.N, dtype=torch.float)
edge_count = torch.zeros(self.E, dtype=torch.float)
if self.log:
pbar = tqdm(total=self.sample_coverage)
pbar.set_description('GraphSAINT Normalization')
num_samples = len(self) * self.sample_coverage
for _ in range(self.sample_coverage):
samples = self.__sample__(1)
for n_id, e_id, _, _ in samples:
node_count[n_id] += 1
edge_count[e_id] += 1
if self.log:
pbar.update(1)
row, col, _ = self.adj.coo()
edge_norm = (node_count[col] / edge_count).clamp_(0, 1e4)
edge_norm[torch.isnan(edge_norm)] = 0.1
node_count[node_count == 0] = 0.1
node_norm = num_samples / (node_count * self.N)
return node_norm, edge_norm
def __get_data_from_sample__(self, sample):
n_id, e_id, adj, res_n_id = sample
data = self.data.__class__()
data.num_nodes = n_id.size(0)
row, col, value = adj.coo()
data.edge_index = torch.stack([row, col], dim=0)
data.edge_attr = value
for key, item in self.data:
if item.size(0) == self.N:
data[key] = item[n_id]
elif item.size(0) == self.E:
data[key] = item[e_id]
else:
data[key] = item
data.node_norm = self.node_norm[n_id]
data.edge_norm = self.edge_norm[e_id]
data.n_id = n_id
data.res_n_id = res_n_id
return data
def __len__(self):
return (self.N + self.batch_size-1) // self.batch_size
def __iter__(self):
for sample in self.__sample__(1):
data = self.__get_data_from_sample__(sample)
yield data
class GraphSAINTSampler(torch.utils.data.DataLoader):
def __init__(self, data, batch_size: int, num_steps: int = 1,
sample_coverage: int = 0, save_dir: Optional[str] = None,
log: bool = True, **kwargs):
assert data.edge_index is not None
assert 'node_norm' not in data
assert 'edge_norm' not in data
self.num_steps = num_steps
self.__batch_size__ = batch_size
self.sample_coverage = sample_coverage
self.log = log
self.N = N = data.num_nodes
self.E = data.num_edges
self.adj = SparseTensor(
row=data.edge_index[0], col=data.edge_index[1],
value=torch.arange(self.E, device=data.edge_index.device),
sparse_sizes=(N, N))
self.data = copy.copy(data)
self.data.edge_index = None
super(GraphSAINTSampler,
self).__init__(self, batch_size=1, collate_fn=self.__collate__,
**kwargs)
if self.sample_coverage > 0:
path = osp.join(save_dir or '', self.__filename__)
if save_dir is not None and osp.exists(path): # pragma: no cover
self.node_norm, self.edge_norm = torch.load(path)
else:
self.node_norm, self.edge_norm = self.__compute_norm__()
if save_dir is not None: # pragma: no cover
torch.save((self.node_norm, self.edge_norm), path)
@property
def __filename__(self):
return f'{self.__class__.__name__.lower()}_{self.sample_coverage}.pt'
def __len__(self):
return self.num_steps
def __sample_nodes__(self, batch_size):
raise NotImplementedError
def __getitem__(self, idx):
node_idx = self.__sample_nodes__(self.__batch_size__).unique()
adj, _ = self.adj.saint_subgraph(node_idx)
return node_idx, adj
def __collate__(self, data_list):
assert len(data_list) == 1
node_idx, adj = data_list[0]
data = self.data.__class__()
data.num_nodes = node_idx.size(0)
row, col, edge_idx = adj.coo()
data.edge_index = torch.stack([row, col], dim=0)
for key, item in self.data:
if isinstance(item, torch.Tensor) and item.size(0) == self.N:
data[key] = item[node_idx]
elif isinstance(item, torch.Tensor) and item.size(0) == self.E:
data[key] = item[edge_idx]
else:
data[key] = item
if self.sample_coverage > 0:
data.node_norm = self.node_norm[node_idx]
data.edge_norm = self.edge_norm[edge_idx]
return data
def __compute_norm__(self):
node_count = torch.zeros(self.N, dtype=torch.float)
edge_count = torch.zeros(self.E, dtype=torch.float)
loader = torch.utils.data.DataLoader(self, batch_size=200,
collate_fn=lambda x: x,
num_workers=self.num_workers)
if self.log:
pbar = tqdm(total=self.N * self.sample_coverage)
pbar.set_description('Compute GraphSAINT normalization')
num_samples = total_sampled_nodes = 0
while total_sampled_nodes < self.N * self.sample_coverage:
for data in loader:
for node_idx, adj in data:
edge_idx = adj.storage.value()
node_count[node_idx] += 1
edge_count[edge_idx] += 1
total_sampled_nodes += node_idx.size(0)
if self.log:
pbar.update(node_idx.size(0))
num_samples += 200
if self.log:
pbar.close()
row, _, edge_idx = self.adj.coo()
t = torch.empty_like(edge_count).scatter_(0, edge_idx, node_count[row])
edge_norm = (t / edge_count).clamp_(0, 1e4)
edge_norm[torch.isnan(edge_norm)] = 0.1
node_count[node_count == 0] = 0.1
node_norm = num_samples / node_count / self.N
return node_norm, edge_norm
