-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_graph_inr.py
178 lines (136 loc) · 6.94 KB
/
train_graph_inr.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
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
import os
import numpy as np
import torch
import torch.nn.functional as f
import pytorch_lightning as pl
from argparse import ArgumentParser
from pytorch_lightning.callbacks import (EarlyStopping, LearningRateMonitor, ModelCheckpoint)
from pytorch_lightning.loggers import WandbLogger
from torch.utils.data import DataLoader
from src.data.graph_dataset import GraphDataset, split_graphdataset
from src.models.graph_inr import GraphINR
from src.models.spatial_graph_inr import SpatialGraphINR
from src.plotting.figures import draw_pc
from src.utils.get_predictions import get_batched_predictions
from src.utils.load_emb_file import load_emb_info
if torch.cuda.is_available():
accelerator = 'gpu'
torch.set_float32_matmul_precision('high')
else:
accelerator = 'cpu'
pl.seed_everything(1234)
parser = ArgumentParser()
parser.add_argument('--mode', default = 'fit', type = str)
parser.add_argument('--patience', default = 5000, type = int)
parser.add_argument('--batch_size', default = 32, type = int)
parser.add_argument('--n_workers', default = 0, type = int)
parser.add_argument('--plot_3d', action = 'store_true')
parser.add_argument('--plot_heat', action = 'store_true')
parser = pl.Trainer.add_argparse_args(parser)
parser = GraphDataset.add_dataset_specific_args(parser)
parser = GraphINR.add_graph_inr_model_specific_args(parser)
parser = SpatialGraphINR.add_spatial_graph_inr_model_specific_args(parser)
args = parser.parse_args()
data_key = None
if 'us_elections' in args.dataset_dir:
data_key = 'us_elections'
elif 'bunny' in args.dataset_dir:
data_key = 'bunny'
if not args.mode in ['fit', 'pred']:
raise ValueError('Mode should be one of the followings: fit, pred')
if not args.model in ['INR', 'GINR', 'Spatial_Graph_INR']:
raise ValueError('Model should be one of the followings: INR, GINR, Spatial_Graph_INR')
if data_key == 'us_elections' and args.model == 'INR':
raise ValueError('US-Election dataset does not have Cartesian coordinates to be used for INR')
# Load Data
dataset = GraphDataset(**vars(args))
if args.mode == 'pred':
train_ratio = 0.8
validation_ratio = 0.1
test_ratio = 0.1
train_dataset, validation_dataset, test_dataset = split_graphdataset(dataset, [train_ratio, validation_ratio, test_ratio])
train = DataLoader(train_dataset, batch_size = args.batch_size, shuffle = True, num_workers = args.n_workers)
validation = DataLoader(validation_dataset, batch_size = args.batch_size, num_workers = args.n_workers)
# test = DataLoader(test_dataset, batch_size = args.batch_size, num_workers = args.n_workers)
else:
loader = DataLoader(dataset, batch_size = args.batch_size, shuffle = True, num_workers = args.n_workers)
if args.model == 'INR':
input_dim = dataset.get_inputs(0).size(-1) + (1 if dataset.time else 0)
output_dim = dataset.target_dim
# Create a INR model based on the graph data loaded
model = GraphINR(input_dim, output_dim, len(dataset), **vars(args))
elif args.model == 'GINR':
input_dim = dataset.n_fourier + (1 if dataset.time else 0)
output_dim = dataset.target_dim
# Create a GINR model based on the graph data loaded
model = GraphINR(input_dim, output_dim, len(dataset), **vars(args))
elif args.model == 'Spatial_Graph_INR':
hyp_dim, hyp_copy, sph_dim, sph_copy, euc_dim, euc_copy = load_emb_info(args.emb_dir, args.emb_name)
hyp_out_dim = 128
sph_out_dim = 128
euc_out_dim = 128
output_dim = dataset.target_dim
# Create a spatial graph INR model based on the graph embedding information loaded
model = SpatialGraphINR(len(dataset), dataset.time,
((hyp_dim + 1) * hyp_copy), 512, hyp_out_dim,
((sph_dim + 1) * sph_copy), 512, sph_out_dim,
(euc_dim * euc_copy), 512, euc_out_dim,
4, 4, 4, output_dim, **vars(args))
# Training
checkpoint_cb = ModelCheckpoint(monitor = 'validation_loss' if args.mode == 'pred' else 'train_loss', mode = 'min', save_last = True, filename = 'best')
earlystopping_cb = EarlyStopping(monitor = 'validation_loss' if args.mode == 'pred' else 'train_loss', patience = args.patience)
lrmonitor_cb = LearningRateMonitor(logging_interval = 'step')
logger = WandbLogger(project = args.model, save_dir = 'lightning_logs')
logger.experiment.log({'CUDA_VISIBLE_DEVICES': os.environ.get('CUDA_VISIBLE_DEVICES', None)})
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs = -1 if args.max_epochs is None else args.max_epochs,
log_every_n_steps = 1,
callbacks = [checkpoint_cb, earlystopping_cb, lrmonitor_cb],
logger = logger,
accelerator = accelerator,
devices = [0]
# ddp strategy is decided not to be used
# devices = torch.cuda.device_count(),
# strategy = 'ddp' if torch.cuda.device_count() > 1 else None
)
if args.mode == 'pred':
trainer.fit(model, train, validation)
else:
trainer.fit(model, loader)
model = model.load_from_checkpoint(checkpoint_cb.best_model_path)
try:
points = dataset.ginr_npzs[0]['points']
except KeyError:
points = np.load(os.path.join(dataset.dataset_dir, 'points.npy'))
if data_key == 'bunny':
if args.mode == 'pred':
inputs = test_dataset.get_data(0)['inputs']
target = test_dataset.get_data(0)['target']
_, pred = get_batched_predictions(model, inputs, 0)
print(f'MSE Loss: {f.mse_loss(torch.Tensor(pred), target).item():.6f}')
print(f'R2 Score: {model.r2_score(torch.Tensor(pred).view(-1, model.inr_out_dim), target.view(-1, model.inr_out_dim)):.6f}')
else:
inputs = dataset.get_data(0)['inputs']
target = dataset.get_data(0)['target']
_, pred = get_batched_predictions(model, inputs, 0)
print(f'MSE Loss: {f.mse_loss(torch.Tensor(pred), target).item():.6f}')
print(f'R2 Score: {model.r2_score(torch.Tensor(pred).view(-1, model.inr_out_dim), target.view(-1, model.inr_out_dim)):.6f}')
inputs = dataset.get_inputs(0)
_, pred = get_batched_predictions(model, inputs, 0)
fig = draw_pc(points, pred[:, 0], colorscale = 'Reds')
fig.show()
logger.experiment.log({'Scatter': fig})
else:
if args.mode == 'pred':
inputs = test_dataset.get_data(0)['inputs']
target = test_dataset.get_data(0)['target']
_, pred = get_batched_predictions(model, inputs, 0)
print(f'MSE Loss: {f.mse_loss(torch.Tensor(pred), target).item():.6f}')
print(f'R2 Score: {model.r2_score(torch.Tensor(pred).view(-1, model.inr_out_dim), target.view(-1, model.inr_out_dim)):.6f}')
else:
inputs = dataset.get_data(0)['inputs']
target = dataset.get_data(0)['target']
_, pred = get_batched_predictions(model, inputs, 0)
print(f'MSE Loss: {f.mse_loss(torch.Tensor(pred), target).item():.6f}')
print(f'R2 Score: {model.r2_score(torch.Tensor(pred).view(-1, model.inr_out_dim), target.view(-1, model.inr_out_dim)):.6f}')