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
|
"""Base PyTorch Lightning model."""
from typing import Any, Dict, List, Union, Tuple, Type
import attr
import hydra
import loguru.logger as log
from omegaconf import DictConfig
import pytorch_lightning as pl
import torch
from torch import nn
from torch import Tensor
import torchmetrics
@attr.s
class LitBaseModel(pl.LightningModule):
"""Abstract PyTorch Lightning class."""
network: Type[nn.Module] = attr.ib()
criterion_config: DictConfig = attr.ib(converter=DictConfig)
optimizer_config: DictConfig = attr.ib(converter=DictConfig)
lr_scheduler_config: DictConfig = attr.ib(converter=DictConfig)
interval: str = attr.ib()
monitor: str = attr.ib(default="val/loss")
loss_fn = attr.ib(init=False)
train_acc = attr.ib(init=False)
val_acc = attr.ib(init=False)
test_acc = attr.ib(init=False)
def __attrs_pre_init__(self):
super().__init__()
def __attrs_post_init__(self):
self.loss_fn = self.configure_criterion()
# Accuracy metric
self.train_acc = torchmetrics.Accuracy()
self.val_acc = torchmetrics.Accuracy()
self.test_acc = torchmetrics.Accuracy()
@staticmethod
def configure_criterion(self) -> Type[nn.Module]:
"""Returns a loss functions."""
log.info(f"Instantiating criterion <{self.criterion_config._target_}>")
return hydra.utils.instantiate(self.criterion_config)
def optimizer_zero_grad(
self,
epoch: int,
batch_idx: int,
optimizer: Type[torch.optim.Optimizer],
optimizer_idx: int,
) -> None:
optimizer.zero_grad(set_to_none=True)
def _configure_optimizer(self) -> Type[torch.optim.Optimizer]:
"""Configures the optimizer."""
log.info(f"Instantiating optimizer <{self.optimizer_config._target_}>")
return hydra.utils.instantiate(self.optimizer_config, params=self.parameters())
def _configure_lr_scheduler(
self, optimizer: Type[torch.optim.Optimizer]
) -> Dict[str, Any]:
"""Configures the lr scheduler."""
log.info(
f"Instantiating learning rate scheduler <{self.lr_scheduler_config._target_}>"
)
scheduler = {
"monitor": self.monitor,
"interval": self.interval,
"scheduler": hydra.utils.instantiate(
self.lr_scheduler_config, optimizer=optimizer
),
}
return scheduler
def configure_optimizers(self) -> Tuple[List[type], List[Dict[str, Any]]]:
"""Configures optimizer and lr scheduler."""
optimizer = self._configure_optimizer()
scheduler = self._configure_lr_scheduler(optimizer)
return [optimizer], [scheduler]
def forward(self, data: Tensor) -> Tensor:
"""Feedforward pass."""
return self.network(data)
def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
"""Training step."""
data, targets = batch
logits = self(data)
loss = self.loss_fn(logits, targets)
self.log("train/loss", loss)
self.train_acc(logits, targets)
self.log("train/acc", self.train_acc, on_step=False, on_epoch=True)
return loss
def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Validation step."""
data, targets = batch
logits = self(data)
loss = self.loss_fn(logits, targets)
self.log("val/loss", loss, prog_bar=True)
self.val_acc(logits, targets)
self.log("val/acc", self.val_acc, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
"""Test step."""
data, targets = batch
logits = self(data)
self.test_acc(logits, targets)
self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
|
