-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathsetupGC.py
153 lines (128 loc) · 7.25 KB
/
setupGC.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
import random
from random import choices
import numpy as np
import pandas as pd
import torch.nn as nn
import torch
import copy
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
from torch_geometric.transforms import OneHotDegree
from models import SSP, Split_model
from server import Server
from client import Client_GC
from utils import get_stats, split_data, get_numGraphLabels, init_structure_encoding
def _randChunk(graphs, num_client, overlap, seed=None):
random.seed(seed)
np.random.seed(seed)
totalNum = len(graphs)
minSize = min(50, int(totalNum/num_client))
graphs_chunks = []
if not overlap:
for i in range(num_client):
graphs_chunks.append(graphs[i*minSize:(i+1)*minSize])
for g in graphs[num_client*minSize:]:
idx_chunk = np.random.randint(low=0, high=num_client, size=1)[0]
graphs_chunks[idx_chunk].append(g)
else:
sizes = np.random.randint(low=50, high=150, size=num_client)
for s in sizes:
graphs_chunks.append(choices(graphs, k=s))
return graphs_chunks
def prepareData_multiDS(args, datapath, group='chem', batchSize=128, seed=None):
assert group in ['chem', "biochem", 'biochemsn', "biosncv", "chemsn", "chemsncv", "chemcv"]
if group == 'chem':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1"]
elif group == 'biochem':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1", "Peking_1", "OHSU", "PROTEINS"]
elif group == 'biochemsn':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1", "Peking_1", "OHSU", "PROTEINS", "IMDB-MULTI", "IMDB-BINARY"]
elif group == 'biosncv':
datasets = ["Peking_1", "OHSU", "PROTEINS", "IMDB-MULTI", "IMDB-BINARY", "Letter-high", "Letter-med", "Letter-low"]
elif group == 'chemsn':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1", "IMDB-MULTI", "IMDB-BINARY"]
elif group == 'chemsncv':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1", "IMDB-MULTI", "IMDB-BINARY", "Letter-high", "Letter-med", "Letter-low"]
elif group == 'chemcv':
datasets = ["MUTAG", "BZR", "COX2", "DHFR", "PTC_MR", "AIDS", "NCI1", "Letter-high", "Letter-med", "Letter-low"]
splitedData = {}
df = pd.DataFrame()
for data in datasets:
if data == "IMDB-BINARY":
tudataset = TUDataset(f"{datapath}/TUDataset", data, pre_transform=OneHotDegree(135, cat=False))
elif data == "IMDB-MULTI":
tudataset = TUDataset(f"{datapath}/TUDataset", data, pre_transform=OneHotDegree(88, cat=False))
elif "Letter" in data:
tudataset = TUDataset(f"{datapath}/TUDataset", data, use_node_attr=True)
else:
tudataset = TUDataset(f"{datapath}/TUDataset", data)
graphs = [x for x in tudataset]
print(" **", data, len(graphs))
graphs_train, graphs_valtest = split_data(graphs, test=0.2, shuffle=True, seed=seed)
graphs_val, graphs_test = split_data(graphs_valtest, train=0.5, test=0.5, shuffle=True, seed=seed)
graphs_train = init_structure_encoding(args, gs=graphs_train, type_init=args.type_init)
graphs_val = init_structure_encoding(args, gs=graphs_val, type_init=args.type_init)
graphs_test = init_structure_encoding(args, gs=graphs_test, type_init=args.type_init)
dataloader_train = DataLoader(graphs_train, batch_size=batchSize, shuffle=True)
dataloader_val = DataLoader(graphs_val, batch_size=batchSize, shuffle=True)
dataloader_test = DataLoader(graphs_test, batch_size=batchSize, shuffle=True)
num_node_features = graphs[0].num_node_features
num_graph_labels = get_numGraphLabels(graphs_train)
splitedData[data] = ({'train': dataloader_train, 'val': dataloader_val, 'test': dataloader_test},
num_node_features, num_graph_labels, len(graphs_train))
df = get_stats(df, data, graphs_train, graphs_val=graphs_val, graphs_test=graphs_test)
return splitedData, df
splitedData = {}
df = pd.DataFrame()
for data in datasets:
if data == "IMDB-BINARY":
tudataset = TUDataset(f"{datapath}/TUDataset", data, pre_transform=OneHotDegree(135, cat=False))
elif data == "IMDB-MULTI":
tudataset = TUDataset(f"{datapath}/TUDataset", data, pre_transform=OneHotDegree(88, cat=False))
elif "Letter" in data:
tudataset = TUDataset(f"{datapath}/TUDataset", data, use_node_attr=True)
else:
tudataset = TUDataset(f"{datapath}/TUDataset", data)
graphs = [x for x in tudataset]
print(" **", data, len(graphs))
num_node_features = graphs[0].num_node_features
graphs_chunks = _randChunk(graphs, nc_per_ds, overlap=False, seed=seed)
for idx, chunks in enumerate(graphs_chunks):
ds = f'{idx}-{data}'
ds_tvt = chunks
graphs_train, graphs_valtest = split_data(ds_tvt, train=0.8, test=0.2, shuffle=True, seed=seed)
graphs_val, graphs_test = split_data(graphs_valtest, train=0.5, test=0.5, shuffle=True, seed=seed)
graphs_train = init_structure_encoding(args, gs=graphs_train, type_init=args.type_init)
graphs_val = init_structure_encoding(args, gs=graphs_val, type_init=args.type_init)
graphs_test = init_structure_encoding(args, gs=graphs_test, type_init=args.type_init)
dataloader_train = DataLoader(graphs_train, batch_size=batchSize, shuffle=True)
dataloader_val = DataLoader(graphs_val, batch_size=batchSize, shuffle=True)
dataloader_test = DataLoader(graphs_test, batch_size=batchSize, shuffle=True)
num_graph_labels = get_numGraphLabels(graphs_train)
splitedData[ds] = ({'train': dataloader_train, 'val': dataloader_val, 'test': dataloader_test},
num_node_features, num_graph_labels, len(graphs_train))
df = get_stats(df, ds, graphs_train, graphs_val=graphs_val, graphs_test=graphs_test)
return splitedData, df
def setup_devices_SSP(splitedData, args):
idx_clients = {}
clients = []
for idx, ds in enumerate(splitedData.keys()):
idx_clients[idx] = ds
dataloaders, num_node_features, num_graph_labels, train_size = splitedData[ds]
first_batch = next(iter(dataloaders['train']))
if first_batch.x is not None:
node_feature_dim = first_batch.x.size(1)
node_feature_dim = [node_feature_dim]
edge_feature_dim = 0
if args.alg == "fedSSP":
former= SSP(num_graph_labels, args.nlayer, node_feature_dim, edge_feature_dim, node_feature_dim[0], args.head, args.hidden)
head = former.fc
former.fc = nn.Identity()
basicModel = Split_model(former, head)
cmodel_gc = copy.deepcopy(basicModel)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, cmodel_gc.parameters()), lr=args.lr,
weight_decay=args.weight_decay)
clients.append(Client_GC(copy.deepcopy(cmodel_gc), idx, ds, train_size, dataloaders, optimizer, args))
smodel = copy.deepcopy(cmodel_gc)
server = Server(smodel, args.device)
return clients, server, idx_clients