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from typing import Any, List
import torch
import torch.nn.functional as F
from torch import nn
from pytorch_lightning import LightningModule
from torchmetrics import MaxMetric, MeanAbsoluteError, MinMetric
from torchmetrics.classification.accuracy import Accuracy
import torchvision.models as models
class ResNetLitModule(LightningModule):
def __init__(
self,
resnet_type: str = "ResNet",
pretrained=False,
lr: float = 0.001,
weight_decay: float = 0.0005,
):
"""Initialize function for a resnet module.
Args:
resnet_type (str, optional): Type of the used resnet network. Defaults to
"ResNet".
Can be one of the following values: "ResNet", "resnet18",
"resnet34", "resnet50", "resnet101", "resnet152", "resnext50_32x4d",
"resnext101_32x8d", "wide_resnet50_2", "wide_resnet101_2"
pretrained (bool, optional): if True loads pytorch pretrained models.
Defaults to False.
"""
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# it also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
# loss function
self.criterion = torch.nn.MSELoss()
# use separate metric instance for train, val and test step
# to ensure a proper reduction over the epoch
self.train_mae = MeanAbsoluteError()
self.val_mae = MeanAbsoluteError()
self.test_mae = MeanAbsoluteError()
# for logging best so far validation accuracy
self.val_mae_best = MinMetric()
self.pretrained = pretrained
if resnet_type == "ResNet":
resnet_constructor = models.ResNet
elif resnet_type == "resnet18":
resnet_constructor = models.resnet18
elif resnet_type == "resnet34":
resnet_constructor = models.resnet34
elif resnet_type == "resnet50":
resnet_constructor = models.resnet50
elif resnet_type == "resnet101":
resnet_constructor = models.resnet101
elif resnet_type == "resnet152":
resnet_constructor = models.resnet152
elif resnet_type == "resnext50_32x4d":
resnet_constructor = models.resnext50_32x4d
elif resnet_type == "resnext101_32x8d":
resnet_constructor = models.resnext101_32x8d
elif resnet_type == "wide_resnet50_2":
resnet_constructor = models.wide_resnet50_2
elif resnet_type == "wide_resnet101_2":
resnet_constructor = models.wide_resnet101_2
else:
raise Exception(f"did not find model type: {resnet_type}")
backbone = resnet_constructor(pretrained=pretrained)
# init a pretrained resnet
num_filters = backbone.fc.in_features
layers = list(backbone.children())[:-1]
self.feature_extractor = nn.Sequential(*layers)
self.fc = nn.Linear(num_filters, 1)
def forward(self, x):
representations = self.feature_extractor(x).flatten(1)
x = self.fc(representations)
return x
def step(self, batch: Any):
x = batch["image"]
y = batch["focus_height"]
logits = self.forward(x)
loss = self.criterion(logits, y.unsqueeze(1))
preds = torch.squeeze(logits)
return loss, preds, y
def training_step(self, batch: Any, batch_idx: int):
loss, preds, targets = self.step(batch)
# log train metrics
mae = self.train_mae(preds, targets)
self.log("train/loss", loss, on_step=False, on_epoch=True, prog_bar=False)
self.log("train/mae", mae, on_step=False, on_epoch=True, prog_bar=True)
# we can return here dict with any tensors
# and then read it in some callback or in `training_epoch_end()`` below
# remember to always return loss from `training_step()` or else
# backpropagation will fail!
return {"loss": loss, "preds": preds, "targets": targets}
def training_epoch_end(self, outputs: List[Any]):
# `outputs` is a list of dicts returned from `training_step()`
pass
def validation_step(self, batch: Any, batch_idx: int):
loss, preds, targets = self.step(batch)
# log val metrics
mae = self.val_mae(preds, targets)
self.log("val/loss", loss, on_step=False, on_epoch=True, prog_bar=False)
self.log("val/mae", mae, on_step=False, on_epoch=True, prog_bar=True)
return {"loss": loss, "preds": preds, "targets": targets}
def validation_epoch_end(self, outputs: List[Any]):
mae = self.val_mae.compute() # get val accuracy from current epoch
self.val_mae_best.update(mae)
self.log(
"val/mae_best", self.val_mae_best.compute(), on_epoch=True, prog_bar=True
)
def test_step(self, batch: Any, batch_idx: int):
loss, preds, targets = self.step(batch)
# log test metrics
mae = self.test_mae(preds, targets)
self.log("test/loss", loss, on_step=False, on_epoch=True)
self.log("test/mae", mae, on_step=False, on_epoch=True)
def test_epoch_end(self, outputs: List[Any]):
print(outputs)
pass
def on_epoch_end(self):
# reset metrics at the end of every epoch
self.train_mae.reset()
self.test_mae.reset()
self.val_mae.reset()
def configure_optimizers(self):
"""Choose what optimizers and learning-rate schedulers.
Normally you'd need one. But in the case of GANs or similar you might
have multiple.
See examples here:
https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#configure-optimizers
"""
return torch.optim.Adam(
params=self.parameters(),
lr=self.hparams.lr,
weight_decay=self.hparams.weight_decay,
)
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