training module

class edafm.training.ImgDataset(data_dir, preproc_fn=None, print_timings=False)

Bases: torch.utils.data.dataset.Dataset

Pytorch dataset for loading AFM data.

Parameters

• data_dir – str. Path to directory where data is saved.

• preproc_fn – Python function. Preprocessing function which is applied to every batch.

• print_timings – Bool. Whether to print timings for each loaded batch.

shuffle()

Shuffle batch order

class edafm.training.ImgLoss(loss_factors)

Bases: torch.nn.modules.module.Module

Weighted mean squared loss for images.

Parameters

loss_factors – list of int. Loss weights.

forward(pred, ref, separate_batch_items=False)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

edafm.training.make_dataloader(datadir, preproc_fn, print_timings=False, num_workers=8)

Produce a dataset and dataloader from data directory.

Parameters

• datadir – str. Path to directory with data.

• preproc_fn – Python function. Preprocessing function to apply to each batch.

• print_timings – Boolean. Whether to print timings for each batch.

• num_workers – int. Number of parallel processes for data loading.

Returns: tuple (dataset, dataloader)

dataset: ImgDataset.

dataloader: torch.DataLoader.