-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
141 lines (99 loc) · 4.08 KB
/
main.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
import pytorch_lightning.profiler
import torch
import timm
import numpy as np
import PIL.Image as Image
import torch.nn.functional as F
from tqdm import tqdm
from torch.utils.data import DataLoader, Dataset
from src.util.sam import SAM
from pytorch_lightning import LightningModule, Trainer
import torchmetrics
bs = 24
torch.backends.cudnn.benchmark=True
class GravWaveDataset(Dataset):
def __init__(self, data, data_dir):
self.data = data
self.data_dir = data_dir
#self.images = []
#for id_, target in tqdm(data):
# self.images.append(self.open(target, id_))
#self.images = [np.array(Image.open("{}/{}/{}.jpg".format(data_dir, target, id_))).transpose((2, 0, 1))
# for id_, target in data]
def __len__(self):
return len(self.data)
def open(self, target, id_):
return np.array(Image.open("{}/{}/{}.jpg".format(self.data_dir, target, id_))).transpose((2, 0, 1)).astype(np.float16) / 255
def __getitem__(self, index):
img = self.open(id_=self.data[index][0], target=self.data[index][1])
#img = self.images[index]
img = torch.tensor(img)
# print(img.shape)
return img, torch.tensor(self.data[index][1], dtype=torch.int64)
data_dir = "./data"
train_data = np.load("./data/train_info.npy", allow_pickle=True)
val_data = np.load("./data/validation_info.npy", allow_pickle=True)
train_ds = GravWaveDataset(train_data, "./data/train")
val_ds = GravWaveDataset(val_data, "./data/validation")
train_dl = DataLoader(train_ds, shuffle=True, batch_size=bs, num_workers=0)
val_dl = DataLoader(val_ds, batch_size=bs)
class GravModel(LightningModule):
def __init__(self, useSAM=True):
super().__init__()
self.num_classes = 2
self.model = timm.create_model('tf_efficientnetv2_m_in21k', pretrained=True, num_classes=self.num_classes)
self.metric = torchmetrics.Accuracy(num_classes=self.num_classes)
self.useSAM = useSAM
if self.useSAM:
self.automatic_optimization = False
def configure_optimizers(self):
lr = 0.001
if self.useSAM:
base_optimizer = torch.optim.Adam
optimizer = SAM(self.parameters(), base_optimizer, lr=lr)
else:
optimizer = torch.optim.Adam(self.parameters(), lr=lr)
# optimizer = torch.optim.Adam(self.model.parameters(), lr=0.1)
return optimizer
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
if self.useSAM:
optimizer = self.optimizers()
optimizer.zero_grad()
# first forward-backward pass
loss_1 = self.compute_loss(x, y)
self.manual_backward(loss_1)
optimizer.first_step(zero_grad=True)
# second forward-backward pass
loss_2 = self.compute_loss(x, y)
self.manual_backward(loss_2)
optimizer.second_step(zero_grad=True)
loss = loss_1
else:
loss = self.compute_loss(x, y)
#print(loss)
self.log('train_loss', loss, on_step=True)
return loss
def validation_step(self, batch, batch_idx):
# print(batch)
x, y = batch
with torch.no_grad():
logits = torch.sigmoid(self.forward(x))
score = self.metric(logits, y)
# print(score)
self.log("val_score_step", score, on_step=True)
return score
def validation_epoch_end(self, outs):
# outs is a list of whatever you returned in `validation_step`
loss = torch.stack(outs).mean()
self.log("val_score", loss, on_epoch=True, logger=True, prog_bar=True)
return loss
def compute_loss(self, x, y):
logits = self.forward(x)
return F.cross_entropy(logits, y)
module = GravModel(useSAM=False)
trainer = Trainer(gpus=1, precision=16, num_sanity_val_steps=1, limit_train_batches=0.1, check_val_every_n_epoch=1,
profiler=pytorch_lightning.profiler.SimpleProfiler())
trainer.fit(module, train_dl, val_dl)