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Add arxiv and reddit dataset for benchmark #459

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88 changes: 53 additions & 35 deletions examples/citation_benchmark/multi_gpu_train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,44 +30,67 @@ def normalize(feat):
return feat / np.maximum(np.sum(feat, -1, keepdims=True), 1)


def load(name, normalized_feature=True):
def load(name):
if name == 'cora':
dataset = pgl.dataset.CoraDataset()
elif name == "pubmed":
dataset = pgl.dataset.CitationDataset("pubmed", symmetry_edges=True)
elif name == "citeseer":
dataset = pgl.dataset.CitationDataset("citeseer", symmetry_edges=True)
elif name == "ogbn_arxiv":
dataset = pgl.dataset.OgbnArxivDataset()
elif name == "reddit":
dataset = pgl.dataset.RedditDataset()
else:
raise ValueError(name + " dataset doesn't exists")

indegree = dataset.graph.indegree()
dataset.graph.node_feat["words"] = normalize(dataset.graph.node_feat[
"words"])

if name == "ogbn_arxiv":
dataset.y = np.reshape(dataset.y, [-1])
if name == "cora" or name == "pubmed" or name == "citeseer":
indegree = dataset.graph.indegree()
dataset.feature = normalize(dataset.graph.node_feat["words"])
dataset.graph = pgl.Graph(
num_nodes=dataset.graph.num_nodes, edges=dataset.graph.edges)
if paddle.distributed.get_world_size() > 1:
dataset.graph = pgl.DistGPUGraph(dataset.graph)
dataset.graph.tensor()
train_index = dataset.train_index
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.y[train_index], -1))
dataset.train_index = paddle.to_tensor(np.expand_dims(train_index, -1))

val_index = dataset.val_index
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.y[val_index], -1))
dataset.val_index = paddle.to_tensor(np.expand_dims(val_index, -1))

test_index = dataset.test_index
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.y[test_index], -1))
dataset.test_index = paddle.to_tensor(np.expand_dims(test_index, -1))


if name != "reddit":
dataset.feature = paddle.to_tensor(dataset.feature, dtype="float32")
train_index = dataset.train_index
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.y[train_index], -1))
dataset.train_index = paddle.to_tensor(np.expand_dims(train_index, -1))

val_index = dataset.val_index
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.y[val_index], -1))
dataset.val_index = paddle.to_tensor(np.expand_dims(val_index, -1))

test_index = dataset.test_index
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.y[test_index], -1))
dataset.test_index = paddle.to_tensor(np.expand_dims(test_index, -1))
else:
dataset.feature = paddle.to_tensor(dataset.feature, dtype="float32")
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.train_label, -1))
dataset.train_index = paddle.to_tensor(
np.expand_dims(dataset.train_index, -1))
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.val_label, -1))
dataset.val_index = paddle.to_tensor(
np.expand_dims(dataset.val_index, -1))
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.test_label, -1))
dataset.test_index = paddle.to_tensor(
np.expand_dims(dataset.test_index, -1))
return dataset


def train(node_index, node_label, gnn_model, graph, criterion, optim):
def train(node_index, node_label, gnn_model, graph, feature, criterion, optim):
gnn_model.train()
pred = gnn_model(graph, graph.node_feat["words"])
pred = gnn_model(graph, feature)
pred = paddle.gather(pred, node_index)
loss = criterion(pred, node_label)
loss.backward()
Expand All @@ -78,9 +101,9 @@ def train(node_index, node_label, gnn_model, graph, criterion, optim):


@paddle.no_grad()
def eval(node_index, node_label, gnn_model, graph, criterion):
def eval(node_index, node_label, gnn_model, graph, feature, criterion):
gnn_model.eval()
pred = gnn_model(graph, graph.node_feat["words"])
pred = gnn_model(graph, feature)
pred = paddle.gather(pred, node_index)
loss = criterion(pred, node_label)
acc = paddle.metric.accuracy(input=pred, label=node_label, k=1)
Expand All @@ -96,9 +119,9 @@ def main(args, config, run):
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()

dataset = load(args.dataset, args.feature_pre_normalize)
dataset = load(args.dataset)
graph = dataset.graph

feature = dataset.feature
train_index = dataset.train_index
train_label = dataset.train_label

Expand All @@ -121,7 +144,7 @@ def main(args, config, run):
cal_test_loss = []

gnn_model = GraphModel(
input_size=graph.node_feat["words"].shape[1],
input_size=feature.shape[1],
num_class=dataset.num_classes,
**config)

Expand All @@ -135,14 +158,14 @@ def main(args, config, run):

for epoch in tqdm.tqdm(range(args.epoch)):
train_loss, train_acc = train(train_index, train_label, gnn_model,
graph, criterion, optim)
graph, feature, criterion, optim)
val_loss, val_acc = eval(val_index, val_label, gnn_model, graph,
criterion)
feature, criterion)
cal_val_acc.append(val_acc.numpy())
cal_val_loss.append(val_loss.numpy())

test_loss, test_acc = eval(test_index, test_label, gnn_model, graph,
criterion)
feature, criterion)
cal_test_acc.append(test_acc.numpy())
cal_test_loss.append(test_loss.numpy())

Expand All @@ -158,11 +181,6 @@ def main(args, config, run):
parser.add_argument("--conf", type=str, help="config file for models")
parser.add_argument("--epoch", type=int, default=200, help="Epoch")
parser.add_argument("--runs", type=int, default=10, help="runs")
parser.add_argument(
"--feature_pre_normalize",
type=bool,
default=True,
help="pre_normalize feature")
args = parser.parse_args()
config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
log.info(args)
Expand Down
85 changes: 52 additions & 33 deletions examples/citation_benchmark/train.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,42 +29,65 @@ def normalize(feat):
return feat / np.maximum(np.sum(feat, -1, keepdims=True), 1)


def load(name, normalized_feature=True):
def load(name):
if name == 'cora':
dataset = pgl.dataset.CoraDataset()
elif name == "pubmed":
dataset = pgl.dataset.CitationDataset("pubmed", symmetry_edges=True)
elif name == "citeseer":
dataset = pgl.dataset.CitationDataset("citeseer", symmetry_edges=True)
elif name == "ogbn_arxiv":
dataset = pgl.dataset.OgbnArxivDataset()
elif name == "reddit":
dataset = pgl.dataset.RedditDataset()
else:
raise ValueError(name + " dataset doesn't exists")

indegree = dataset.graph.indegree()
dataset.graph.node_feat["words"] = normalize(dataset.graph.node_feat[
"words"])

if name == "ogbn_arxiv":
dataset.y = np.reshape(dataset.y, [-1])
if name == "cora" or name == "pubmed" or name == "citeseer":
indegree = dataset.graph.indegree()
dataset.feature = normalize(dataset.graph.node_feat["words"])
dataset.graph = pgl.Graph(
num_nodes=dataset.graph.num_nodes, edges=dataset.graph.edges)
dataset.graph.tensor()
train_index = dataset.train_index
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.y[train_index], -1))
dataset.train_index = paddle.to_tensor(np.expand_dims(train_index, -1))

val_index = dataset.val_index
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.y[val_index], -1))
dataset.val_index = paddle.to_tensor(np.expand_dims(val_index, -1))

test_index = dataset.test_index
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.y[test_index], -1))
dataset.test_index = paddle.to_tensor(np.expand_dims(test_index, -1))

if name != "reddit":
dataset.feature = paddle.to_tensor(dataset.feature, dtype="float32")
train_index = dataset.train_index
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.y[train_index], -1))
dataset.train_index = paddle.to_tensor(np.expand_dims(train_index, -1))

val_index = dataset.val_index
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.y[val_index], -1))
dataset.val_index = paddle.to_tensor(np.expand_dims(val_index, -1))

test_index = dataset.test_index
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.y[test_index], -1))
dataset.test_index = paddle.to_tensor(np.expand_dims(test_index, -1))
else:
dataset.feature = paddle.to_tensor(dataset.feature, dtype="float32")
dataset.train_label = paddle.to_tensor(
np.expand_dims(dataset.train_label, -1))
dataset.train_index = paddle.to_tensor(
np.expand_dims(dataset.train_index, -1))
dataset.val_label = paddle.to_tensor(
np.expand_dims(dataset.val_label, -1))
dataset.val_index = paddle.to_tensor(
np.expand_dims(dataset.val_index, -1))
dataset.test_label = paddle.to_tensor(
np.expand_dims(dataset.test_label, -1))
dataset.test_index = paddle.to_tensor(
np.expand_dims(dataset.test_index, -1))
return dataset


def train(node_index, node_label, gnn_model, graph, criterion, optim):
def train(node_index, node_label, gnn_model, graph, feature, criterion, optim):
gnn_model.train()
pred = gnn_model(graph, graph.node_feat["words"])
pred = gnn_model(graph, feature)
pred = paddle.gather(pred, node_index)
loss = criterion(pred, node_label)
loss.backward()
Expand All @@ -75,9 +98,9 @@ def train(node_index, node_label, gnn_model, graph, criterion, optim):


@paddle.no_grad()
def eval(node_index, node_label, gnn_model, graph, criterion):
def eval(node_index, node_label, gnn_model, graph, feature, criterion):
gnn_model.eval()
pred = gnn_model(graph, graph.node_feat["words"])
pred = gnn_model(graph, feature)
pred = paddle.gather(pred, node_index)
loss = criterion(pred, node_label)
acc = paddle.metric.accuracy(input=pred, label=node_label, k=1)
Expand All @@ -90,9 +113,10 @@ def set_seed(seed):


def main(args, config):
dataset = load(args.dataset, args.feature_pre_normalize)
dataset = load(args.dataset)

graph = dataset.graph
feature = dataset.feature
train_index = dataset.train_index
train_label = dataset.train_label

Expand All @@ -115,7 +139,7 @@ def main(args, config):
cal_test_loss = []

gnn_model = GraphModel(
input_size=graph.node_feat["words"].shape[1],
input_size=feature.shape[1],
num_class=dataset.num_classes,
**config)

Expand All @@ -126,14 +150,14 @@ def main(args, config):

for epoch in tqdm.tqdm(range(args.epoch)):
train_loss, train_acc = train(train_index, train_label, gnn_model,
graph, criterion, optim)
graph, feature, criterion, optim)
val_loss, val_acc = eval(val_index, val_label, gnn_model, graph,
criterion)
feature, criterion)
cal_val_acc.append(val_acc.numpy())
cal_val_loss.append(val_loss.numpy())

test_loss, test_acc = eval(test_index, test_label, gnn_model,
graph, criterion)
graph, feature, criterion)
cal_test_acc.append(test_acc.numpy())
cal_test_loss.append(test_loss.numpy())

Expand All @@ -154,11 +178,6 @@ def main(args, config):
parser.add_argument("--conf", type=str, help="config file for models")
parser.add_argument("--epoch", type=int, default=200, help="Epoch")
parser.add_argument("--runs", type=int, default=10, help="runs")
parser.add_argument(
"--feature_pre_normalize",
type=bool,
default=True,
help="pre_normalize feature")
args = parser.parse_args()
config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
log.info(args)
Expand Down