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
|
import os.path as osp
import ignite
import ignite.contrib.handlers.tensorboard_logger
import ignite.contrib.handlers.tqdm_logger
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric import seed_everything
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GIN, MLP, global_add_pool
class Model(torch.nn.Module):
def __init__(self, in_channels: int, out_channels: int,
hidden_channels: int = 64, num_layers: int = 3,
dropout: float = 0.5):
super().__init__()
self.gnn = GIN(in_channels, hidden_channels, num_layers,
dropout=dropout, jk='cat')
self.classifier = MLP([hidden_channels, hidden_channels, out_channels],
norm="batch_norm", dropout=dropout)
def forward(self, data):
x = self.gnn(data.x, data.edge_index)
x = global_add_pool(x, data.batch)
x = self.classifier(x)
return x
def main():
seed_everything(42)
root = osp.join('data', 'TUDataset')
dataset = TUDataset(root, 'IMDB-BINARY', pre_transform=T.OneHotDegree(135))
dataset = dataset.shuffle()
test_dataset = dataset[:len(dataset) // 10]
val_dataset = dataset[len(dataset) // 10:2 * len(dataset) // 10]
train_dataset = dataset[2 * len(dataset) // 10:]
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True,
pin_memory=True)
val_loader = DataLoader(val_dataset, batch_size=64, pin_memory=True)
test_loader = DataLoader(test_dataset, batch_size=64, pin_memory=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Model(dataset.num_node_features, dataset.num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
metrics = {'acc': ignite.metrics.Accuracy()}
def prepare_batch_fn(batch, device, non_blocking):
return (batch.to(device, non_blocking=non_blocking),
batch.y.to(device, non_blocking=non_blocking))
trainer = ignite.engine.create_supervised_trainer(
model=model,
optimizer=optimizer,
loss_fn=F.cross_entropy,
device=device,
prepare_batch=prepare_batch_fn,
output_transform=lambda x, y, y_pred, loss: loss.item(),
amp_mode='amp',
)
# Progress bar for each epoch:
pbar = ignite.contrib.handlers.tqdm_logger.ProgressBar()
pbar.attach(trainer, output_transform=lambda x: {'loss': x})
def log_metrics(evaluator, loader, tag):
def logger(trainer):
evaluator.run(loader)
print(f'{tag:10} Epoch: {trainer.state.epoch:02d}, '
f'Acc: {evaluator.state.metrics["acc"]:.4f}')
return logger
train_evaluator = ignite.engine.create_supervised_evaluator(
model=model,
metrics=metrics,
device=device,
prepare_batch=prepare_batch_fn,
output_transform=lambda x, y, y_pred: (y_pred, y),
amp_mode='amp',
)
trainer.on(ignite.engine.Events.EPOCH_COMPLETED(every=1))(log_metrics(
train_evaluator, train_loader, 'Training'))
val_evaluator = ignite.engine.create_supervised_evaluator(
model=model,
metrics=metrics,
device=device,
prepare_batch=prepare_batch_fn,
output_transform=lambda x, y, y_pred: (y_pred, y),
amp_mode='amp',
)
trainer.on(ignite.engine.Events.EPOCH_COMPLETED(every=1))(log_metrics(
val_evaluator, val_loader, 'Validation'))
test_evaluator = ignite.engine.create_supervised_evaluator(
model=model,
metrics=metrics,
device=device,
prepare_batch=prepare_batch_fn,
output_transform=lambda x, y, y_pred: (y_pred, y),
amp_mode='amp',
)
trainer.on(ignite.engine.Events.EPOCH_COMPLETED(every=1))(log_metrics(
test_evaluator, test_loader, 'Test'))
# Save checkpoint of the model based on Accuracy on the validation set:
checkpoint_handler = ignite.handlers.Checkpoint(
{'model': model},
'runs/gin',
n_saved=2,
score_name=list(metrics.keys())[0],
filename_pattern='best-{global_step}-{score_name}-{score}.pt',
global_step_transform=ignite.handlers.global_step_from_engine(trainer),
)
val_evaluator.add_event_handler(ignite.engine.Events.EPOCH_COMPLETED,
checkpoint_handler)
# Create a tensorboard logger to write logs:
tb_logger = ignite.contrib.handlers.tensorboard_logger.TensorboardLogger(
log_dir=osp.join('runs/example', 'tb_logs'))
tb_logger.attach_output_handler(
trainer, event_name=ignite.engine.Events.ITERATION_COMPLETED,
tag='training', output_transform=lambda loss: {'loss_iteration': loss})
tb_logger.attach_output_handler(
trainer, event_name=ignite.engine.Events.EPOCH_COMPLETED,
tag='training', output_transform=lambda loss: {'loss_epoch': loss})
tb_logger.attach_output_handler(
train_evaluator,
event_name=ignite.engine.Events.EPOCH_COMPLETED,
tag='training',
metric_names='all',
global_step_transform=ignite.handlers.global_step_from_engine(trainer),
)
tb_logger.attach_output_handler(
val_evaluator,
event_name=ignite.engine.Events.EPOCH_COMPLETED,
tag='validation',
metric_names='all',
global_step_transform=ignite.handlers.global_step_from_engine(trainer),
)
tb_logger.attach_output_handler(
test_evaluator,
event_name=ignite.engine.Events.EPOCH_COMPLETED,
tag='test',
metric_names='all',
global_step_transform=ignite.handlers.global_step_from_engine(trainer),
)
tb_logger.close()
trainer.run(train_loader, max_epochs=50)
if __name__ == '__main__':
main()
|
