diff --git a/examples/GaAN/conv.py b/examples/GaAN/conv.py index 68a1d733ed1d297e7a20daa1fb7c14828ff8722b..a9264d3c8cd475791d2e23097aaa4a94e7665d5a 100644 --- a/examples/GaAN/conv.py +++ b/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')) diff --git a/examples/GaAN/model.py b/examples/GaAN/model.py index 9ad02abee102bc9d6951683a334c6eb0720396e3..37b9f9bcd5dd4adf5e826ea9d47260d3208e22c3 100644 --- a/examples/GaAN/model.py +++ b/examples/GaAN/model.py @@ -1,3 +1,16 @@ +# 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 diff --git a/examples/GaAN/preprocess.py b/examples/GaAN/preprocess.py index a9bfffc8a8d2c7ef2ff4d14e268ce9d4814b0d2f..4357ee47cac919832530b94facb66a76e1470e49 100644 --- a/examples/GaAN/preprocess.py +++ b/examples/GaAN/preprocess.py @@ -1,6 +1,16 @@ -""" -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 diff --git a/examples/GaAN/train.py b/examples/GaAN/train.py index d4aebeca8837a747093beb02779be9000be78da0..6a8f905e158b21222bef67cd0dc4c6232113c42c 100644 --- a/examples/GaAN/train.py +++ b/examples/GaAN/train.py @@ -1,10 +1,22 @@ +# 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 @@ -49,10 +61,8 @@ if __name__ == "__main__": help="the hidden size of each layer in GaAN") 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) @@ -180,16 +172,7 @@ 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)) best_valid = valid_rocauc @@ -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 diff --git a/examples/GaAN/train_tool.py b/examples/GaAN/train_tool.py index 699d72ed8edc3a3fdd325a06717d30352f976a37..bdb1c928e6801f8b72cb076e7e4b353525d92303 100644 --- a/examples/GaAN/train_tool.py +++ b/examples/GaAN/train_tool.py @@ -1,3 +1,16 @@ +# 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)) -