add FRANKENSTEIN

examples/SAGPool/README.md

 # Self-Attention Graph Pooling
 
-SAGPool is a graph pooling method based on self-attention. Self-attention uses graph convolution, which allows the pooling method to consider both node features and graph topology.  Based on PGL, we implement the SAGPool algorithm and train the model on four datasets.
+SAGPool is a graph pooling method based on self-attention. Self-attention uses graph convolution, which allows the pooling method to consider both node features and graph topology. Based on PGL, we implement the SAGPool algorithm and train the model on five datasets.
 
 ## Datasets
 
-There are four datasets, including D&D, PROTEINS, NCI1, NCI109. You can download the datasets from [here](https://bj.bcebos.com/paddle-pgl/SAGPool/data.zip), and unzip it directly. The pkl format datasets should be in directory ./data.
+There are five datasets, including D&D, PROTEINS, NCI1, NCI109 and FRANKENSTEIN. You can download the datasets from [here](https://bj.bcebos.com/paddle-pgl/SAGPool/data.zip), and unzip it directly. The pkl format datasets should be in directory ./data.
 
 ## Dependencies
 
@@ -21,18 +21,20 @@ python main.py --dataset_name PROTEINS --learning_rate 0.001 --hidden_size 32 --
 python main.py --dataset_name NCI1 --learning_rate 0.001 --weight_decay 0.00001
 
 python main.py --dataset_name NCI109 --learning_rate 0.0005 --hidden_size 64 --weight_decay 0.0001 --patience 200 
+
+python main.py --dataset_name FRANKENSTEIN --learning_rate 0.001 --weight_decay 0.0001
 ```
 
 ## Hyperparameters
 
 - seed: random seed
 - batch\_size: the number of batch size
-- learning\_rate: the number of learning rate
+- learning\_rate: learning rate of optimizer
 - weight\_decay: the weight decay for L2 regularization
 - hidden\_size: the hidden size of gcn
 - pooling\_ratio: the pooling ratio of SAGPool
 - dropout\_ratio: the number of dropout ratio
-- dataset\_name: the name of datasets, including DD, PROTEINS, NCI1, NCI109
+- dataset\_name: the name of datasets, including DD, PROTEINS, NCI1, NCI109, FRANKENSTEIN
 - epochs: maximum number of epochs
 - patience: patience for early stopping
 - use\_cuda: whether to use cuda
@@ -48,3 +50,4 @@ We evaluate the implemented method for 20 random seeds using 10-fold cross valid
 | PROTEINS     | 72.7858       | 0.6617             |
 | NCI1         | 75.781        | 1.2125             |
 | NCI109       | 74.3156       | 1.3                |
+| FRANKENSTEIN | 60.7826       | 0.629              |

examples/SAGPool/base_dataset.py

@@ -57,6 +57,7 @@ class Dataset(BaseDataset):
         
         with open('data/%s.pkl' % args.dataset_name, 'rb') as f:
             graphs_info_list = pickle.load(f)
+
         self.pgl_graph_list = []
         self.graph_label_list = []
         for i in range(len(graphs_info_list) - 1):
@@ -64,8 +65,9 @@ class Dataset(BaseDataset):
             edges_l, edges_r = graph["edge_src"], graph["edge_dst"]
             
             # add self-loops
-            node_nums = graph["num_nodes"]
-            x = np.arange(0, node_nums)
+            if self.args.dataset_name != "FRANKENSTEIN":
+                num_nodes = graph["num_nodes"]
+                x = np.arange(0, num_nodes)
                 edges_l = np.append(edges_l, x)
                 edges_r = np.append(edges_r, x)
             

examples/SAGPool/layers.py

@@ -20,6 +20,7 @@ import pgl
 from pgl.graph_wrapper import GraphWrapper
 from pgl.utils.logger import log
 from conv import norm_gcn
+from pgl.layers.conv import gcn
 
 def topk_pool(gw, score, graph_id, ratio):
     """Implementation of topk pooling, where k means pooling ratio.
@@ -53,6 +54,7 @@ def topk_pool(gw, score, graph_id, ratio):
     index = L.arange(0, gw.num_nodes, dtype="int64")
     temp = L.gather(graph_lod, graph_id, overwrite=False)
     index = (index - temp) + (graph_id * max_num_nodes)
+    index.stop_gradient = True
     
     # padding
     dense_score = L.fill_constant(shape=[num_graph * max_num_nodes],
@@ -86,7 +88,7 @@ def topk_pool(gw, score, graph_id, ratio):
     return perm, ratio_length 
 
 
-def sag_pool(gw, feature, ratio, graph_id, name, activation=L.tanh):
+def sag_pool(gw, feature, ratio, graph_id, dataset, name, activation=L.tanh):
     """Implementation of self-attention graph pooling (SAGPool)
 
     This is an implementation of the paper SELF-ATTENTION GRAPH POOLING
@@ -101,7 +103,9 @@ def sag_pool(gw, feature, ratio, graph_id, name, activation=L.tanh):
 
         graph_id: The graphs that the nodes belong to. 
 
-        gcn: To use the official gcn or norm gcn.
+        dataset: To differentiate FRANKENSTEIN dataset and other datasets.
+
+        name: The name of SAGPool layer.
         
         activation: The activation function.
 
@@ -112,7 +116,12 @@ def sag_pool(gw, feature, ratio, graph_id, name, activation=L.tanh):
         ratio_length: The selected node numbers of each graph.
 
     """
-    score = norm_gcn(gw=gw,    
+    if dataset == "FRANKENSTEIN":
+        gcn_ = gcn
+    else:
+        gcn_ = norm_gcn
+
+    score = gcn_(gw=gw,    
                 feature=feature, 
                 hidden_size=1,
                 activation=None,

examples/SAGPool/main.py

@@ -15,8 +15,6 @@
 import sys
 import os
 import argparse
-import torch
-from torch_geometric.datasets import TUDataset
 import pgl
 from pgl.utils.logger import log
 import paddle

examples/SAGPool/model.py

@@ -22,6 +22,7 @@ import pgl
 from pgl.graph import Graph, MultiGraph
 from pgl.graph_wrapper import GraphWrapper
 from pgl.utils.logger import log
+from pgl.layers.conv import gcn
 from layers import sag_pool
 from conv import norm_gcn
 
@@ -73,26 +74,31 @@ class GlobalModel(object):
           dtype="int32",
           append_batch_size=False)
 
+        if self.args.dataset_name == "FRANKENSTEIN":
+            self.gcn = gcn
+        else:
+            self.gcn = norm_gcn
+        
         self.build_model()
 
     def build_model(self):
         node_features = self.graph_wrapper.node_feat["feat"]
 
-        output = gcn(gw=self.graph_wrapper, 
+        output = self.gcn(gw=self.graph_wrapper, 
                      feature=node_features, 
                      hidden_size=self.hidden_size,
                      activation="relu", 
                      norm=self.graph_wrapper.node_feat["norm"],
                      name="gcn_layer_1")
         output1 = output
-        output = gcn(gw=self.graph_wrapper, 
+        output = self.gcn(gw=self.graph_wrapper, 
                      feature=output, 
                      hidden_size=self.hidden_size,
                      activation="relu", 
                      norm=self.graph_wrapper.node_feat["norm"],
                      name="gcn_layer_2")
         output2 = output
-        output = gcn(gw=self.graph_wrapper, 
+        output = self.gcn(gw=self.graph_wrapper, 
                      feature=output, 
                      hidden_size=self.hidden_size,
                      activation="relu", 
@@ -105,6 +111,7 @@ class GlobalModel(object):
                           feature=output, 
                           ratio=self.pooling_ratio,
                           graph_id=self.graph_id,
+                          dataset=self.args.dataset_name,
                           name="sag_pool_1")
         output = L.lod_reset(output, self.graph_wrapper.graph_lod)
         cat1 = L.sequence_pool(output, "sum")