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PGL
提交 98049ba1 编写于 作者: W wangwenjin
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Showing with 73 addition and 164 deletion
examples/GaAN/conv.py
浏览文件 @ 98049ba1
......@@ -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
浏览文件 @ 98049ba1
# 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
浏览文件 @ 98049ba1
"""
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
浏览文件 @ 98049ba1
# 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
examples/GaAN/train_tool.py
浏览文件 @ 98049ba1
# 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))
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