add ogbg ppi

ogb_examples/graphproppred/README.md

+# Graph Property Prediction for Open Graph Benchmark (OGB)
+
+[The Open Graph Benchmark (OGB)](https://ogb.stanford.edu/) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Here we complete the Graph Property Prediction task based on PGL.
+
+### Requirements
+
+- paddlpaddle 1.7.1
+- pgl 1.0.1
+- ogb
+
+NOTE: To install ogb that is fited for this project, run below command to install ogb
+```
+git clone https://github.com/snap-stanford/ogb.git
+git checkout 482c40bc9f31fe25f9df5aa11c8fb657bd2b1621
+python setup.py install
+```
+
+### How to run
+For example, use GPU to train model on ogbg-molhiv dataset.
+```
+python main_pgl.py --use_cuda --dataset ogbg-molhiv
+```

ogb_examples/graphproppred/main_pgl.py

@@ -14,7 +14,11 @@
 """test ogb
 """
 import argparse
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+# SSL
 
+import torch
 import pgl
 import numpy as np
 import paddle.fluid as fluid
@@ -46,6 +50,9 @@ def train(exe, batch_size, graph_wrapper, train_program, splitted_idx, dataset,
         loss, pred = exe.run(train_program,
                              feed=feed_dict,
                              fetch_list=[fetch_loss, fetch_pred])
+
+        if batch_no % 100 == 0:
+            print("loss: %s" % loss[0])
         pred_output[batch_index] = pred
         batch_no += 1
     print("train", evaluator.eval({"y_true": labels, "y_pred": pred_output}))
@@ -110,6 +117,7 @@ def main():
     """
     # Training settings
     parser = argparse.ArgumentParser(description='Graph Dataset')
+    parser.add_argument('--use_cuda', action='store_true')
     parser.add_argument(
         '--epochs',
         type=int,
@@ -118,11 +126,11 @@ def main():
     parser.add_argument(
         '--dataset',
         type=str,
-        default="ogbg-mol-tox21",
+        default="ogbg-molhiv",
         help='dataset name (default: proteinfunc)')
     args = parser.parse_args()
 
-    place = fluid.CPUPlace()  # Dataset too big to use GPU
+    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
 
     ### automatic dataloading and splitting
     dataset = PglGraphPropPredDataset(name=args.dataset)

ogb_examples/graphproppred/ogbg_ppi.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""test ogb
+"""
+import argparse
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+# SSL
+
+import torch
+import pgl
+import numpy as np
+import paddle.fluid as fluid
+from pgl.contrib.ogb.graphproppred.dataset_pgl import PglGraphPropPredDataset
+from pgl.utils import paddle_helper
+from ogb.graphproppred import Evaluator
+from pgl.contrib.ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
+
+
+def train(exe, batch_size, graph_wrapper, train_program, splitted_idx, dataset,
+          evaluator, fetch_loss, fetch_pred):
+    """Train"""
+    graphs, labels = dataset[splitted_idx["train"]]
+    perm = np.arange(0, len(graphs))
+    np.random.shuffle(perm)
+    start_batch = 0
+    batch_no = 0
+    pred_output = np.zeros_like(labels, dtype="float32")
+    while start_batch < len(perm):
+        batch_index = perm[start_batch:start_batch + batch_size]
+        start_batch += batch_size
+        batch_graph = pgl.graph.MultiGraph(graphs[batch_index])
+        batch_label = labels[batch_index]
+        batch_valid = (batch_label == batch_label).astype("float32")
+        batch_label = np.nan_to_num(batch_label).astype("float32")
+        feed_dict = graph_wrapper.to_feed(batch_graph)
+        feed_dict["label"] = batch_label
+        feed_dict["weight"] = batch_valid
+        loss, pred = exe.run(train_program,
+                             feed=feed_dict,
+                             fetch_list=[fetch_loss, fetch_pred])
+
+        if batch_no % 100 == 0:
+            print("loss: %s" % loss[0])
+        pred_output[batch_index] = pred
+        batch_no += 1
+    print("train", evaluator.eval({"y_true": labels, "y_pred": pred_output}))
+
+
+def evaluate(exe, batch_size, graph_wrapper, val_program, splitted_idx,
+             dataset, mode, evaluator, fetch_pred):
+    """Eval"""
+    graphs, labels = dataset[splitted_idx[mode]]
+    perm = np.arange(0, len(graphs))
+    start_batch = 0
+    batch_no = 0
+    pred_output = np.zeros_like(labels, dtype="float32")
+    while start_batch < len(perm):
+        batch_index = perm[start_batch:start_batch + batch_size]
+        start_batch += batch_size
+        batch_graph = pgl.graph.MultiGraph(graphs[batch_index])
+        feed_dict = graph_wrapper.to_feed(batch_graph)
+        pred = exe.run(val_program, feed=feed_dict, fetch_list=[fetch_pred])
+        pred_output[batch_index] = pred[0]
+        batch_no += 1
+    print(mode, evaluator.eval({"y_true": labels, "y_pred": pred_output}))
+
+
+def send_func(src_feat, dst_feat, edge_feat):
+    """Send"""
+    return src_feat["h"] + edge_feat["h"]
+
+
+class GNNModel(object):
+    """GNNModel"""
+
+    def __init__(self, name, emb_dim, num_task, num_layers):
+        self.num_task = num_task
+        self.emb_dim = emb_dim
+        self.num_layers = num_layers
+        self.name = name
+        self.atom_encoder = AtomEncoder(name=name, emb_dim=emb_dim)
+        self.bond_encoder = BondEncoder(name=name, emb_dim=emb_dim)
+
+    def edges_encoder(self, inputs, name):
+        outputs = fluid.layers.fc(inputs,
+                                  size=self.emb_dim,
+                                  param_attr=fluid.ParamAttr(name=name))
+        return outputs
+
+    def forward(self, graph):
+        """foward"""
+        #  h_node = self.atom_encoder(graph.node_feat['feat'])
+        h_node = fluid.layers.embedding(
+            graph.node_feat['feat'], size=[1, self.emb_dim])
+
+        #  h_edge = self.bond_encoder(graph.edge_feat['feat'])
+        h_edge = self.edges_encoder(
+            graph.edge_feat['feat'], name="edges_encoder")
+        for layer in range(self.num_layers):
+            msg = graph.send(
+                send_func,
+                nfeat_list=[("h", h_node)],
+                efeat_list=[("h", h_edge)])
+            h_node = graph.recv(msg, 'sum') + h_node
+            h_node = fluid.layers.fc(h_node,
+                                     size=self.emb_dim,
+                                     name=self.name + '_%s' % layer,
+                                     act="relu")
+        graph_nodes = pgl.layers.graph_pooling(graph, h_node, "average")
+        graph_pred = fluid.layers.fc(graph_nodes, self.num_task, name="final")
+        return graph_pred
+
+
+def main():
+    """main
+    """
+    # Training settings
+    parser = argparse.ArgumentParser(description='Graph Dataset')
+    parser.add_argument('--use_cuda', action='store_true')
+    parser.add_argument(
+        '--epochs',
+        type=int,
+        default=100,
+        help='number of epochs to train (default: 100)')
+    parser.add_argument(
+        '--dataset',
+        type=str,
+        default="ogbg-molhiv",
+        help='dataset name (default: proteinfunc)')
+    args = parser.parse_args()
+
+    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
+
+    ### automatic dataloading and splitting
+    dataset = PglGraphPropPredDataset(name=args.dataset)
+
+    for i in range(len(dataset)):
+        g, l = dataset[i]
+        if not g.node_feat:
+            g.node_feat['feat'] = np.array(
+                [0 for _ in range(g.num_nodes)]).reshape(-1, 1)
+
+    splitted_idx = dataset.get_idx_split()
+
+    ### automatic evaluator. takes dataset name as input
+    evaluator = Evaluator(args.dataset)
+
+    graph_data, label = dataset[:2]
+    batch_graph = pgl.graph.MultiGraph(graph_data)
+    graph_data = batch_graph
+
+    train_program = fluid.Program()
+    startup_program = fluid.Program()
+    test_program = fluid.Program()
+    # degree normalize
+    graph_data.edge_feat["feat"] = graph_data.edge_feat["feat"].astype(
+        "float32")
+    graph_data.node_feat["feat"] = graph_data.node_feat["feat"].astype("int64")
+
+    model = GNNModel(
+        name="gnn", num_task=dataset.num_tasks, emb_dim=64, num_layers=2)
+
+    with fluid.program_guard(train_program, startup_program):
+        gw = pgl.graph_wrapper.GraphWrapper(
+            "graph",
+            place=place,
+            node_feat=graph_data.node_feat_info(),
+            edge_feat=graph_data.edge_feat_info())
+        pred = model.forward(gw)
+        sigmoid_pred = fluid.layers.sigmoid(pred)
+
+    val_program = train_program.clone(for_test=True)
+
+    initializer = []
+    with fluid.program_guard(train_program, startup_program):
+        train_label = fluid.layers.data(
+            name="label", dtype="float32", shape=[None, dataset.num_tasks])
+        train_weight = fluid.layers.data(
+            name="weight", dtype="float32", shape=[None, dataset.num_tasks])
+        train_loss_t = fluid.layers.sigmoid_cross_entropy_with_logits(
+            x=pred, label=train_label) * train_weight
+        train_loss_t = fluid.layers.reduce_sum(train_loss_t)
+
+        adam = fluid.optimizer.Adam(
+            learning_rate=1e-2,
+            regularization=fluid.regularizer.L2DecayRegularizer(
+                regularization_coeff=0.0005))
+        adam.minimize(train_loss_t)
+
+    exe = fluid.Executor(place)
+    exe.run(startup_program)
+
+    for epoch in range(1, args.epochs + 1):
+        print("Epoch", epoch)
+        train(exe, 128, gw, train_program, splitted_idx, dataset, evaluator,
+              train_loss_t, sigmoid_pred)
+        evaluate(exe, 128, gw, val_program, splitted_idx, dataset, "valid",
+                 evaluator, sigmoid_pred)
+        evaluate(exe, 128, gw, val_program, splitted_idx, dataset, "test",
+                 evaluator, sigmoid_pred)
+
+
+if __name__ == "__main__":
+    main()