add FNN with MSE loss

src/datamodules/focus_datamodule.py

@@ -3,6 +3,8 @@ from typing import Optional, Tuple
 import pandas as pd
 from skimage import io
 
+import numpy as np
+
 import torch
 from pytorch_lightning import LightningDataModule
 from torch.utils.data import DataLoader, Dataset, random_split
@@ -51,7 +53,9 @@ class FocusDataSet(Dataset):
  self.root_dir, self.metadata.iloc[idx, self.col_index_path]
  )
  image = io.imread(img_name)
- focus_value = self.metadata.iloc[idx, self.col_index_focus]
+ focus_value = torch.from_numpy(
+ np.asarray(self.metadata.iloc[idx, self.col_index_focus])
+ ).float()
  sample = {"image": image, "focus_value": focus_value}
 
  if self.transform:

src/models/focus_module.py

@@ -156,3 +156,128 @@ class FocusLitModule(LightningModule):
  lr=self.hparams.lr,
  weight_decay=self.hparams.weight_decay,
  )
+
+
+class FocusMSELitModule(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,
+ input_size: int = 75 * 75 * 3,
+ lin1_size: int = 256,
+ lin2_size: int = 256,
+ lin3_size: int = 256,
+ 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 = SimpleDenseNet(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)
+ 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,
+ )