-
Notifications
You must be signed in to change notification settings - Fork 2
/
training_sleepedf.py
166 lines (135 loc) · 4.92 KB
/
training_sleepedf.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
"""Import libraries"""
import os
from datetime import datetime
from models.backbones.dfformer import calculate_output_size
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from pytorch_lightning import seed_everything, Trainer
from datasets.sleepedf import SleepEDF
import datasets.eeg_transforms as e_transforms
from models.litmodel import LitSleepLinear
from models.init import get_model
from utils.setup_utils import get_device
from utils.training_utils import get_configs
""" Config setting """
CONFIG_PATH = f"{os.getcwd()}/configs"
filename = "sleepedf_config.yaml"
args = get_configs(config_path=CONFIG_PATH, filename=filename, dataset="SleepEDF")
args.current_time = datetime.now().strftime("%Y%m%d")
# Set Device
if torch.cuda.is_available():
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU_NUM
args["device"] = get_device(args.GPU_NUM)
cudnn.benchmark = True
cudnn.fastest = True
cudnn.deteministic = True
args.lr = float(args.lr)
args.weight_decay = float(args.weight_decay)
# Set SEED
seed_everything(args.SEED)
def load_data(num_subject: int):
args.target_subject = num_subject
transform = transforms.Compose(
[e_transforms.ToTensor(), e_transforms.MinMaxNormalization()]
)
dataset = SleepEDF(args=args, is_test=False, transform=transform)
seed_everything(args.SEED)
train_data_size = int(len(dataset) * args.train_data_ratio / 100)
train_dataset, _ = random_split(
dataset, [train_data_size, len(dataset) - train_data_size]
)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
test_dataset = SleepEDF(args=args, is_test=True, transform=transform)
test_dataloader = DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False,
)
return train_dataloader, test_dataloader
def load_ckpt(model: nn.Module, path: str):
checkpoint = torch.load(path, map_location="cpu")
state_dict = checkpoint["state_dict"]
for k in list(state_dict.keys()):
if k.startswith("model.encoder."):
state_dict[k[len("model.encoder.") :]] = state_dict[k]
del state_dict[k]
for k in list(state_dict.keys()):
if k.startswith("classifier_head."):
del state_dict[k]
### Reshape pos embedding ###
if state_dict["embedding.temporal_pos_embed"].shape[1] < args.seq_len + 1:
seq_len = calculate_output_size(args.seq_len, args.cnn_layers)
state_dict["embedding.temporal_pos_embed"] = F.interpolate(
state_dict["embedding.temporal_pos_embed"][np.newaxis, ...],
size=(seq_len + 1, args.dim),
)[0]
else:
state_dict["embedding.temporal_pos_embed"] = state_dict[
"embedding.temporal_pos_embed"
][:, : args.seq_len + 1, :]
if (
state_dict["embedding.spatial_pos_embed"].shape[1]
< args.inter_information_length
):
state_dict["embedding.spatial_pos_embed"] = F.interpolate(
state_dict["embedding.spatial_pos_embed"][np.newaxis, ...],
size=(args.inter_information_length + 1, args.dim),
)[0]
else:
state_dict["embedding.spatial_pos_embed"] = state_dict[
"embedding.spatial_pos_embed"
][:, : args.inter_information_length + 1, :]
msg = model.load_state_dict(state_dict, strict=False)
print(f"LOG >>>\n{msg}")
return model
def main():
total_results = []
for num_subject in range(args.num_subjects):
train_dataloader, test_dataloader = load_data(num_subject=num_subject)
### Load Model ###
encoder = get_model(
args=args, load_ckpt=load_ckpt if args.WEIGHT_PATH is not None else None
)
model = LitSleepLinear(model=encoder, args=args)
### Training ###
devices = list(map(int, args.GPU_NUM.split(",")))
trainer = Trainer(
max_epochs=args.EPOCHS,
accelerator="gpu",
devices=devices,
default_root_dir=f"{args.CKPT_PATH}/sleepedf",
)
trainer.fit(
model,
train_dataloaders=train_dataloader,
val_dataloaders=test_dataloader,
)
# Evaluate in test data
result = trainer.test(model, dataloaders=test_dataloader)
acc = result[0]["eval_acc"]
total_results.append(acc)
total_results = list(map(lambda x: f"{x:.4f}", total_results))
total_results = ",".join(total_results)
print(f"Total results: {total_results}")
if __name__ == "__main__":
import traceback
try:
main()
except Exception as e:
print(e)
print(traceback.format_exc())