src/models/focus_conv_module.py

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


class SimpleConvNet(nn.Module):
 def __init__(self, hparams):
 super().__init__()

 pool_size = hparams["pool_size"] # 2
 conv1_size = hparams["conv1_size"] # 5
 conv1_out = hparams["conv1_channels"] # 6
 conv2_size = hparams["conv1_channels"] # 5
 conv2_out = hparams["conv2_channels"] # 16
 size_img = hparams["image_size"] # 150

 lin1_size = hparams["lin1_size"] # 100
 lin2_size = hparams["lin2_size"] # 80
 output_size = hparams["output_size"] # 1

 size_img -= conv1_size - 1
 size_img = int((size_img) / pool_size)
 size_img -= conv2_size - 1
 size_img = int(size_img / pool_size)

 self.model = nn.Sequential(
 nn.Conv2d(3, conv1_out, conv1_size),
 nn.MaxPool2d(pool_size, pool_size),
 nn.Conv2d(conv1_out, conv2_out, conv2_size),
 nn.MaxPool2d(pool_size, pool_size),
 nn.Flatten(),
 nn.Linear(conv2_out * size_img * size_img, lin1_size),
 nn.Linear(lin1_size, lin2_size),
 nn.Linear(lin2_size, output_size),
 )

 def forward(self, x):
 x = self.model(x)
 return x


class FocusConvLitModule(LightningModule):
 """
 Example of LightningModule for MNIST classification.

 A LightningModule organizes your PyTorch code into 5 sections:
 - Computations (init).
 - Train loop (training_step)
 - Validation loop (validation_step)
 - Test loop (test_step)
 - Optimizers (configure_optimizers)

 Read the docs:
 https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html
 """

 def __init__(
 self,
 image_size: int = 150,
 pool_size: int = 2,
 conv1_size: int = 5,
 conv1_channels: int = 6,
 conv2_size: int = 5,
 conv2_channels: int = 16,
 lin1_size: int = 100,
 lin2_size: int = 80,
 output_size: int = 1,
 lr: float = 0.001,
 weight_decay: float = 0.0005,
 ):
 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)

 self.model = SimpleConvNet(hparams=self.hparams)

 # 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()

 def forward(self, x: torch.Tensor):
 return self.model(x)

 def step(self, batch: Any):
 x = batch["image"]
 y = batch["focus_value"]
 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)

 return {"loss": loss, "preds": preds, "targets": targets}

 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,
 )