src.FRAME_FM.models.demo_autoencoder

EuroSAT Autoencoder (torchvision EuroSAT friendly)

This module defines a simple convolutional autoencoder intended for use with torchvision.datasets.EuroSAT and a dataloader that yields batches as:

batch = (x, y)

where:

• x is a float Tensor of shape [B, C, H, W]

• y is the class label Tensor [B] (not used for reconstruction loss)

Important: - Your transforms should convert PIL -> Tensor (e.g., ToTensor()). - For this architecture, it’s simplest to resize images to 64x64 so that

4x MaxPool(2) leads to a 4x4 spatial map.

Classes

EuroSATAutoencoder

Convolutional autoencoder:

Module Contents

class src.FRAME_FM.models.demo_autoencoder.EuroSATAutoencoder(in_channels: int = 3, base_ch: int = 32, k_size: int = 5, latent_dim: int = 256, lr: float = 0.001, **kwargs)

Bases: FRAME_FM.utils.LightningModuleWrapper.BaseModule

Convolutional autoencoder:

x -> encoder -> z -> decoder -> x_recon

Uses MSE reconstruction loss.

> Thappitla, R.S., Villuri, V.G.K. & Kumar, S. An autoencoder driven deep learning geospatial approach to flood vulnerability analysis

in the upper and middle basin of river Damodar. Sci Rep 15, 33741 (2025). https://doi.org/10.1038/s41598-025-96781-2

Hydra config (example):

_target_: FRAME_FM.model_code.demo_model.EuroSATAutoencoder in_channels: 3 base_ch: 32 k_size: 5 latent_dim: 256 lr: 1e-3

encoder

to_latent

from_latent

decoder

loss_fn

forward(x: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Parameters:

x – Tensor [B, C, H, W]

Returns:

Tensor [B, C, H, W] z: Tensor [B, latent_dim]

Return type:

x_recon

training_step_body(batch, batch_idx: int = 0)

Subclasses implement this instead of training_step. Should return (loss, logs_dict).

validation_step_body(batch, batch_idx: int = 0)

test_step_body(batch, batch_idx: int = 0)

configure_optimizers()

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.

Returns:

Any of these 6 options.

• Single optimizer.

• List or Tuple of optimizers.

• Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).

• Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.

• None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

# The ReduceLROnPlateau scheduler requires a monitor
def configure_optimizers(self):
    optimizer = Adam(...)
    return {
        "optimizer": optimizer,
        "lr_scheduler": {
            "scheduler": ReduceLROnPlateau(optimizer, ...),
            "monitor": "metric_to_track",
            "frequency": "indicates how often the metric is updated",
            # If "monitor" references validation metrics, then "frequency" should be set to a
            # multiple of "trainer.check_val_every_n_epoch".
        },
    }

# In the case of two optimizers, only one using the ReduceLROnPlateau scheduler
def configure_optimizers(self):
    optimizer1 = Adam(...)
    optimizer2 = SGD(...)
    scheduler1 = ReduceLROnPlateau(optimizer1, ...)
    scheduler2 = LambdaLR(optimizer2, ...)
    return (
        {
            "optimizer": optimizer1,
            "lr_scheduler": {
                "scheduler": scheduler1,
                "monitor": "metric_to_track",
            },
        },
        {"optimizer": optimizer2, "lr_scheduler": scheduler2},
    )

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note

Some things to know:

• Lightning calls .backward() and .step() automatically in case of automatic optimization.

• If a learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.

• If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.

• If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.

• If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.

• If you need to control how often the optimizer steps, override the optimizer_step() hook.