update

examples/GaAN/conv.py

@@ -264,8 +264,8 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
     """Implementation of GaAN"""
 
     def send_func(src_feat, dst_feat, edge_feat):
-        # 计算每条边上的注意力分数
-        # E * (M * D1), 每个 dst 点都查询它的全部邻边的 src 点
+        # attention score of each edge
+        # E * (M * D1)
         feat_query, feat_key = dst_feat['feat_query'], src_feat['feat_key']
         # E * M * D1
         old = feat_query
@@ -281,16 +281,11 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
                 'feat_gate': src_feat['feat_gate']}
 
     def recv_func(message):
-        # 每条边的终点的特征
         dst_feat = message['dst_node_feat']
-        # 每条边的出发点的特征
         src_feat = message['src_node_feat']
-        # 每个中心点自己的特征
         x = fluid.layers.sequence_pool(dst_feat, 'average')
-        # 每个中心点的邻居的特征的平均值
         z = fluid.layers.sequence_pool(src_feat, 'average')
 
-        # 计算 gate
         feat_gate = message['feat_gate']
         g_max = fluid.layers.sequence_pool(feat_gate, 'max')
         g = fluid.layers.concat([x, g_max, z], axis=1)
@@ -318,10 +313,6 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
 
         return output
 
-    # feature N * D
-
-    # 计算每个点自己需要发送出去的内容
-    # 投影后的特征向量
     # N * (D1 * M)
     feat_key = fluid.layers.fc(feature, hidden_size_a * heads, bias_attr=False,
                      param_attr=fluid.ParamAttr(name=name + '_project_key'))
@@ -335,8 +326,7 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
     feat_gate = fluid.layers.fc(feature, hidden_size_m, bias_attr=False, 
                                 param_attr=fluid.ParamAttr(name=name + '_project_gate'))
 
-    # send 阶段
-
+    # send stage
     message = gw.send(
         send_func,
         nfeat_list=[('node_feat', feature), ('feat_key', feat_key), ('feat_value', feat_value),
@@ -344,7 +334,7 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
         efeat_list=None,
     )
 
-    # 聚合邻居特征
+    # recv stage
     output = gw.recv(message, recv_func)
     output = fluid.layers.fc(output, hidden_size_o, bias_attr=False,
                             param_attr=fluid.ParamAttr(name=name + '_project_output'))

examples/GaAN/model.py

+# Copyright (c) 2019 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.
 from paddle import fluid
 from pgl.utils import paddle_helper
 

examples/GaAN/preprocess.py

-"""
-将 ogb_proteins 的数据处理为 PGL 的 graph 数据，并返回 graph, label, train/valid/test 等信息
-"""
+# Copyright (c) 2019 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.
 import ssl
 ssl._create_default_https_context = ssl._create_unverified_context
 from ogb.nodeproppred import NodePropPredDataset, Evaluator
@@ -17,7 +27,7 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
             d_name: name of dataset
             mini_data: if mini_data==True, only use a small dataset (for test)
     """
-    # 导入 ogb 数据
+    # import ogb data
     dataset = NodePropPredDataset(name = d_name)
     num_tasks = dataset.num_tasks # obtaining the number of prediction tasks in a dataset
 
@@ -25,10 +35,10 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
     train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
     graph, label = dataset[0]
     
-    # 调整维度，符合 PGL 的 Graph 要求
+    # reshape
     graph["edge_index"] = graph["edge_index"].T
     
-    # 使用小规模数据，500个节点
+    # mini dataset
     if mini_data: 
         graph['num_nodes'] = 500
         mask = (graph['edge_index'][:, 0] < 500)*(graph['edge_index'][:, 1] < 500)
@@ -39,19 +49,9 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
         valid_idx = np.arange(400,450)
         test_idx = np.arange(450,500)
     
-    # 输出 dataset 的信息    
-    print(graph.keys())
-    print("节点个数 ", graph["num_nodes"])
-    print("节点最小编号", graph['edge_index'][0].min())
-    print("边个数 ", graph["edge_index"].shape[1])
-    print("边索引 shape ", graph["edge_index"].shape)
-    print("边特征 shape ", graph["edge_feat"].shape)
-    print("节点特征是 ", graph["node_feat"])
-    print("species shape", graph['species'].shape)
-    print("label shape ", label.shape)
-    
-    # 读取/计算 node feature
-    # 确定读取文件的路径
+
+    
+    # read/compute node feature
     if mini_data:
         node_feat_path = './dataset/ogbn_proteins_node_feat_small.npy'
     else:
@@ -59,14 +59,11 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
 
     new_node_feat = None
     if os.path.exists(node_feat_path):
-        # 如果文件存在，直接读取
-        print("读取 node feature 开始".center(50, '='))
+        print("Begin: read node feature".center(50, '='))
         new_node_feat = np.load(node_feat_path)
-        print("读取 node feature 成功".center(50, '='))
+        print("End: read node feature".center(50, '='))
     else:
-        # 如果文件不存在，则计算
-        # 每个节点 i 的特征为其邻边特征的均值
-        print("计算 node feature 开始".center(50, '='))
+        print("Begin: compute node feature".center(50, '='))
         start = time.perf_counter()
         for i in range(graph['num_nodes']):
             if i % 100 == 0:
@@ -74,8 +71,8 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
                 print("{}/{}({}%), times: {:.2f}s".format(
                     i, graph['num_nodes'], i/graph['num_nodes']*100, dur
                 ))
-            mask = (graph['edge_index'][:, 0] == i) # 选择 i 的所有邻边
-            # 计算均值
+            mask = (graph['edge_index'][:, 0] == i)
+            
             current_node_feat = np.mean(np.compress(mask, graph['edge_feat'], axis=0),
                                         axis=0, keepdims=True)
             if i == 0:
@@ -84,23 +81,23 @@ def get_graph_data(d_name="ogbn-proteins", mini_data=False):
                 new_node_feat.append(current_node_feat)
 
         new_node_feat = np.concatenate(new_node_feat, axis=0)
-        print("计算 node feature 结束".center(50,'='))
+        print("End: compute node feature".center(50,'='))
 
-        print("存储 node feature 中，在"+node_feat_path.center(50, '='))
+        print("Saving node feature in "+node_feat_path.center(50, '='))
         np.save(node_feat_path, new_node_feat)
-        print("存储 node feature 结束".center(50,'='))
+        print("Saving finish".center(50,'='))
     
     print(new_node_feat)
     
     
-    # 构造 Graph 对象
+    # create graph
     g = pgl.graph.Graph(
         num_nodes=graph["num_nodes"],
         edges = graph["edge_index"],
         node_feat = {'node_feat': new_node_feat},
         edge_feat = None
     )
-    print("创建 Graph 对象成功")
+    print("Create graph")
     print(g)
     return g, label, train_idx, valid_idx, test_idx, Evaluator(d_name)
     
\ No newline at end of file

examples/GaAN/train.py

+# Copyright (c) 2019 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.
 from preprocess import get_graph_data
 import pgl
 import argparse
 import numpy as np
 import time
 from paddle import fluid
-from visualdl import LogWriter
 
 import reader
 from train_tool import train_epoch, valid_epoch
@@ -50,9 +62,7 @@ if __name__ == "__main__":
     
     args = parser.parse_args()
 
-#     d_name = "ogbn-proteins"
-
-    print("超参数配置".center(50, "="))
+    print("Parameters Setting".center(50, "="))
     print("lr = {}, rc = {}, epochs = {}, batch_size = {}".format(args.lr, args.rc, args.epochs,
                                                                   args.batch_size))
     print("Experiment ID: {}".format(args.exp_id).center(50, "="))
@@ -63,20 +73,6 @@ if __name__ == "__main__":
     g, label, train_idx, valid_idx, test_idx, evaluator = get_graph_data(d_name=d_name, 
                                                                          mini_data=eval(args.mini_data))
     
-    
-    # create log writer
-    log_writer = LogWriter(args.log_path+'/'+str(args.exp_id), sync_cycle=10)
-    with log_writer.mode("train") as logger:
-        log_train_loss_epoch = logger.scalar("loss")
-        log_train_rocauc_epoch = logger.scalar("rocauc")
-    with log_writer.mode("valid") as logger:
-        log_valid_loss_epoch = logger.scalar("loss")
-        log_valid_rocauc_epoch = logger.scalar("rocauc")
-    log_text = log_writer.text("text")
-    log_time = log_writer.scalar("time")
-    log_test_loss = log_writer.scalar("test_loss")
-    log_test_rocauc = log_writer.scalar("test_rocauc")
-
     if args.model == "GaAN":
         graph_model = GaANModel(112, 3, args.hidden_size_a, args.hidden_size_v, args.hidden_size_m,
                                 args.hidden_size_o, args.heads)
@@ -162,17 +158,13 @@ if __name__ == "__main__":
 
     start = time.time()
     print("Training Begin".center(50, "="))
-    log_text.add_record(0, "Training Begin".center(50, "="))
     best_valid = -1.0
     for epoch in range(args.epochs):
         start_e = time.time()
-#         print("Train Epoch {}".format(epoch).center(50, "="))
         train_loss, train_rocauc = train_epoch(
             train_iter, program=train_program, exe=exe, loss=loss, score=score, 
             evaluator=evaluator, epoch=epoch
         )
-
-        print("Valid Epoch {}".format(epoch).center(50, "="))
         valid_loss, valid_rocauc = valid_epoch(
             val_iter, program=val_program, exe=exe, loss=loss, score=score,
             evaluator=evaluator, epoch=epoch)
@@ -180,15 +172,6 @@ if __name__ == "__main__":
         print("Epoch {}: train_loss={:.4},val_loss={:.4}, train_rocauc={:.4}, val_rocauc={:.4}, s/epoch={:.3}".format(
             epoch, train_loss, valid_loss, train_rocauc, valid_rocauc, end_e-start_e
         ))
-        log_text.add_record(epoch+1,
-            "Epoch {}: train_loss={:.4},val_loss={:.4}, train_rocauc={:.4}, val_rocauc={:.4}, s/epoch={:.3}".format(
-            epoch, train_loss, valid_loss, train_rocauc, valid_rocauc, end_e-start_e
-        ))
-        log_train_loss_epoch.add_record(epoch, train_loss)
-        log_valid_loss_epoch.add_record(epoch, valid_loss)
-        log_train_rocauc_epoch.add_record(epoch, train_rocauc)
-        log_valid_rocauc_epoch.add_record(epoch, valid_rocauc)
-        log_time.add_record(epoch, end_e-start_e)
 
         if valid_rocauc > best_valid:
             print("Update: new {}, old {}".format(valid_rocauc, best_valid))
@@ -198,23 +181,14 @@ if __name__ == "__main__":
             
 
     print("Test Stage".center(50, "="))
-    log_text.add_record(args.epochs+1, "Test Stage".center(50, "="))
     
     fluid.io.load_params(executor=exe, dirname='./params/'+str(args.exp_id), main_program=val_program)
     
     test_loss, test_rocauc = valid_epoch(
         test_iter, program=val_program, exe=exe, loss=loss, score=score,
         evaluator=evaluator, epoch=epoch)
-    log_test_loss.add_record(0, test_loss)
-    log_test_rocauc.add_record(0, test_rocauc)
     end = time.time()
     print("test_loss={:.4},test_rocauc={:.4}, Total Time={:.3}".format(
             test_loss, test_rocauc, end-start
     ))
     print("End".center(50, "="))
-    log_text.add_record(args.epochs+2, "test_loss={:.4},test_rocauc={:.4}, Total Time={:.3}".format(
-            test_loss, test_rocauc, end-start
-    ))
-    log_text.add_record(args.epochs+3, "End".center(50, "="))
-    
-    
\ No newline at end of file

examples/GaAN/train_tool.py

+# Copyright (c) 2019 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.
 import time
 from pgl.utils.logger import log
 
@@ -15,50 +28,12 @@ def train_epoch(batch_iter, exe, program, loss, score, evaluator, epoch, log_per
         total_sample += num_samples
         input_dict = {
             "y_true": batch_feed_dict["node_label"],
-#             "y_pred": y_pred[batch_feed_dict["node_index"]]
             "y_pred": y_pred
         }
         result += evaluator.eval(input_dict)["rocauc"]
 
-#         if batch % log_per_step == 0:
-#             print("Batch {}: Loss={}".format(batch, batch_loss))
-#             log.info("Batch %s %s-Loss %s %s-Acc %s" %
-#                      (batch, prefix, batch_loss, prefix, batch_acc))
-        
-#     print("Epoch {} Train: Loss={}, rocauc={}, Speed(per batch)={}".format(
-#         epoch, total_loss/total_sample, result/batch, (end-start)/batch))
     return total_loss.item()/total_sample, result/batch
 
-def inference(batch_iter, exe, program, loss, score, evaluator, epoch, log_per_step=1):
-    batch = 0
-    total_sample = 0
-    total_loss = 0
-    result = 0
-    start = time.time()
-    for batch_feed_dict in batch_iter():
-        batch += 1
-        y_pred = exe.run(program, fetch_list=[score], feed=batch_feed_dict)[0]
-        input_dict = {
-            "y_true": batch_feed_dict["node_label"],
-            "y_pred": y_pred[batch_feed_dict["node_index"]]
-        }
-        result += evaluator.eval(input_dict)["rocauc"]
-
-
-        if batch % log_per_step == 0:
-            print(batch, result/batch)
-
-
-        num_samples = len(batch_feed_dict["node_index"])
-#         total_loss += batch_loss * num_samples
-#         total_acc += batch_acc * num_samples
-        total_sample += num_samples
-    end = time.time()
-    print("Epoch {} Valid: Loss={}, Speed(per batch)={}".format(epoch, total_loss/total_sample,
-                                                                (end-start)/batch))
-    return total_loss/total_sample, result/batch
-
-    
 def valid_epoch(batch_iter, exe, program, loss, score, evaluator, epoch, log_per_step=1):
     batch = 0
     total_sample = 0
@@ -69,53 +44,13 @@ def valid_epoch(batch_iter, exe, program, loss, score, evaluator, epoch, log_per
         batch_loss, y_pred = exe.run(program, fetch_list=[loss, score], feed=batch_feed_dict)
         input_dict = {
             "y_true": batch_feed_dict["node_label"],
-#             "y_pred": y_pred[batch_feed_dict["node_index"]]
             "y_pred": y_pred
         }
-#         print(evaluator.eval(input_dict))
         result += evaluator.eval(input_dict)["rocauc"]
 
 
-#         if batch % log_per_step == 0:
-#             print(batch, result/batch)
-
-
         num_samples = len(batch_feed_dict["node_index"])
         total_loss += batch_loss * num_samples
-#         total_acc += batch_acc * num_samples
         total_sample += num_samples
 
-#     print("Epoch {} Valid: Loss={}, Speed(per batch)={}".format(epoch, total_loss/total_sample, (end-start)/batch))
     return total_loss.item()/total_sample, result/batch
-
-    
-
-def run_epoch(batch_iter, exe, program, prefix, model_loss, model_acc, epoch, log_per_step=100):
-    """
-    已废弃
-    """
-    batch = 0
-    total_loss = 0.
-    total_acc = 0.
-    total_sample = 0
-    start = time.time()
-    for batch_feed_dict in batch_iter():
-        batch += 1
-        batch_loss, batch_acc = exe.run(program,
-                                        fetch_list=[model_loss, model_acc],
-                                        feed=batch_feed_dict)
-
-        if batch % log_per_step == 0:
-            log.info("Batch %s %s-Loss %s %s-Acc %s" %
-                     (batch, prefix, batch_loss, prefix, batch_acc))
-
-        num_samples = len(batch_feed_dict["node_index"])
-        total_loss += batch_loss * num_samples
-        total_acc += batch_acc * num_samples
-        total_sample += num_samples
-    end = time.time()
-
-    log.info("%s Epoch %s Loss %.5lf Acc %.5lf Speed(per batch) %.5lf sec" %
-             (prefix, epoch, total_loss / total_sample,
-              total_acc / total_sample, (end - start) / batch))
-